




'''simple docstring'''



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_    	=				5_0_0_0_0
UpperCamelCase_    	=				5_0_0_0

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




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      for i in range(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      str    					=						dataset[i]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      for i in range(0      ,len(__UpperCamelCase    )      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						dataset[i : i + batch_size]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      with dataset.formatted_as(type=__UpperCamelCase    ):
            for i in range(__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						dataset[i]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      with dataset.formatted_as(type=__UpperCamelCase    ):
            for i in range(0      ,__UpperCamelCase      ,__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      str    					=						dataset[i : i + batch_size]




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						{'num examples': SPEED_TEST_N_EXAMPLES}
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[
          (read, {'length': SMALL_TEST}),
          (read, {'length': SPEED_TEST_N_EXAMPLES}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
      ]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[
          (read, {'length': SMALL_TEST}),
          (read, {'length': SPEED_TEST_N_EXAMPLES}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print('generating dataset'    )
            SCREAMING_SNAKE_CASE   :      Any    					=						datasets.Features(
                {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
            SCREAMING_SNAKE_CASE   :      int    					=						generate_example_dataset(
                os.path.join(__UpperCamelCase      ,'dataset.arrow'    )      ,__UpperCamelCase      ,num_examples=__UpperCamelCase      ,seq_shapes={'list': (1_00,)}      ,)
            print('first set of iterations'    )
            for func, kwargs in functions:
                  print(func.__name__      ,str(__UpperCamelCase    )    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						func(__UpperCamelCase      ,**__UpperCamelCase    )

            print('shuffling dataset'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						dataset.shuffle()
            print('Second set of iterations (after shuffling'    )
            for func, kwargs in functions_shuffled:
                  print('shuffled '      ,func.__name__      ,str(__UpperCamelCase    )    )
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						func(
                      __UpperCamelCase      ,**__UpperCamelCase    )

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


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








import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE__     :     List[Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				warnings.warn(
				    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use PerceiverImageProcessor instead.'      ,     SCREAMING_SNAKE_CASE      ,     )
				super().__init__(*SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
