



'''simple docstring'''





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A					:       str									=     500_000

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

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

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

      snake_case       :				Union[str, Any]		    =map(__lowerCamelCase				)

      snake_case       :				Dict		    =map(__lowerCamelCase	,		batched=__lowerCamelCase				)

      snake_case       :				List[str]		    =map(__lowerCamelCase	,		function=lambda lowerCamelCase_				: None	,		batched=__lowerCamelCase				)

      with dataset.formatted_as(type='''numpy'''				):
         snake_case       :				Dict		    =map(__lowerCamelCase	,		function=lambda lowerCamelCase_				: None	,		batched=__lowerCamelCase				)

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

      with dataset.formatted_as(type='''torch'''	,		columns='''numbers'''				):
         snake_case       :				Optional[int]		    =map(__lowerCamelCase	,		function=lambda lowerCamelCase_				: None	,		batched=__lowerCamelCase				)

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

      snake_case       :				Dict		    =map(__lowerCamelCase	,		function=__lowerCamelCase	,		batched=__lowerCamelCase				)

      snake_case       :				Union[str, Any]		    =filter(__lowerCamelCase				)

      # 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(__lowerCamelCase	,		'''wb'''				) as f:
      f.write(json.dumps(__lowerCamelCase				).encode('''utf-8'''				)				)


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









'''simple docstring'''






import warnings

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






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["image_processor", "tokenizer"]
       UpperCAmelCase		 ="CLIPImageProcessor"
       UpperCAmelCase		 =("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(     self    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  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.'    ,				snake_case    ,				)
             _UpperCAmelCase  :  str				       =kwargs.pop('feature_extractor')

          _UpperCAmelCase  :  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__(snake_case    ,				snake_case)






       def __call__(     self    ,				snake_case=None    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''

          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  :  List[Any]				       =self.tokenizer(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if images is not None:
             _UpperCAmelCase  :  List[str]				       =self.image_processor(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

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






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    int:




          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Optional[int]:




          '''simple docstring'''
          return self.tokenizer.decode(*snake_case    ,				**snake_case)






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.tokenizer.model_input_names
          _UpperCAmelCase  :  Any				       =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






       @property
       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				snake_case    ,				)
          return self.image_processor_class






       @property
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				snake_case    ,				)
          return self.image_processor

