




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Tuple      	=		'Speech2TextFeatureExtractor'
	__magic_name__   :       Optional[int]      	=		'Speech2TextTokenizer'






	def __init__(					self		:						Dict					,     lowerCAmelCase		:						Optional[Any]					,     lowerCAmelCase		:						Optional[int]	)	->       List[Any]:







						"""simple docstring"""







						super().__init__(lowerCAmelCase					,     lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      self.feature_extractor
						__UpperCamelCase					:	Dict             =      False






	def __call__(					self		:						Optional[int]					,     *lowerCAmelCase		:						str					,     **lowerCAmelCase		:						List[str]	)	->       Optional[Any]:







						"""simple docstring"""







						if self._in_target_context_manager:
											return self.current_processor(*lowerCAmelCase					,     **lowerCAmelCase	)

						if "raw_speech" in kwargs:
											warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""	)
											__UpperCamelCase					:	int             =      kwargs.pop("""raw_speech"""	)
						else:
											__UpperCamelCase					:	Any             =      kwargs.pop("""audio"""					,     lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      kwargs.pop("""sampling_rate"""					,     lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      kwargs.pop("""text"""					,     lowerCAmelCase	)
						if len(lowerCAmelCase	) > 0:
											__UpperCamelCase					:	List[Any]             =      args[0]
											__UpperCamelCase					:	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:
											__UpperCamelCase					:	int             =      self.feature_extractor(lowerCAmelCase					,     *lowerCAmelCase					,     sampling_rate=lowerCAmelCase					,     **lowerCAmelCase	)
						if text is not None:
											__UpperCamelCase					:	int             =      self.tokenizer(lowerCAmelCase					,     **lowerCAmelCase	)

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											__UpperCamelCase					:	int             =      encodings["""input_ids"""]
											return inputs






	def 					lowerCamelCase__     (					self		:						Dict					,     *lowerCAmelCase		:						str					,     **lowerCAmelCase		:						int	)	->       str:







						"""simple docstring"""







						return self.tokenizer.batch_decode(*lowerCAmelCase					,     **lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int					,     *lowerCAmelCase		:						Tuple					,     **lowerCAmelCase		:						Optional[Any]	)	->       Any:







						"""simple docstring"""







						return self.tokenizer.decode(*lowerCAmelCase					,     **lowerCAmelCase	)






	@contextmanager
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       List[str]:







						"""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."""	)
						__UpperCamelCase					:	List[Any]             =      True
						__UpperCamelCase					:	Dict             =      self.tokenizer
						yield
						__UpperCamelCase					:	List[Any]             =      self.feature_extractor
						__UpperCamelCase					:	Tuple             =      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__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)





def 				A__ (snake_case   :		int				)     ->						Any:
					__UpperCamelCase					:	List[str]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	List[Any]             =      gather(snake_case				)
					assert gathered_tensor.tolist() == list(range(1		,	state.num_processes**2 + 1				)				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						Dict:
					__UpperCamelCase					:	Any             =      [state.process_index]
					__UpperCamelCase					:	int             =      gather_object(snake_case				)
					assert len(snake_case				) == state.num_processes, F'''{gathered_obj}, {len(snake_case				)} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes				)				), F'''{gathered_obj} != {list(range(state.num_processes				)				)}'''





def 				A__ (snake_case   :		Any				)     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	Any             =      broadcast(snake_case				)
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
					assert broadcasted_tensor.tolist() == list(range(1		,	state.num_processes + 1				)				)





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# We need to pad the tensor with one more element if we are the main process
					# to ensure that we can pad
					if state.is_main_process:
										__UpperCamelCase					:	List[Any]             =      torch.arange(state.num_processes + 1				).to(state.device				)
					else:
										__UpperCamelCase					:	List[str]             =      torch.arange(state.num_processes				).to(state.device				)
					__UpperCamelCase					:	List[str]             =      pad_across_processes(snake_case				)
					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__ (snake_case   :		Tuple				)     ->						Dict:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	Optional[int]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""sum"""				)
					__UpperCamelCase					:	Optional[int]             =      torch.tensor([4.0, 6]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Optional[Any]				)     ->						Optional[Any]:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	List[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""mean"""				)
					__UpperCamelCase					:	Union[str, Any]             =      torch.tensor([2.0, 3]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Tuple				)     ->						int:
					# For xla_spawn (TPUs)
					main()





def 				A__ ()     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      PartialState()
					state.print(F'''State: {state}'''				)
					state.print("""testing gather"""				)
					test_gather(snake_case				)
					state.print("""testing gather_object"""				)
					test_gather_object(snake_case				)
					state.print("""testing broadcast"""				)
					test_broadcast(snake_case				)
					state.print("""testing pad_across_processes"""				)
					test_pad_across_processes(snake_case				)
					state.print("""testing reduce_sum"""				)
					test_reduce_sum(snake_case				)
					state.print("""testing reduce_mean"""				)
					test_reduce_mean(snake_case				)


if __name__ == "__main__":
	main()






