






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 	A_ (	__lowercase   ):




      '''simple docstring'''


      __snake_case 	=       ["""image_processor""", """tokenizer"""]
      __snake_case 	=       """ViTImageProcessor"""
      __snake_case 	=       ("""CLIPTokenizer""", """CLIPTokenizerFast""")




      def __init__(    self:	Dict						,				a:	str=None						,				a:	Dict=None						,				**a:	List[str]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						None
       if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'						,				_a						,				)
        __lowerCamelCase		:							List[Any]		 =						kwargs.pop('feature_extractor'    )

       __lowerCamelCase		:							List[str]		 =						image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'    )
       if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'    )

       super().__init__(_a						,				_a    )




      def __call__(    self:	Optional[int]						,				a:	int=None						,				a:	int=None						,				a:	str=None						,				a:	Union[str, Any]=None						,				**a:	List[Any]    ):
       if text is None and visual_prompt is None and images is None:
        raise ValueError('You have to specify either text, visual prompt or images.'    )

       if text is not None and visual_prompt is not None:
        raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'    )

       if text is not None:
        __lowerCamelCase		:							int		 =						self.tokenizer(_a						,				return_tensors=_a						,				**_a    )

       if visual_prompt is not None:
        __lowerCamelCase		:							int		 =						self.image_processor(_a						,				return_tensors=_a						,				**_a    )

       if images is not None:
        __lowerCamelCase		:							Optional[Any]		 =						self.image_processor(_a						,				return_tensors=_a						,				**_a    )

       if visual_prompt is not None and images is not None:
        __lowerCamelCase		:							Any		 =						{
            'pixel_values': image_features.pixel_values,
            'conditional_pixel_values': prompt_features.pixel_values,
        }
        return encoding
       elif text is not None and images is not None:
        __lowerCamelCase		:							Dict		 =						image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       elif visual_prompt is not None:
        __lowerCamelCase		:							Tuple		 =						{
            'conditional_pixel_values': prompt_features.pixel_values,
        }
        return encoding
       else:
        return BatchEncoding(data=dict(**_a    )						,				tensor_type=_a    )




      def 					_snake_case     (    self:	Dict						,				*a:	Optional[Any]						,				**a:	Tuple    ):
       return self.tokenizer.batch_decode(*_a						,				**_a    )




      def 					_snake_case     (    self:	Dict						,				*a:	Tuple						,				**a:	Dict    ):
       return self.tokenizer.decode(*_a						,				**_a    )




      @property
      def 					_snake_case     (    self:	List[Any]    ):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,				_a						,				)
       return self.image_processor_class




      @property
      def 					_snake_case     (    self:	Tuple    ):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,				_a						,				)
       return self.image_processor





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						dataset.map(**SCREAMING_SNAKE_CASE__						)
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						dataset.filter(**SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							str		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  __lowerCamelCase		:							Any		 =						datasets.Features({'text': datasets.Value('string'						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							Any		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							List[Any]		 =						transformers.AutoTokenizer.from_pretrained('bert-base-cased'      ,	use_fast=SCREAMING_SNAKE_CASE__						)

  def tokenize(SCREAMING_SNAKE_CASE__						):
   return tokenizer(examples['text']						)

  __lowerCamelCase		:							str		 =						map(SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							int		 =						map(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='numpy'						):
   __lowerCamelCase		:							Union[str, Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='pandas'						):
   __lowerCamelCase		:							Any		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='torch'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[str]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='tensorflow'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						filter(SCREAMING_SNAKE_CASE__						)

  # Activate later when tokenizer support batched inputs
  # with dataset.formatted_as(type='numpy'):
  #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

 with open(SCREAMING_SNAKE_CASE__      ,	'wb'						) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()

