



'''simple docstring'''




import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase__	:     Tuple    =			logging.getLogger()
UpperCamelCase__	:     Tuple    =			torch.cuda.is_available()
UpperCamelCase__	:     List[str]    =			{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1E-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}


def     lowerCAmelCase_       (     _lowerCamelCase:       Path	,				_lowerCamelCase:       list ):
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""\n""".join(_lowerCamelCase )
		Path(_lowerCamelCase ).open("""w""" ).writelines(_lowerCamelCase )


UpperCamelCase__	:     Any    =			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCamelCase__	:     Tuple    =			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCamelCase__	:     Tuple    =			'''patrickvonplaten/t5-tiny-random'''
UpperCamelCase__	:     Optional[int]    =			'''sshleifer/t5-tinier-random'''
UpperCamelCase__	:     Union[str, Any]    =			'''sshleifer/bart-tiny-random'''
UpperCamelCase__	:     int    =			'''sshleifer/tiny-mbart'''
UpperCamelCase__	:     Optional[Any]    =			'''sshleifer/tiny-marian-en-de'''
UpperCamelCase__	:     Any    =			'''stas/tiny-wmt19-en-de'''


UpperCamelCase__	:     str    =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def     lowerCAmelCase_       (     _lowerCamelCase:       List[str] ):
		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(_lowerCamelCase	,				F"{split}.source" )	,				_lowerCamelCase )
				_dump_articles(os.path.join(_lowerCamelCase	,				F"{split}.target" )	,				_lowerCamelCase )
		return tmp_dir






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			@classmethod
			def UpperCamelCase__ (							cls					:			Union[str, Any]						):





					"""simple docstring"""






					logging.disable(logging.CRITICAL						)  # remove noisy download output from tracebacks
					return cls

			@slow
			@require_torch_gpu
			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		list_models()
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		"""sshleifer"""
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[x.modelId for x in model_list if x.modelId.startswith(lowerCAmelCase__						)]
					__SCREAMING_SNAKE_CASE	:		int   	=		["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
					__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
					for m in model_ids:
							if m in allowed_to_be_broken:
									continue
							try:
									AutoConfig.from_pretrained(lowerCAmelCase__						)
							except Exception:
									failures.append(lowerCAmelCase__						)
					assert not failures, F"The following models could not be loaded through AutoConfig: {failures}"

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
					self._test_distiller_cli(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		{
					    """student_encoder_layers""": 2,
					    """student_decoder_layers""": 1,
					    """max_epochs""": 4,
					    """val_check_interval""": 0.25,
					    """alpha_hid""": 2.0,
					    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
					}
					__SCREAMING_SNAKE_CASE	:		int   	=		self._test_distiller_cli(lowerCAmelCase__							,					check_contents=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		list(Path(model.output_dir						).glob("""*.ckpt"""						)						)
					self.assertEqual(1							,					len(lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		list(Path(model.output_dir						).glob("""**/*.bin"""						)						)
					self.assertEqual(len(lowerCAmelCase__						)							,					2						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		lmap(str.strip							,					Path(model.hparams.data_dir						).joinpath("""test.source"""						).open().readlines()						)
					__SCREAMING_SNAKE_CASE	:		int   	=		tempfile.mktemp()  # XXX: not being cleaned up
					generate_summaries_or_translations(lowerCAmelCase__							,					lowerCAmelCase__							,					str(model.output_dir / """best_tfmr"""						)						)
					self.assertTrue(Path(lowerCAmelCase__						).exists()						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		self.get_auto_remove_tmp_dir()
					convert_pl_to_hf(ckpts[0]							,					transformer_ckpts[0].parent							,					lowerCAmelCase__						)
					assert os.path.exists(os.path.join(lowerCAmelCase__							,					"""pytorch_model.bin"""						)						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
					__SCREAMING_SNAKE_CASE	:		Any   	=		torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]							,					dtype=torch.long							,					device=model.device						)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		target_ids[:, :-1].contiguous()  # Why this line?
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		target_ids[:, 1:].clone()  # why clone?
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model(
					    lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					decoder_input_ids=lowerCAmelCase__							,					labels=lowerCAmelCase__							,					use_cache=lowerCAmelCase__						).loss

