




'''simple docstring'''


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       A   (		_a   ):







							@slow
							@require_torch
							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							List[str]:


														"""simple docstring"""


														_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(lowerCAmelCase_	:  int ):
																					# Tokenizer will automatically set [BOS] <text> [EOS]
																					_a     					=    tokenizer(batch['''article''']							,      padding='''max_length'''							,      truncation=lowerCAmelCase_							,      max_length=5_12 )
																					_a     					=    tokenizer(batch['''highlights''']							,      padding='''max_length'''							,      truncation=lowerCAmelCase_							,      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(lowerCAmelCase_ ) == 5_12 for x in inputs.input_ids )
																					assert all(len(lowerCAmelCase_ ) == 1_28 for x in outputs.input_ids )

																					return batch

														def _compute_metrics(lowerCAmelCase_	:  int ):
																					_a     					=    pred.label_ids
																					_a     					=    pred.predictions

																					# all unnecessary tokens are removed
																					_a     					=    tokenizer.batch_decode(lowerCAmelCase_							,      skip_special_tokens=lowerCAmelCase_ )
																					_a     					=    tokenizer.batch_decode(lowerCAmelCase_							,      skip_special_tokens=lowerCAmelCase_ )

																					_a     					=    sum([int(pred_str[i] == label_str[i] ) for i in range(len(lowerCAmelCase_ ) )] ) / len(lowerCAmelCase_ )

																					return {"accuracy": accuracy}

														# map train dataset
														_a     					=    train_dataset.map(
														    _map_to_encoder_decoder_inputs							,      batched=lowerCAmelCase_							,      batch_size=lowerCAmelCase_							,      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=lowerCAmelCase_							,      batch_size=lowerCAmelCase_							,      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=lowerCAmelCase_							,      per_device_train_batch_size=lowerCAmelCase_							,      per_device_eval_batch_size=lowerCAmelCase_							,      predict_with_generate=lowerCAmelCase_							,      evaluation_strategy='''steps'''							,      do_train=lowerCAmelCase_							,      do_eval=lowerCAmelCase_							,      warmup_steps=0							,      eval_steps=2							,      logging_steps=2							,      )

														# instantiate trainer
														_a     					=    SeqaSeqTrainer(
														    model=lowerCAmelCase_							,      args=lowerCAmelCase_							,      compute_metrics=_compute_metrics							,      train_dataset=lowerCAmelCase_							,      eval_dataset=lowerCAmelCase_							,      tokenizer=lowerCAmelCase_							,      )

														# start training
														trainer.train()







'''simple docstring'''


import argparse
from collections import defaultdict

import yaml


_snake_case				:				int       							=		'docs/source/en/_toctree.yml'
def 		snake_case_   (UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    defaultdict(UpperCamelCase				)
							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(UpperCamelCase				) > 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(UpperCamelCase			,						key=lambda UpperCamelCase				: s["title"].lower()				)
def 		snake_case_   (UpperCamelCase	:  str=False				):





							'''simple docstring'''


							with open(UpperCamelCase			,						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(UpperCamelCase				) if '''sections''' in section]
							_a     					=    False
							for idx, modality_doc in modalities_docs:
														_a     					=    modality_doc['''sections''']
														_a     					=    clean_model_doc_toc(UpperCamelCase				)

														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(UpperCamelCase			,						'''w'''			,						encoding='''utf-8'''				) as f:
																												f.write(yaml.dump(UpperCamelCase			,						allow_unicode=UpperCamelCase				)				)
														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__":
				_snake_case				:				List[str]       							=		argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				_snake_case				:				List[Any]       							=		parser.parse_args()

				check_model_doc(args.fix_and_overwrite)

