
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							str           =		["image_processor", "tokenizer"]
     UpperCAmelCase__				:							Optional[int]           =		"AutoImageProcessor"
     UpperCAmelCase__				:							Optional[int]           =		"AutoTokenizer"





     def __init__(self      :    Optional[int]				,    _A      :    Union[str, Any]=None				,    _A      :    List[str]=None				,    **_A      :    int   )						->						Union[str, Any]:
         snake_case      =					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				,    )
             snake_case      =					kwargs.pop("feature_extractor"   )

         snake_case      =					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   )
         snake_case      =					self.image_processor
         snake_case      =					False





     def __call__(self      :    List[str]				,    *_A      :    List[Any]				,    **_A      :    List[Any]   )						->						List[Any]:
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor(*_A				,    **_A   )

         snake_case      =					kwargs.pop("images"				,    _A   )
         snake_case      =					kwargs.pop("text"				,    _A   )
         if len(_A   ) > 0:
             snake_case      =					args[0]
             snake_case      =					args[1:]

         if images is None and text is None:
             raise ValueError("You need to specify either an `images` or `text` input to process."   )

         if images is not None:
             snake_case      =					self.image_processor(_A				,    *_A				,    **_A   )
         if text is not None:
             snake_case      =					self.tokenizer(_A				,    **_A   )

         if text is None:
             return inputs
         elif images is None:
             return encodings
         else:
             snake_case      =					encodings["input_ids"]
             return inputs





     def 		UpperCAmelCase(self      :    Tuple				,    *_A      :    List[str]				,    **_A      :    Tuple   )						->						Dict:
         return self.tokenizer.batch_decode(*_A				,    **_A   )





     def 		UpperCAmelCase(self      :    Union[str, Any]				,    *_A      :    Any				,    **_A      :    Tuple   )						->						Optional[int]:
         return self.tokenizer.decode(*_A				,    **_A   )





     @contextmanager
     def 		UpperCAmelCase(self      :    List[Any]   )						->						Dict:
         warnings.warn(
             "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
             "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
             "your images inputs, or in a separate call."   )
         snake_case      =					True
         snake_case      =					self.tokenizer
         yield
         snake_case      =					self.image_processor
         snake_case      =					False





     @property
     def 		UpperCAmelCase(self      :    List[str]   )						->						str:
         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 		UpperCAmelCase(self      :    Tuple   )						->						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 datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

_A					,  _A  					= os.path.split(__file__)
_A  					= os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

    snake_case      =					{"num examples": SPEED_TEST_N_EXAMPLES}
    snake_case      =					(select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"		)
        snake_case      =					datasets.Features({"text": datasets.Value("string"		), "numbers": datasets.Value("float32"		)}		)
        snake_case      =					generate_example_dataset(
            os.path.join(A__ ,		"dataset.arrow"		) ,		A__ ,		num_examples=A__		)
        print("Functions"		)
        for func in functions:
            print(func.__name__		)
            snake_case      =					func(A__		)

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


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