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					decoder_input_ids=lowerCAmelCase__							,					use_cache=lowerCAmelCase__						).logits

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		nn.functional.log_softmax(lowerCAmelCase__							,					dim=-1						)
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		label_smoothed_nll_loss(
					    lowerCAmelCase__							,					lowerCAmelCase__							,					0.1							,					ignore_index=model.config.pad_token_id						)
					with self.assertRaises(lowerCAmelCase__						):
							# TODO: understand why this breaks
							self.assertEqual(lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		{
					    """student_encoder_layers""": 2,
					    """student_decoder_layers""": 1,
					    """num_train_epochs""": 4,
					    """val_check_interval""": 0.25,
					    """alpha_hid""": 2.0,
					    """task""": """translation""",
					    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
					    """tokenizer_name""": MBART_TINY,
					    """teacher""": MBART_TINY,
					    """src_lang""": """en_XX""",
					    """tgt_lang""": """ro_RO""",
					}
					__SCREAMING_SNAKE_CASE	:		int   	=		self._test_distiller_cli(lowerCAmelCase__							,					check_contents=lowerCAmelCase__						)
					assert model.model.config.model_type == "mbart"

					__SCREAMING_SNAKE_CASE	:		str   	=		list(Path(model.output_dir						).glob("""*.ckpt"""						)						)
					self.assertEqual(1							,					len(lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE	:		int   	=		list(Path(model.output_dir						).glob("""**/*.bin"""						)						)
					__SCREAMING_SNAKE_CASE	:		str   	=		list(Path(model.output_dir						).glob("""best_tfmr/*"""						)						)
					assert len(lowerCAmelCase__						) > 2
					self.assertEqual(len(lowerCAmelCase__						)							,					2						)

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		{
					    """student_encoder_layers""": 1,
					    """student_decoder_layers""": 1,
					    """alpha_hid""": 2.0,
					    """teacher""": T5_TINY,
					    """model_name_or_path""": T5_TINY,
					    """tokenizer_name""": T5_TINY,
					}
					self._test_distiller_cli(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		{
					    """teacher""": T5_TINY,
					    """student""": T5_TINIER,
					    """model_name_or_path""": T5_TINIER,
					    """tokenizer_name""": T5_TINIER,
					}
					self._test_distiller_cli(lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			Tuple							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any=True						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		{
					    """label_smoothing""": 0.0,
					    """early_stopping_patience""": -1,
					    """train_batch_size""": 1,
					    """eval_batch_size""": 2,
					    """max_epochs""": 2,
					    """alpha_mlm""": 0.2,
					    """alpha_ce""": 0.8,
					    """do_predict""": True,
					    """model_name_or_path""": """sshleifer/tinier_bart""",
					    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
					    """val_check_interval""": 0.5,
					}
					default_updates.update(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		dict   	=		CHEAP_ARGS.copy()
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self.get_auto_remove_tmp_dir()

					args_d.update(data_dir=lowerCAmelCase__							,					output_dir=lowerCAmelCase__							,					**lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		distill_main(argparse.Namespace(**lowerCAmelCase__						)						)
					if not check_contents:
							return model
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		os.listdir(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		{os.path.basename(lowerCAmelCase__						) for p in contents}
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[p for p in contents if p.endswith("""ckpt"""						)]
					assert len(lowerCAmelCase__						) > 0

					self.assertIn("""test_generations.txt"""							,					lowerCAmelCase__						)
					self.assertIn("""test_results.txt"""							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		load_json(model.metrics_save_path						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		metrics["""val"""][-1]
					self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]							,					0.01						)
					self.assertGreaterEqual(1.0							,					last_step_stats["""val_avg_gen_time"""]						)
					self.assertIsInstance(last_step_stats[F"val_avg_{model.val_metric}"]							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1						)
					self.assertEqual(len(metrics["""val"""]						)							,					lowerCAmelCase__						)
					self.assertEqual(len(metrics["""test"""]						)							,					1						)
					return model






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			@parameterized.expand(
			    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]							,					)
			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		dict   	=		CHEAP_ARGS.copy()
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		0.1 if task == """translation""" else 0

