


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """Speech2TextFeatureExtractor"""
      __snake_case 	=       """Speech2TextTokenizer"""




      def __init__(    self:	Tuple						,				a:	Any						,				a:	Dict    ):
       super().__init__(a						,				a    )
       __lowerCamelCase		:							str		 =						self.feature_extractor
       __lowerCamelCase		:							List[Any]		 =						False




      def __call__(    self:	List[Any]						,				*a:	Optional[Any]						,				**a:	Any    ):
       # For backward compatibility
       if self._in_target_context_manager:
        return self.current_processor(*a						,				**a    )

       if "raw_speech" in kwargs:
        warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'    )
        __lowerCamelCase		:							str		 =						kwargs.pop('raw_speech'    )
       else:
        __lowerCamelCase		:							Union[str, Any]		 =						kwargs.pop('audio'						,				a    )
       __lowerCamelCase		:							Any		 =						kwargs.pop('sampling_rate'						,				a    )
       __lowerCamelCase		:							Tuple		 =						kwargs.pop('text'						,				a    )
       if len(a    ) > 0:
        __lowerCamelCase		:							Any		 =						args[0]
        __lowerCamelCase		:							Optional[int]		 =						args[1:]

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

       if audio is not None:
        __lowerCamelCase		:							int		 =						self.feature_extractor(a						,				*a						,				sampling_rate=a						,				**a    )
       if text is not None:
        __lowerCamelCase		:							Optional[int]		 =						self.tokenizer(a						,				**a    )

       if text is None:
        return inputs
       elif audio is None:
        return encodings
       else:
        __lowerCamelCase		:							List[str]		 =						encodings['input_ids']
        return inputs




      def 					_snake_case     (    self:	Union[str, Any]						,				*a:	Union[str, Any]						,				**a:	List[Any]    ):
       return self.tokenizer.batch_decode(*a						,				**a    )




      def 					_snake_case     (    self:	Optional[int]						,				*a:	Tuple						,				**a:	Optional[Any]    ):
       return self.tokenizer.decode(*a						,				**a    )




      @contextmanager
      def 					_snake_case     (    self:	Any    ):
       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 audio inputs, or in a separate call.'    )
       __lowerCamelCase		:							List[Any]		 =						True
       __lowerCamelCase		:							Optional[int]		 =						self.tokenizer
       yield
       __lowerCamelCase		:							Tuple		 =						self.feature_extractor
       __lowerCamelCase		:							List[str]		 =						False





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0
lowercase_	   =     5_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__						):
 for i in range(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Tuple		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(0      ,	len(SCREAMING_SNAKE_CASE__						)      ,	SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Optional[Any]		 =						dataset[i : i + batch_size]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							str		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(0      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							int		 =						dataset[i : i + batch_size]
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Union[str, Any]		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 __lowerCamelCase		:							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': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (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': 1_000}),
 ]

 __lowerCamelCase		:							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': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (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': 1_000}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('generating dataset'						)
  __lowerCamelCase		:							Optional[int]		 =						datasets.Features(
      {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							str		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__      ,	seq_shapes={'list': (100,)}      ,	)
  print('first set of iterations'						)
  for func, kwargs in functions:
   print(func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							Optional[int]		 =						func(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

  print('shuffling dataset'						)
  __lowerCamelCase		:							str		 =						dataset.shuffle()
  print('Second set of iterations (after shuffling'						)
  for func, kwargs in functions_shuffled:
   print('shuffled '      ,	func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							int		 =						func(
       SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

 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_iterating()

