'''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 					UpperCAmelCase			(							A__			):




    '''simple docstring'''







    @slow
    @require_torch
    def    _UpperCamelCase     (							self			)				->			Optional[Any]:
         SCREAMING_SNAKE_CASE       :      Dict     						=							EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,				'prajjwal1/bert-tiny'			)
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							BertTokenizer.from_pretrained('bert-base-uncased'			)

         SCREAMING_SNAKE_CASE       :      str     						=							bertabert.config.encoder.vocab_size
         SCREAMING_SNAKE_CASE       :      List[str]     						=							tokenizer.sep_token_id
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							tokenizer.cls_token_id
         SCREAMING_SNAKE_CASE       :      str     						=							128

         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							datasets.load_dataset('cnn_dailymail'     ,				'3.0.0'     ,				split='train[:1%]'			)
         SCREAMING_SNAKE_CASE       :      Any     						=							datasets.load_dataset('cnn_dailymail'     ,				'3.0.0'     ,				split='validation[:1%]'			)

         SCREAMING_SNAKE_CASE       :      str     						=							train_dataset.select(range(32			)			)
         SCREAMING_SNAKE_CASE       :      Dict     						=							val_dataset.select(range(16			)			)

         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							4

         def _map_to_encoder_decoder_inputs(lowercase__			):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              SCREAMING_SNAKE_CASE       :      str     						=							tokenizer(batch['article']     ,				padding='max_length'     ,				truncation=lowercase__     ,				max_length=512			)
              SCREAMING_SNAKE_CASE       :      Optional[int]     						=							tokenizer(batch['highlights']     ,				padding='max_length'     ,				truncation=lowercase__     ,				max_length=128			)
              SCREAMING_SNAKE_CASE       :      Tuple     						=							inputs.input_ids
              SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							inputs.attention_mask

              SCREAMING_SNAKE_CASE       :      str     						=							outputs.input_ids
              SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							outputs.input_ids.copy()
              SCREAMING_SNAKE_CASE       :      List[str]     						=							[
                  [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              SCREAMING_SNAKE_CASE       :      List[str]     						=							outputs.attention_mask

              assert all(len(lowercase__			) == 512 for x in inputs.input_ids			)
              assert all(len(lowercase__			) == 128 for x in outputs.input_ids			)

              return batch

         def _compute_metrics(lowercase__			):
              SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							pred.label_ids
              SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							pred.predictions

              # all unnecessary tokens are removed
              SCREAMING_SNAKE_CASE       :      Any     						=							tokenizer.batch_decode(lowercase__     ,				skip_special_tokens=lowercase__			)
              SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							tokenizer.batch_decode(lowercase__     ,				skip_special_tokens=lowercase__			)

              SCREAMING_SNAKE_CASE       :      Tuple     						=							sum([int(pred_str[i] == label_str[i]			) for i in range(len(lowercase__			)			)]			) / len(lowercase__			)

              return {"accuracy": accuracy}

         # map train dataset
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							train_dataset.map(
             _map_to_encoder_decoder_inputs     ,				batched=lowercase__     ,				batch_size=lowercase__     ,				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
         SCREAMING_SNAKE_CASE       :      str     						=							val_dataset.map(
             _map_to_encoder_decoder_inputs     ,				batched=lowercase__     ,				batch_size=lowercase__     ,				remove_columns=['article', 'highlights']     ,				)
         val_dataset.set_format(
             type='torch'     ,				columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,				)

         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							self.get_auto_remove_tmp_dir()

         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							SeqaSeqTrainingArguments(
             output_dir=lowercase__     ,				per_device_train_batch_size=lowercase__     ,				per_device_eval_batch_size=lowercase__     ,				predict_with_generate=lowercase__     ,				evaluation_strategy='steps'     ,				do_train=lowercase__     ,				do_eval=lowercase__     ,				warmup_steps=0     ,				eval_steps=2     ,				logging_steps=2     ,				)

         # instantiate trainer
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							SeqaSeqTrainer(
             model=lowercase__     ,				args=lowercase__     ,				compute_metrics=_compute_metrics     ,				train_dataset=lowercase__     ,				eval_dataset=lowercase__     ,				tokenizer=lowercase__     ,				)

         # start training
         trainer.train()











import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A__      :     Tuple       =					{
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				if got_ver is None or want_ver is None:
								raise ValueError(
								    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
								    F""" reinstalling {pkg}."""							)
				if not ops[op](version.parse(lowerCamelCase_							)						,  version.parse(lowerCamelCase_							)							):
								raise ImportError(
								    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_ = None							):






				'''simple docstring'''
				lowercase__  					=     F"""\n{hint}""" if hint is not None else ''''''

				# non-versioned check
				if re.match(r'''^[\w_\-\d]+$'''						,  lowerCamelCase_							):
								lowercase__					,		lowercase__					,		lowercase__  					=     requirement, None, None
				else:
								lowercase__  					=     re.findall(r'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)'''						,  lowerCamelCase_							)
								if not match:
												raise ValueError(
												    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
												    F""" got {requirement}"""							)
								lowercase__					,		lowercase__  					=     match[0]
								lowercase__  					=     want_full.split(''','''							)  # there could be multiple requirements
								lowercase__  					=     {}
								for w in want_range:
												lowercase__  					=     re.findall(r'''^([\s!=<>]{1,2})(.+)'''						,  lowerCamelCase_							)
												if not match:
																raise ValueError(
																    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
																    F""" but got {requirement}"""							)
												lowercase__					,		lowercase__  					=     match[0]
												lowercase__  					=     want_ver
												if op not in ops:
																raise ValueError(F"""{requirement}: need one of {list(ops.keys()							)}, but got {op}"""							)

    # special case
				if pkg == "python":
								lowercase__  					=     '''.'''.join([str(lowerCamelCase_							) for x in sys.version_info[:3]]							)
								for op, want_ver in wanted.items():
												_compare_versions(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)
								return

				# check if any version is installed
				try:
								lowercase__  					=     importlib.metadata.version(lowerCamelCase_							)
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""							)

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
				return require_version(lowerCamelCase_						,  lowerCamelCase_							)