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.get_auto_remove_tmp_dir()
					args_d.update(
					    data_dir=lowerCAmelCase__							,					model_name_or_path=lowerCAmelCase__							,					tokenizer_name=lowerCAmelCase__							,					train_batch_size=2							,					eval_batch_size=2							,					output_dir=lowerCAmelCase__							,					do_predict=lowerCAmelCase__							,					task=lowerCAmelCase__							,					src_lang="""en_XX"""							,					tgt_lang="""ro_RO"""							,					freeze_encoder=lowerCAmelCase__							,					freeze_embeds=lowerCAmelCase__							,					)
					assert "n_train" in args_d
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		argparse.Namespace(**lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		main(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		int   	=		module.model.get_input_embeddings()
					assert not input_embeds.weight.requires_grad
					if model == T5_TINY:
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		module.model.lm_head
							assert not lm_head.weight.requires_grad
							assert (lm_head.weight == input_embeds.weight).all().item()
					elif model == FSMT_TINY:
							__SCREAMING_SNAKE_CASE	:		str   	=		module.model.model
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		fsmt.decoder.embed_positions
							assert not embed_pos.weight.requires_grad
							assert not fsmt.decoder.embed_tokens.weight.requires_grad
							# check that embeds are not the same
							assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
					else:
							__SCREAMING_SNAKE_CASE	:		Any   	=		module.model.model
							__SCREAMING_SNAKE_CASE	:		str   	=		bart.decoder.embed_positions
							assert not embed_pos.weight.requires_grad
							assert not bart.shared.weight.requires_grad
							# check that embeds are the same
							assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
							assert bart.decoder.embed_tokens == bart.shared

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		load_json(module.output_dir / """text_batch.json"""						)
					assert isinstance(lowerCAmelCase__							,					lowerCAmelCase__						)
					assert len(lowerCAmelCase__						) >= 4

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		dict   	=		CHEAP_ARGS.copy()

					__SCREAMING_SNAKE_CASE	:		Dict   	=		"""summarization"""
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

					args_d.update(
					    data_dir=lowerCAmelCase__							,					tokenizer_name=lowerCAmelCase__							,					train_batch_size=2							,					eval_batch_size=2							,					do_predict=lowerCAmelCase__							,					task=lowerCAmelCase__							,					src_lang="""en_XX"""							,					tgt_lang="""ro_RO"""							,					freeze_encoder=lowerCAmelCase__							,					freeze_embeds=lowerCAmelCase__							,					)

					# test models whose config includes the extra_model_args
					__SCREAMING_SNAKE_CASE	:		Any   	=		BART_TINY
					__SCREAMING_SNAKE_CASE	:		str   	=		self.get_auto_remove_tmp_dir()
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		args_d.copy()
					args_da.update(
					    model_name_or_path=lowerCAmelCase__							,					output_dir=lowerCAmelCase__							,					)
					__SCREAMING_SNAKE_CASE	:		Any   	=		("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
					for p in extra_model_params:
							__SCREAMING_SNAKE_CASE	:		str   	=		0.5
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		argparse.Namespace(**lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		main(lowerCAmelCase__						)
					for p in extra_model_params:
							assert getattr(model.config							,					lowerCAmelCase__						) == 0.5, F"failed to override the model config for param {p}"

					# test models whose config doesn't include the extra_model_args
					__SCREAMING_SNAKE_CASE	:		int   	=		T5_TINY
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.get_auto_remove_tmp_dir()
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		args_d.copy()
					args_da.update(
					    model_name_or_path=lowerCAmelCase__							,					output_dir=lowerCAmelCase__							,					)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		"""encoder_layerdrop"""
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		0.5
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		argparse.Namespace(**lowerCAmelCase__						)
					with pytest.raises(lowerCAmelCase__						) as excinfo:
							__SCREAMING_SNAKE_CASE	:		str   	=		main(lowerCAmelCase__						)
					assert str(excinfo.value						) == F"model config doesn't have a `{unsupported_param}` attribute"



			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		dict   	=		CHEAP_ARGS.copy()

