
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
					import datasets





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	@slow
	@require_torch
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,"prajjwal1/bert-tiny"			)
					__A             =			BertTokenizer.from_pretrained("bert-base-uncased"			)

					__A             =			bertabert.config.encoder.vocab_size
					__A             =			tokenizer.sep_token_id
					__A             =			tokenizer.cls_token_id
					__A             =			1_28

					__A             =			datasets.load_dataset("cnn_dailymail"							,"3.0.0"							,split="train[:1%]"			)
					__A             =			datasets.load_dataset("cnn_dailymail"							,"3.0.0"							,split="validation[:1%]"			)

					__A             =			train_dataset.select(range(32			)			)
					__A             =			val_dataset.select(range(16			)			)

					__A             =			4

					def _map_to_encoder_decoder_inputs(A :			int			):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									__A             =			tokenizer(batch["article"]							,padding="max_length"							,truncation=A							,max_length=5_12			)
									__A             =			tokenizer(batch["highlights"]							,padding="max_length"							,truncation=A							,max_length=1_28			)
									__A             =			inputs.input_ids
									__A             =			inputs.attention_mask

									__A             =			outputs.input_ids
									__A             =			outputs.input_ids.copy()
									__A             =			[
									    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
									]
									__A             =			outputs.attention_mask

									assert all(len(A			) == 5_12 for x in inputs.input_ids			)
									assert all(len(A			) == 1_28 for x in outputs.input_ids			)

									return batch

					def _compute_metrics(A :			List[str]			):
									__A             =			pred.label_ids
									__A             =			pred.predictions

									# all unnecessary tokens are removed
									__A             =			tokenizer.batch_decode(A							,skip_special_tokens=A			)
									__A             =			tokenizer.batch_decode(A							,skip_special_tokens=A			)

									__A             =			sum([int(pred_str[i] == label_str[i]			) for i in range(len(A			)			)]			) / len(A			)

									return {"accuracy": accuracy}

					# map train dataset
					__A             =			train_dataset.map(
					    _map_to_encoder_decoder_inputs							,batched=A							,batch_size=A							,remove_columns=["article", "highlights"]							,)
					train_dataset.set_format(
					    type="torch"							,columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,)

					# same for validation dataset
					__A             =			val_dataset.map(
					    _map_to_encoder_decoder_inputs							,batched=A							,batch_size=A							,remove_columns=["article", "highlights"]							,)
					val_dataset.set_format(
					    type="torch"							,columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,)

					__A             =			self.get_auto_remove_tmp_dir()

					__A             =			SeqaSeqTrainingArguments(
					    output_dir=A							,per_device_train_batch_size=A							,per_device_eval_batch_size=A							,predict_with_generate=A							,evaluation_strategy="steps"							,do_train=A							,do_eval=A							,warmup_steps=0							,eval_steps=2							,logging_steps=2							,)

					# instantiate trainer
					__A             =			SeqaSeqTrainer(
					    model=A							,args=A							,compute_metrics=_compute_metrics							,train_dataset=A							,eval_dataset=A							,tokenizer=A							,)

					# start training
					trainer.train()


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
