





"""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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'				,       'prajjwal1/bert-tiny'     )
  lowerCAmelCase    				= BertTokenizer.from_pretrained('bert-base-uncased'     )

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

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

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

  lowerCAmelCase    				= 4

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

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

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

   return batch

  def _compute_metrics(_snake_case     ):
   lowerCAmelCase    				= pred.label_ids
   lowerCAmelCase    				= pred.predictions

   # all unnecessary tokens are removed
   lowerCAmelCase    				= tokenizer.batch_decode(_snake_case				,       skip_special_tokens=_snake_case     )
   lowerCAmelCase    				= tokenizer.batch_decode(_snake_case				,       skip_special_tokens=_snake_case     )

   lowerCAmelCase    				= sum([int(pred_str[i] == label_str[i]     ) for i in range(len(_snake_case     )     )]     ) / len(_snake_case     )

   return {"accuracy": accuracy}

  # map train dataset
  lowerCAmelCase    				= train_dataset.map(
      _map_to_encoder_decoder_inputs				,       batched=_snake_case				,       batch_size=_snake_case				,       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
  lowerCAmelCase    				= val_dataset.map(
      _map_to_encoder_decoder_inputs				,       batched=_snake_case				,       batch_size=_snake_case				,       remove_columns=['article', 'highlights']				,       )
  val_dataset.set_format(
      type='torch'				,       columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']				,       )

  lowerCAmelCase    				= self.get_auto_remove_tmp_dir()

  lowerCAmelCase    				= SeqaSeqTrainingArguments(
      output_dir=_snake_case				,       per_device_train_batch_size=_snake_case				,       per_device_eval_batch_size=_snake_case				,       predict_with_generate=_snake_case				,       evaluation_strategy='steps'				,       do_train=_snake_case				,       do_eval=_snake_case				,       warmup_steps=0				,       eval_steps=2				,       logging_steps=2				,       )

  # instantiate trainer
  lowerCAmelCase    				= SeqaSeqTrainer(
      model=_snake_case				,       args=_snake_case				,       compute_metrics=_compute_metrics				,       train_dataset=_snake_case				,       eval_dataset=_snake_case				,       tokenizer=_snake_case				,       )

  # start training
  trainer.train()











import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		field(default='image-classification'	,  metadata={'include_in_asdict_even_if_is_default': True}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'image': Image()}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'labels': ClassLabel}   )
	__magic_name__   :       str      	=		"image"
	__magic_name__   :       str      	=		"labels"






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Optional[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						if self.label_column not in features:
											raise ValueError(F'''Column {self.label_column} is not present in features.'''	)
						if not isinstance(features[self.label_column]					,     lowerCAmelCase	):
											raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''	)
						__UpperCamelCase					:	List[str]             =      copy.deepcopy(self	)
						__UpperCamelCase					:	List[Any]             =      self.label_schema.copy()
						__UpperCamelCase					:	List[str]             =      features[self.label_column]
						__UpperCamelCase					:	List[str]             =      label_schema
						return task_template






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Dict[str, str]:







						"""simple docstring"""







						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}