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		"""summarization"""
					__SCREAMING_SNAKE_CASE	:		Dict   	=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		BART_TINY
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.get_auto_remove_tmp_dir()

					args_d.update(
					    data_dir=lowerCAmelCase__							,					model_name_or_path=lowerCAmelCase__							,					output_dir=lowerCAmelCase__							,					tokenizer_name=lowerCAmelCase__							,					train_batch_size=2							,					eval_batch_size=2							,					do_predict=lowerCAmelCase__							,					task=lowerCAmelCase__							,					src_lang="""en_XX"""							,					tgt_lang="""ro_RO"""							,					freeze_encoder=lowerCAmelCase__							,					freeze_embeds=lowerCAmelCase__							,					)

					# emulate finetune.py
					__SCREAMING_SNAKE_CASE	:		Any   	=		argparse.ArgumentParser()
					__SCREAMING_SNAKE_CASE	:		str   	=		pl.Trainer.add_argparse_args(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		SummarizationModule.add_model_specific_args(lowerCAmelCase__							,					os.getcwd()						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		{"""--help""": True}

					# --help test
					with pytest.raises(lowerCAmelCase__						) as excinfo:
							with CaptureStdout() as cs:
									__SCREAMING_SNAKE_CASE	:		str   	=		parser.parse_args(lowerCAmelCase__						)
							assert False, "--help is expected to sys.exit"
					assert excinfo.type == SystemExit
					__SCREAMING_SNAKE_CASE	:		Dict   	=		lightning_base.arg_to_scheduler_metavar
					assert expected in cs.out, "--help is expected to list the supported schedulers"

					# --lr_scheduler=non_existing_scheduler test
					__SCREAMING_SNAKE_CASE	:		Any   	=		"""non_existing_scheduler"""
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		{F"--lr_scheduler={unsupported_param}"}
					with pytest.raises(lowerCAmelCase__						) as excinfo:
							with CaptureStderr() as cs:
									__SCREAMING_SNAKE_CASE	:		Dict   	=		parser.parse_args(lowerCAmelCase__						)
							assert False, "invalid argument is expected to sys.exit"
					assert excinfo.type == SystemExit
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		F"invalid choice: '{unsupported_param}'"
					assert expected in cs.err, F"should have bailed on invalid choice of scheduler {unsupported_param}"

					# --lr_scheduler=existing_scheduler test
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		"""cosine"""
					__SCREAMING_SNAKE_CASE	:		Dict   	=		args_d.copy()
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		supported_param
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		argparse.Namespace(**lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		int   	=		main(lowerCAmelCase__						)
					assert (
					    getattr(model.hparams							,					"""lr_scheduler"""						) == supported_param
					), F"lr_scheduler={supported_param} shouldn't fail"




'''simple docstring'''




import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
						from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Dict							,					lowerCAmelCase__					:			List[str]							,					lowerCAmelCase__					:			Tuple=1_3							,					lowerCAmelCase__					:			Tuple=3_2							,					lowerCAmelCase__					:			Optional[int]=2							,					lowerCAmelCase__					:			str=3							,					lowerCAmelCase__					:			Optional[int]=1_6							,					lowerCAmelCase__					:			List[str]=[1, 2, 1]							,					lowerCAmelCase__					:			Dict=[2, 2, 4]							,					lowerCAmelCase__					:			Any=2							,					lowerCAmelCase__					:			str=2.0							,					lowerCAmelCase__					:			List[Any]=True							,					lowerCAmelCase__					:			Any=0.0							,					lowerCAmelCase__					:			List[Any]=0.0							,					lowerCAmelCase__					:			Optional[Any]=0.1							,					lowerCAmelCase__					:			Optional[int]="gelu"							,					lowerCAmelCase__					:			Dict=False							,					lowerCAmelCase__					:			Union[str, Any]=True							,					lowerCAmelCase__					:			Any=0.02							,					lowerCAmelCase__					:			List[str]=1E-5							,					lowerCAmelCase__					:			Union[str, Any]=True							,					lowerCAmelCase__					:			int=None							,					lowerCAmelCase__					:			Optional[Any]=True							,					lowerCAmelCase__					:			int=1_0							,					lowerCAmelCase__					:			List[Any]=8							,					lowerCAmelCase__					:			Union[str, Any]=["stage1", "stage2"]							,					lowerCAmelCase__					:			Any=[1, 2]							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		parent
					__SCREAMING_SNAKE_CASE	:		Any   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		str   	=		image_size
					__SCREAMING_SNAKE_CASE	:		Any   	=		patch_size
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		num_channels
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		embed_dim
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		depths
					__SCREAMING_SNAKE_CASE	:		Any   	=		num_heads
					__SCREAMING_SNAKE_CASE	:		Any   	=		window_size
					__SCREAMING_SNAKE_CASE	:		Dict   	=		mlp_ratio
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		qkv_bias
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE	:		str   	=		drop_path_rate
					__SCREAMING_SNAKE_CASE	:		Any   	=		hidden_act
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		use_absolute_embeddings
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		patch_norm
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		layer_norm_eps
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		is_training
					__SCREAMING_SNAKE_CASE	:		int   	=		scope
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		type_sequence_label_size
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		encoder_stride
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		out_features
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		out_indices

