





"""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      __SCREAMING_SNAKE_CASE    (						__lowerCamelCase   ):







  '''simple docstring'''


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

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

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

        _UpperCamelCase      :	Tuple						=       train_dataset.select(range(32	)	)
        _UpperCamelCase      :	Tuple						=       val_dataset.select(range(16	)	)

        _UpperCamelCase      :	Any						=       4

        def _map_to_encoder_decoder_inputs(__a   :       List[Any]	):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              _UpperCamelCase      :	int						=       tokenizer(batch["article"]					,   padding="max_length"					,   truncation=UpperCamelCase_					,   max_length=512	)
              _UpperCamelCase      :	int						=       tokenizer(batch["highlights"]					,   padding="max_length"					,   truncation=UpperCamelCase_					,   max_length=128	)
              _UpperCamelCase      :	Tuple						=       inputs.input_ids
              _UpperCamelCase      :	Any						=       inputs.attention_mask

              _UpperCamelCase      :	Any						=       outputs.input_ids
              _UpperCamelCase      :	Optional[Any]						=       outputs.input_ids.copy()
              _UpperCamelCase      :	Union[str, Any]						=       [
                  [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
              ]
              _UpperCamelCase      :	Tuple						=       outputs.attention_mask

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

              return batch

        def _compute_metrics(__a   :       int	):
              _UpperCamelCase      :	Tuple						=       pred.label_ids
              _UpperCamelCase      :	Union[str, Any]						=       pred.predictions

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

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

              return {"accuracy": accuracy}

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

        _UpperCamelCase      :	int						=       self.get_auto_remove_tmp_dir()

        _UpperCamelCase      :	Union[str, Any]						=       SeqaSeqTrainingArguments(
            output_dir=UpperCamelCase_					,   per_device_train_batch_size=UpperCamelCase_					,   per_device_eval_batch_size=UpperCamelCase_					,   predict_with_generate=UpperCamelCase_					,   evaluation_strategy="steps"					,   do_train=UpperCamelCase_					,   do_eval=UpperCamelCase_					,   warmup_steps=0					,   eval_steps=2					,   logging_steps=2					,   )

        # instantiate trainer
        _UpperCamelCase      :	List[Any]						=       SeqaSeqTrainer(
            model=UpperCamelCase_					,   args=UpperCamelCase_					,   compute_metrics=_compute_metrics					,   train_dataset=UpperCamelCase_					,   eval_dataset=UpperCamelCase_					,   tokenizer=UpperCamelCase_					,   )

        # start training
        trainer.train()












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase__				  =   {
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["OwlViTFeatureExtractor"]
  lowerCamelCase__				  =   ["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
