
"""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)

_lowercase											=			logging.getLogger()
_lowercase											=			torch.cuda.is_available()
_lowercase											=			{
    '''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  _snake_case			(						snake_case__				:       Path		, snake_case__				:       list       ):
	A  				=				'\n'.join(snake_case__       )
	Path(snake_case__       ).open('w'       ).writelines(snake_case__       )


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


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






def  _snake_case			(						snake_case__				:       Optional[int]       ):
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(snake_case__		, F'{split}.source'       )		, snake_case__       )
		_dump_articles(os.path.join(snake_case__		, F'{split}.target'       )		, snake_case__       )
	return tmp_dir







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						@classmethod
						def   _SCREAMING_SNAKE_CASE (	cls       : Tuple )       ->				List[Any]:
							logging.disable(logging.CRITICAL )  # remove noisy download output from tracebacks
							return cls


						@slow
						@require_torch_gpu
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				Any:
							A  				=				list_models()
							A  				=				'sshleifer'
							A  				=				[x.modelId for x in model_list if x.modelId.startswith(A_ )]
							A  				=				['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
							A  				=				[]
							for m in model_ids:
								if m in allowed_to_be_broken:
									continue
								try:
									AutoConfig.from_pretrained(A_ )
								except Exception:
									failures.append(A_ )
							assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'


						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				List[str]:
							A  				=				{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
							self._test_distiller_cli(A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[int]:
							A  				=				{
							    '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',
							}
							A  				=				self._test_distiller_cli(A_					,check_contents=A_ )

							A  				=				list(Path(model.output_dir ).glob('*.ckpt' ) )
							self.assertEqual(1					,len(A_ ) )
							A  				=				list(Path(model.output_dir ).glob('**/*.bin' ) )
							self.assertEqual(len(A_ )					,2 )
							A  				=				lmap(str.strip					,Path(model.hparams.data_dir ).joinpath('test.source' ).open().readlines() )
							A  				=				tempfile.mktemp()  # XXX: not being cleaned up
							generate_summaries_or_translations(A_					,A_					,str(model.output_dir / 'best_tfmr' ) )
							self.assertTrue(Path(A_ ).exists() )

							A  				=				self.get_auto_remove_tmp_dir()
							convert_pl_to_hf(ckpts[0]					,transformer_ckpts[0].parent					,A_ )
							assert os.path.exists(os.path.join(A_					,'pytorch_model.bin' ) )


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Tuple:
							A  				=				AutoModelForSeqaSeqLM.from_pretrained(A_ )
							A				, A  				=				model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
							A  				=				torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]					,dtype=torch.long					,device=model.device )
							A  				=				target_ids[:, :-1].contiguous()  # Why this line?
							A  				=				target_ids[:, 1:].clone()  # why clone?
							A  				=				model(
							    A_					,attention_mask=A_					,decoder_input_ids=A_					,labels=A_					,use_cache=A_ ).loss

							A  				=				model(A_					,attention_mask=A_					,decoder_input_ids=A_					,use_cache=A_ ).logits

							A  				=				nn.functional.log_softmax(A_					,dim=-1 )
							A				, A  				=				label_smoothed_nll_loss(
							    A_					,A_					,0.1					,ignore_index=model.config.pad_token_id )
							with self.assertRaises(A_ ):
								# TODO: understand why this breaks
								self.assertEqual(A_					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				Union[str, Any]:
							A  				=				{
							    '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',
							}
							A  				=				self._test_distiller_cli(A_					,check_contents=A_ )
							assert model.model.config.model_type == "mbart"

							A  				=				list(Path(model.output_dir ).glob('*.ckpt' ) )
							self.assertEqual(1					,len(A_ ) )
							A  				=				list(Path(model.output_dir ).glob('**/*.bin' ) )
							A  				=				list(Path(model.output_dir ).glob('best_tfmr/*' ) )
							assert len(A_ ) > 2
							self.assertEqual(len(A_ )					,2 )


						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				Tuple:
							A  				=				{
							    '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(A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				Dict:
							A  				=				{
							    'teacher': T5_TINY,
							    'student': T5_TINIER,
							    'model_name_or_path': T5_TINIER,
							    'tokenizer_name': T5_TINIER,
							}
							self._test_distiller_cli(A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Tuple					,A_       : List[Any]=True )       ->				Any:
							A  				=				{
							    '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(A_ )
							A  				=				CHEAP_ARGS.copy()
							A  				=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
							A  				=				self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=A_					,output_dir=A_					,**A_ )
							A  				=				distill_main(argparse.Namespace(**A_ ) )
							if not check_contents:
								return model
							A  				=				os.listdir(A_ )
							A  				=				{os.path.basename(A_ ) for p in contents}
							A  				=				[p for p in contents if p.endswith('ckpt' )]
							assert len(A_ ) > 0

							self.assertIn('test_generations.txt'					,A_ )
							self.assertIn('test_results.txt'					,A_ )