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE	:		int   	=		ids_tensor([self.batch_size]							,					self.type_sequence_label_size						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.get_config()

					return config, pixel_values, labels

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					return SwinConfig(
					    image_size=self.image_size							,					patch_size=self.patch_size							,					num_channels=self.num_channels							,					embed_dim=self.embed_dim							,					depths=self.depths							,					num_heads=self.num_heads							,					window_size=self.window_size							,					mlp_ratio=self.mlp_ratio							,					qkv_bias=self.qkv_bias							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					drop_path_rate=self.drop_path_rate							,					hidden_act=self.hidden_act							,					use_absolute_embeddings=self.use_absolute_embeddings							,					path_norm=self.patch_norm							,					layer_norm_eps=self.layer_norm_eps							,					initializer_range=self.initializer_range							,					encoder_stride=self.encoder_stride							,					out_features=self.out_features							,					out_indices=self.out_indices							,					)

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		SwinModel(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
					__SCREAMING_SNAKE_CASE	:		Dict   	=		int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

					self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, expected_seq_len, expected_dim)						)

			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		SwinBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		Any   	=		model(lowerCAmelCase__						)

					# verify hidden states
					self.parent.assertEqual(len(result.feature_maps						)							,					len(config.out_features						)						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,					[self.batch_size, model.channels[0], 1_6, 1_6]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,					len(config.out_features						)						)

					# verify backbone works with out_features=None
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		None
					__SCREAMING_SNAKE_CASE	:		str   	=		SwinBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(lowerCAmelCase__						)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps						)							,					1						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,					[self.batch_size, model.channels[-1], 4, 4]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,					1						)

			def UpperCamelCase__ (							self					:			Tuple							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		SwinForMaskedImageModeling(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__						)
					self.parent.assertEqual(
					    result.logits.shape							,					(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

					# test greyscale images
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		1
					__SCREAMING_SNAKE_CASE	:		Dict   	=		SwinForMaskedImageModeling(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model(lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, 1, self.image_size, self.image_size)						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.type_sequence_label_size
					__SCREAMING_SNAKE_CASE	:		Dict   	=		SwinForImageClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model(lowerCAmelCase__							,					labels=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)						)

					# test greyscale images
					__SCREAMING_SNAKE_CASE	:		Dict   	=		1
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		SwinForImageClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		model(lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)						)



			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		self.prepare_config_and_inputs()
					(
					    (
					    __SCREAMING_SNAKE_CASE
					)	,						(
					    __SCREAMING_SNAKE_CASE
					)	,						(
					    __SCREAMING_SNAKE_CASE
					)	,						
					)	:		Union[str, Any]   	=		config_and_inputs
					__SCREAMING_SNAKE_CASE	:		Any   	=		{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_torch
class 						_UpperCamelCase  (							lowerCamelCase__   , lowerCamelCase__   , unittest.TestCase   ):


			'''simple docstring'''

			_A				:		Dict         =		(
			    (
			        SwinModel,
			        SwinBackbone,
			        SwinForImageClassification,
			        SwinForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			_A				:		List[str]         =		(
			    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_A				:		int         =		True

			_A				:		str         =		False
			_A				:		str         =		False
			_A				:		Optional[int]         =		False

