

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)

__snake_case							:     Tuple 						=	logging.getLogger()
__snake_case							:     List[Any] 						=	torch.cuda.is_available()
__snake_case							:     Optional[int] 						=	{
    """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 			_UpperCAmelCase							(		a__       ,					a__) ->    str:

			'''simple docstring'''



			a_		:		Tuple				    =				"""\n""".join(a__)
			Path(a__).open("""w""").writelines(a__)


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


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


def 			_UpperCAmelCase							(		a__) ->    Any:

			'''simple docstring'''



			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(a__       ,					f'''{split}.source''')       ,					a__)
						_dump_articles(os.path.join(a__       ,					f'''{split}.target''')       ,					a__)
			return tmp_dir



class    A__(a_    ):
						"""simple docstring"""






						@classmethod
						def 	UpperCamelCase__			(  cls  )      ->				Union[str, Any]:
									logging.disable(logging.CRITICAL  )  # remove noisy download output from tracebacks
									return cls





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





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		Union[str, Any]				    =				{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
									self._test_distiller_cli(_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		Any				    =				{
									    """student_encoder_layers""": 2,
									    """student_decoder_layers""": 1,
									    """max_epochs""": 4,
									    """val_check_interval""": 0.2_5,
									    """alpha_hid""": 2.0,
									    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
									}
									a_		:		List[Any]				    =				self._test_distiller_cli(_lowercase       ,     check_contents=_lowercase  )

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

									a_		:		List[str]				    =				self.get_auto_remove_tmp_dir()
									convert_pl_to_hf(ckpts[0]       ,     transformer_ckpts[0].parent       ,     _lowercase  )
									assert os.path.exists(os.path.join(_lowercase       ,     """pytorch_model.bin"""  )  )





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

									a_		:		Tuple				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     decoder_input_ids=_lowercase       ,     use_cache=_lowercase  ).logits

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





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		Optional[Any]				    =				{
									    """student_encoder_layers""": 2,
									    """student_decoder_layers""": 1,
									    """num_train_epochs""": 4,
									    """val_check_interval""": 0.2_5,
									    """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_		:		List[str]				    =				self._test_distiller_cli(_lowercase       ,     check_contents=_lowercase  )
									assert model.model.config.model_type == "mbart"

									a_		:		Any				    =				list(Path(model.output_dir  ).glob("""*.ckpt"""  )  )
									self.assertEqual(1       ,     len(_lowercase  )  )
									a_		:		Any				    =				list(Path(model.output_dir  ).glob("""**/*.bin"""  )  )
									a_		:		Dict				    =				list(Path(model.output_dir  ).glob("""best_tfmr/*"""  )  )
									assert len(_lowercase  ) > 2
									self.assertEqual(len(_lowercase  )       ,     2  )





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		Optional[int]				    =				{
									    """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(_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		Dict				    =				{
									    """teacher""": T5_TINY,
									    """student""": T5_TINIER,
									    """model_name_or_path""": T5_TINIER,
									    """tokenizer_name""": T5_TINIER,
									}
									self._test_distiller_cli(_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase=True  )      ->				int:
									a_		:		int				    =				{
									    """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(_lowercase  )
									a_		:		dict				    =				CHEAP_ARGS.copy()
									a_		:		Dict				    =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
									a_		:		Dict				    =				self.get_auto_remove_tmp_dir()

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

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

									a_		:		Union[str, Any]				    =				load_json(model.metrics_save_path  )
									a_		:		Any				    =				metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]       ,     0.0_1  )
									self.assertGreaterEqual(1.0       ,     last_step_stats["""val_avg_gen_time"""]  )
									self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']       ,     _lowercase  )
									a_		:		Optional[int]				    =				int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1  )
									self.assertEqual(len(metrics["""val"""]  )       ,     _lowercase  )
									self.assertEqual(len(metrics["""test"""]  )       ,     1  )
									return model







class    A__(a_    ):
						"""simple docstring"""