							A  				=				load_json(model.metrics_save_path )
							A  				=				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}']					,A_ )
							A  				=				int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1 )
							self.assertEqual(len(metrics['val'] )					,A_ )
							self.assertEqual(len(metrics['test'] )					,1 )
							return model







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,)
						def   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : Optional[Any] )       ->				str:
							A  				=				CHEAP_ARGS.copy()
							A  				=				'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
							A  				=				0.1 if task == 'translation' else 0

							A  				=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
							A  				=				self.get_auto_remove_tmp_dir()
							args_d.update(
							    data_dir=A_					,model_name_or_path=A_					,tokenizer_name=A_					,train_batch_size=2					,eval_batch_size=2					,output_dir=A_					,do_predict=A_					,task=A_					,src_lang='en_XX'					,tgt_lang='ro_RO'					,freeze_encoder=A_					,freeze_embeds=A_					,)
							assert "n_train" in args_d
							A  				=				argparse.Namespace(**A_ )
							A  				=				main(A_ )

							A  				=				module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
								A  				=				module.model.lm_head
								assert not lm_head.weight.requires_grad
								assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
								A  				=				module.model.model
								A  				=				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:
								A  				=				module.model.model
								A  				=				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

							A  				=				load_json(module.output_dir / 'text_batch.json' )
							assert isinstance(A_					,A_ )
							assert len(A_ ) >= 4


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Optional[Any]:
							A  				=				CHEAP_ARGS.copy()

							A  				=				'summarization'
							A  				=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

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

							# test models whose config includes the extra_model_args
							A  				=				BART_TINY
							A  				=				self.get_auto_remove_tmp_dir()
							A  				=				args_d.copy()
							args_da.update(
							    model_name_or_path=A_					,output_dir=A_					,)
							A  				=				('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
								A  				=				0.5
							A  				=				argparse.Namespace(**A_ )
							A  				=				main(A_ )
							for p in extra_model_params:
								assert getattr(model.config					,A_ ) == 0.5, F'failed to override the model config for param {p}'

							# test models whose config doesn't include the extra_model_args
							A  				=				T5_TINY
							A  				=				self.get_auto_remove_tmp_dir()
							A  				=				args_d.copy()
							args_da.update(
							    model_name_or_path=A_					,output_dir=A_					,)
							A  				=				'encoder_layerdrop'
							A  				=				0.5
							A  				=				argparse.Namespace(**A_ )
							with pytest.raises(A_ ) as excinfo:
								A  				=				main(A_ )
							assert str(excinfo.value ) == F'model config doesn\'t have a `{unsupported_param}` attribute'




						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[Any]:
							A  				=				CHEAP_ARGS.copy()

							A  				=				'summarization'
							A  				=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

							A  				=				BART_TINY
							A  				=				self.get_auto_remove_tmp_dir()

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

							# emulate finetune.py
							A  				=				argparse.ArgumentParser()
							A  				=				pl.Trainer.add_argparse_args(A_ )
							A  				=				SummarizationModule.add_model_specific_args(A_					,os.getcwd() )
							A  				=				{'--help': True}

							# --help test
							with pytest.raises(A_ ) as excinfo:
								with CaptureStdout() as cs:
									A  				=				parser.parse_args(A_ )
								assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							A  				=				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
							A  				=				'non_existing_scheduler'
							A  				=				{F'--lr_scheduler={unsupported_param}'}
							with pytest.raises(A_ ) as excinfo:
								with CaptureStderr() as cs:
									A  				=				parser.parse_args(A_ )
								assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							A  				=				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
							A  				=				'cosine'
							A  				=				args_d.copy()
							A  				=				supported_param
							A  				=				argparse.Namespace(**A_ )
							A  				=				main(A_ )
							assert (
							    getattr(model.hparams					,'lr_scheduler' ) == supported_param
							), F'lr_scheduler={supported_param} shouldn\'t fail'

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase											=			{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			['''ViltFeatureExtractor''']
		_lowercase											=			['''ViltImageProcessor''']
		_lowercase											=			['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		_lowercase											=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)