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		SwinModelTester(self						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		ConfigTester(self							,					config_class=lowerCAmelCase__							,					embed_dim=3_7						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					return

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__						)

			@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""						)
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					pass

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					super().test_training_gradient_checkpointing()

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)

			@unittest.skip(reason="""Swin does not use inputs_embeds"""						)
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""						)
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					pass

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(lowerCAmelCase__						)
							self.assertIsInstance(model.get_input_embeddings()							,					(nn.Module)						)
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowerCAmelCase__							,					nn.Linear						)						)

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Any   	=		model_class(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE	:		str   	=		[*signature.parameters.keys()]

							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		True
							__SCREAMING_SNAKE_CASE	:		int   	=		False
							__SCREAMING_SNAKE_CASE	:		str   	=		True
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)
							__SCREAMING_SNAKE_CASE	:		Any   	=		outputs.attentions
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		len(self.model_tester.depths						)
							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							__SCREAMING_SNAKE_CASE	:		str   	=		True
							__SCREAMING_SNAKE_CASE	:		Dict   	=		config.window_size**2
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		Dict   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		outputs.attentions
							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]						)							,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,					)
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		len(lowerCAmelCase__						)

							# Check attention is always last and order is fine
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		True
							__SCREAMING_SNAKE_CASE	:		str   	=		True
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)

							# also another +1 for reshaped_hidden_states
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		1 if model_class.__name__ == """SwinBackbone""" else 2
							self.assertEqual(out_len + added_hidden_states							,					len(lowerCAmelCase__						)						)

							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		outputs.attentions

							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]						)							,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,					)

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model_class(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		outputs.hidden_states

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		getattr(
					    self.model_tester							,					"""expected_num_hidden_layers"""							,					len(self.model_tester.depths						) + 1						)
					self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

					# Swin has a different seq_length
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    config.patch_size
					    if isinstance(config.patch_size							,					collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					__SCREAMING_SNAKE_CASE	:		Any   	=		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]						)							,					[num_patches, self.model_tester.embed_dim]							,					)

					if not model_class.__name__ == "SwinBackbone":
							__SCREAMING_SNAKE_CASE	:		int   	=		outputs.reshaped_hidden_states
							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		reshaped_hidden_states[0].shape
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(
							    reshaped_hidden_states[0].view(lowerCAmelCase__							,					lowerCAmelCase__							,					height * width						).permute(0							,					2							,					1						)
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]						)							,					[num_patches, self.model_tester.embed_dim]							,					)

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size							,					collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True

							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		3

					__SCREAMING_SNAKE_CASE	:		Any   	=		(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size							,					collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(
					    config.patch_size
					    if isinstance(config.patch_size							,					collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		int   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					(padded_height, padded_width)						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					(padded_height, padded_width)						)

			@slow
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		SwinModel.from_pretrained(lowerCAmelCase__						)
							self.assertIsNotNone(lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE	:		Any   	=		_config_zero_init(lowerCAmelCase__						)
					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model_class(config=lowerCAmelCase__						)
							for name, param in model.named_parameters():
									if "embeddings" not in name and param.requires_grad:
											self.assertIn(
											    ((param.data.mean() * 1E9).round() / 1E9).item()							,					[0.0, 1.0]							,					msg=F"Parameter {name} of model {model_class} seems not properly initialized"							,					)






@require_vision
@require_torch
class 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					return (
					    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						)
					    if is_vision_available()
					    else None
					)



			@slow
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						).to(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.default_image_processor

					__SCREAMING_SNAKE_CASE	:		Dict   	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		image_processor(images=lowerCAmelCase__							,					return_tensors="""pt"""						).to(lowerCAmelCase__						)

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(**lowerCAmelCase__						)

					# verify the logits
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.Size((1, 1_0_0_0)						)
					self.assertEqual(outputs.logits.shape							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.tensor([-0.09_48, -0.64_54, -0.09_21]						).to(lowerCAmelCase__						)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,					lowerCAmelCase__							,					atol=1E-4						)						)






@require_torch
class 						_UpperCamelCase  (							unittest.TestCase   , lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Tuple         =		(SwinBackbone,) if is_torch_available() else ()
			_A				:		Optional[int]         =		SwinConfig

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		SwinModelTester(self						)