						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]       ,     )
						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[Any]:
									a_		:		dict				    =				CHEAP_ARGS.copy()
									a_		:		Dict				    =				"""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
									a_		:		Union[str, Any]				    =				0.1 if task == """translation""" else 0

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

									a_		:		Optional[Any]				    =				module.model.get_input_embeddings()
									assert not input_embeds.weight.requires_grad
									if model == T5_TINY:
												a_		:		List[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:
												a_		:		Optional[int]				    =				module.model.model
												a_		:		Union[str, Any]				    =				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_		:		int				    =				module.model.model
												a_		:		Optional[int]				    =				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_		:		str				    =				load_json(module.output_dir / """text_batch.json"""  )
									assert isinstance(_lowercase       ,     _lowercase  )
									assert len(_lowercase  ) >= 4





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		dict				    =				CHEAP_ARGS.copy()

									a_		:		Dict				    =				"""summarization"""
									a_		:		Tuple				    =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

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

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

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





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		dict				    =				CHEAP_ARGS.copy()

									a_		:		int				    =				"""summarization"""
									a_		:		Tuple				    =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

									a_		:		Optional[int]				    =				BART_TINY
									a_		:		int				    =				self.get_auto_remove_tmp_dir()

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

									# emulate finetune.py
									a_		:		str				    =				argparse.ArgumentParser()
									a_		:		Optional[int]				    =				pl.Trainer.add_argparse_args(_lowercase  )
									a_		:		str				    =				SummarizationModule.add_model_specific_args(_lowercase       ,     os.getcwd()  )
									a_		:		Optional[int]				    =				{"""--help""": True}

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							:     Optional[Any] 						=	logging.get_logger(__name__)

__snake_case							:     str 						=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	'''tvlt'''





						def __init__(  self       ,     _lowercase=224       ,     _lowercase=2_048       ,     _lowercase=128       ,     _lowercase=[16, 16]       ,     _lowercase=[16, 16]       ,     _lowercase=3       ,     _lowercase=1       ,     _lowercase=8       ,     _lowercase=768       ,     _lowercase=12       ,     _lowercase=12       ,     _lowercase=3_072       ,     _lowercase="gelu"       ,     _lowercase=0.0       ,     _lowercase=0.0       ,     _lowercase=0.0_2       ,     _lowercase=1e-6       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=16       ,     _lowercase=512       ,     _lowercase=8       ,     _lowercase=2_048       ,     _lowercase=0.7_5       ,     _lowercase=0.1_5       ,     _lowercase="frame-level"       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="classification"       ,     **_lowercase       ,     )      ->				Tuple:
									super().__init__(**_lowercase  )

									if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    F'''got {audio_mask_type}'''  )

									a_		:		int				    =				image_size
									a_		:		str				    =				spectrogram_length
									a_		:		int				    =				frequency_length
									a_		:		str				    =				image_patch_size
									a_		:		int				    =				audio_patch_size
									a_		:		Any				    =				num_image_channels
									a_		:		str				    =				num_audio_channels
									a_		:		int				    =				num_frames

									a_		:		Optional[int]				    =				hidden_size
									a_		:		Tuple				    =				num_hidden_layers
									a_		:		Any				    =				num_attention_heads
									a_		:		Union[str, Any]				    =				intermediate_size
									a_		:		Tuple				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		int				    =				attention_probs_dropout_prob
									a_		:		Any				    =				initializer_range
									a_		:		int				    =				layer_norm_eps
									a_		:		Union[str, Any]				    =				qkv_bias
									a_		:		str				    =				use_mean_pooling

									a_		:		List[Any]				    =				decoder_num_attention_heads
									a_		:		Optional[Any]				    =				decoder_hidden_size
									a_		:		Dict				    =				decoder_num_hidden_layers
									a_		:		Optional[Any]				    =				decoder_intermediate_size
									a_		:		Dict				    =				pixel_mask_ratio
									a_		:		int				    =				audio_mask_ratio
									a_		:		Optional[Any]				    =				audio_mask_type

									a_		:		Union[str, Any]				    =				task_matching
									a_		:		Dict				    =				task_mae
									a_		:		Union[str, Any]				    =				loss_type


