
"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE   :							Dict							      =	"AutoTokenizer"




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					Tuple:
          super().__init__(lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					self.image_processor




      def __call__(							self   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       **lowercase_							)						->					Dict:

          if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'							)

          if text is not None:
              UpperCAmelCase									=					self.tokenizer(lowercase_   ,       return_tensors=lowercase_   ,       **lowercase_							)

          if images is not None:
              UpperCAmelCase									=					self.image_processor(lowercase_   ,       return_tensors=lowercase_   ,       **lowercase_							)

          if text is not None and images is not None:
              UpperCAmelCase									=					image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowercase_							)   ,       tensor_type=lowercase_							)




      def     a_      (							self   ,       *lowercase_   ,       **lowercase_							)						->					int:
          return self.tokenizer.batch_decode(*lowercase_   ,       **lowercase_							)




      def     a_      (							self   ,       *lowercase_   ,       **lowercase_							)						->					Any:
          return self.tokenizer.decode(*lowercase_   ,       **lowercase_							)




      @property
      def     a_      (							self							)						->					Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]





"""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							(		SCREAMING_SNAKE_CASE_							):




      @slow
      @require_torch
      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,       'prajjwal1/bert-tiny'							)
          UpperCAmelCase									=					BertTokenizer.from_pretrained('bert-base-uncased'							)

          UpperCAmelCase									=					bertabert.config.encoder.vocab_size
          UpperCAmelCase									=					tokenizer.sep_token_id
          UpperCAmelCase									=					tokenizer.cls_token_id
          UpperCAmelCase									=					1_2_8

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

          UpperCAmelCase									=					train_dataset.select(range(3_2							)							)
          UpperCAmelCase									=					val_dataset.select(range(1_6							)							)

          UpperCAmelCase									=					4

          def _map_to_encoder_decoder_inputs(lowercase_							):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              UpperCAmelCase									=					tokenizer(batch['article']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=5_1_2							)
              UpperCAmelCase									=					tokenizer(batch['highlights']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=1_2_8							)
              UpperCAmelCase									=					inputs.input_ids
              UpperCAmelCase									=					inputs.attention_mask

              UpperCAmelCase									=					outputs.input_ids
              UpperCAmelCase									=					outputs.input_ids.copy()
              UpperCAmelCase									=					[
                  [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              UpperCAmelCase									=					outputs.attention_mask

              assert all(len(lowercase_							) == 5_1_2 for x in inputs.input_ids							)
              assert all(len(lowercase_							) == 1_2_8 for x in outputs.input_ids							)

              return batch

          def _compute_metrics(lowercase_							):
              UpperCAmelCase									=					pred.label_ids
              UpperCAmelCase									=					pred.predictions

              # all unnecessary tokens are removed
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)

              UpperCAmelCase									=					sum([int(pred_str[i] == label_str[i]							) for i in range(len(lowercase_							)							)]							) / len(lowercase_							)

              return {"accuracy": accuracy}

          # map train dataset
          UpperCAmelCase									=					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
          UpperCAmelCase									=					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']   ,       )

          UpperCAmelCase									=					self.get_auto_remove_tmp_dir()

          UpperCAmelCase									=					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
          UpperCAmelCase									=					SeqaSeqTrainer(
              model=lowercase_   ,       args=lowercase_   ,       compute_metrics=_compute_metrics   ,       train_dataset=lowercase_   ,       eval_dataset=lowercase_   ,       tokenizer=lowercase_   ,       )

          # start training
          trainer.train()



