import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'Speech2TextFeatureExtractor'
  lowercase_			=     'Speech2TextTokenizer'




  def __init__(      self     :	Union[str, Any]     , a_     :	Optional[int]     , a_     :	Any				)->       List[str]:




       """simple docstring"""
       super().__init__(a_     , a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		self.feature_extractor
       SCREAMING_SNAKE_CASE__				:				Dict			=		False




  def __call__(      self     :	Tuple     , *a_     :	Optional[Any]     , **a_     :	Dict				)->       Optional[int]:




       """simple docstring"""
       # 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.'				)
            SCREAMING_SNAKE_CASE__				:				Any			=		kwargs.pop('raw_speech'				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		kwargs.pop('audio'     , a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		kwargs.pop('sampling_rate'     , a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		kwargs.pop('text'     , a_				)
       if len(a_				) > 0:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		args[0]
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		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:
            SCREAMING_SNAKE_CASE__				:				int			=		self.feature_extractor(a_     , *a_     , sampling_rate=a_     , **a_				)
       if text is not None:
            SCREAMING_SNAKE_CASE__				:				Any			=		self.tokenizer(a_     , **a_				)

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




  def 	__lowercase(      self     :	int     , *a_     :	List[Any]     , **a_     :	str				)->       Union[str, Any]:




       """simple docstring"""
       return self.tokenizer.batch_decode(*a_     , **a_				)




  def 	__lowercase(      self     :	Optional[int]     , *a_     :	Tuple     , **a_     :	str				)->       Union[str, Any]:




       """simple docstring"""
       return self.tokenizer.decode(*a_     , **a_				)




  @contextmanager
  def 	__lowercase(      self     :	Dict				)->       List[Any]:




       """simple docstring"""
       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.'				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		True
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.tokenizer
       yield
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.feature_extractor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		False




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0     ,  state.num_processes ) ) + [0]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



