
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_ ):



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


          '''simple docstring'''


          snake_case       :   Tuple							=      EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"    ,						"prajjwal1/bert-tiny" )
          snake_case       :   Optional[int]							=      BertTokenizer.from_pretrained("bert-base-uncased" )

          snake_case       :   List[str]							=      bertabert.config.encoder.vocab_size
          snake_case       :   List[str]							=      tokenizer.sep_token_id
          snake_case       :   str							=      tokenizer.cls_token_id
          snake_case       :   Optional[int]							=      1_28

          snake_case       :   int							=      datasets.load_dataset("cnn_dailymail"    ,						"3.0.0"    ,						split="train[:1%]" )
          snake_case       :   List[Any]							=      datasets.load_dataset("cnn_dailymail"    ,						"3.0.0"    ,						split="validation[:1%]" )

          snake_case       :   int							=      train_dataset.select(range(32 ) )
          snake_case       :   List[str]							=      val_dataset.select(range(16 ) )

          snake_case       :   int							=      4

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

                 snake_case       :   Dict							=      outputs.input_ids
                 snake_case       :   List[str]							=      outputs.input_ids.copy()
                 snake_case       :   Dict							=      [
                     [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                 ]
                 snake_case       :   Optional[Any]							=      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(snake_case__		:					Tuple ):
                 snake_case       :   Tuple							=      pred.label_ids
                 snake_case       :   Optional[Any]							=      pred.predictions

                 # all unnecessary tokens are removed
                 snake_case       :   int							=      tokenizer.batch_decode(A_    ,						skip_special_tokens=A_ )
                 snake_case       :   Union[str, Any]							=      tokenizer.batch_decode(A_    ,						skip_special_tokens=A_ )

                 snake_case       :   Dict							=      sum([int(pred_str[i] == label_str[i] ) for i in range(len(A_ ) )] ) / len(A_ )

                 return {"accuracy": accuracy}

          # map train dataset
          snake_case       :   Union[str, Any]							=      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
          snake_case       :   Optional[int]							=      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"]    ,						)

          snake_case       :   List[str]							=      self.get_auto_remove_tmp_dir()

          snake_case       :   Dict							=      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
          snake_case       :   Optional[int]							=      SeqaSeqTrainer(
              model=A_    ,						args=A_    ,						compute_metrics=_compute_metrics    ,						train_dataset=A_    ,						eval_dataset=A_    ,						tokenizer=A_    ,						)

          # start training
          trainer.train()











import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


lowerCamelCase				:		Any     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  Dict						,							*A_					:  Optional[int]						,							**A_					:  int  )   ->	None:

       """simple docstring"""






       warnings.warn(
           'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use DonutImageProcessor instead.'						,							A_						,							)
       super().__init__(*A_						,							**A_  )



