


from ...processing_utils import ProcessorMixin


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'SpeechT5FeatureExtractor'
						lowerCamelCase_         =     'SpeechT5Tokenizer'




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






									"""simple docstring"""






									super().__init__(__A   ,     __A						)




						def __call__(	self						:   List[Any]   ,     *__A						:   List[Any]   ,     **__A						:   List[Any]						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :List[str]						  =    kwargs.pop("audio"   ,     __A						)
									a__ :Any						  =    kwargs.pop("text"   ,     __A						)
									a__ :int						  =    kwargs.pop("text_target"   ,     __A						)
									a__ :int						  =    kwargs.pop("audio_target"   ,     __A						)
									a__ :List[str]						  =    kwargs.pop("sampling_rate"   ,     __A						)

									if audio is not None and text is not None:
												raise ValueError(
												    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"						)
									if audio_target is not None and text_target is not None:
												raise ValueError(
												    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"						)
									if audio is None and audio_target is None and text is None and text_target is None:
												raise ValueError(
												    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."						)

									if audio is not None:
												a__ :Any						  =    self.feature_extractor(__A   ,     *__A   ,     sampling_rate=__A   ,     **__A						)
									elif text is not None:
												a__ :List[Any]						  =    self.tokenizer(__A   ,     **__A						)
									else:
												a__ :Optional[int]						  =    None

									if audio_target is not None:
												a__ :str						  =    self.feature_extractor(audio_target=__A   ,     *__A   ,     sampling_rate=__A   ,     **__A						)
												a__ :Any						  =    targets["input_values"]
									elif text_target is not None:
												a__ :Tuple						  =    self.tokenizer(__A   ,     **__A						)
												a__ :Any						  =    targets["input_ids"]
									else:
												a__ :List[Any]						  =    None

									if inputs is None:
												return targets

									if targets is not None:
												a__ :int						  =    labels

												a__ :Union[str, Any]						  =    targets.get("attention_mask"						)
												if decoder_attention_mask is not None:
															a__ :str						  =    decoder_attention_mask

									return inputs




						def  _snake_case					(	self						:   str   ,     *__A						:   Union[str, Any]   ,     **__A						:   Dict						)      ->Tuple:






									"""simple docstring"""






									a__ :Any						  =    kwargs.pop("input_values"   ,     __A						)
									a__ :List[Any]						  =    kwargs.pop("input_ids"   ,     __A						)
									a__ :int						  =    kwargs.pop("labels"   ,     __A						)

									if input_values is not None and input_ids is not None:
												raise ValueError("Cannot process both `input_values` and `input_ids` inputs."						)
									if input_values is None and input_ids is None and labels is None:
												raise ValueError(
												    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."						)

									if input_values is not None:
												a__ :List[Any]						  =    self.feature_extractor.pad(__A   ,     *__A   ,     **__A						)
									elif input_ids is not None:
												a__ :List[str]						  =    self.tokenizer.pad(__A   ,     **__A						)
									else:
												a__ :Dict						  =    None

									if labels is not None:
												if "input_ids" in labels or (isinstance(__A   ,     __A						) and "input_ids" in labels[0]):
															a__ :Optional[Any]						  =    self.tokenizer.pad(__A   ,     **__A						)
															a__ :List[str]						  =    targets["input_ids"]
												else:
															a__ :List[str]						  =    self.feature_extractor.feature_size
															a__ :Optional[int]						  =    self.feature_extractor.num_mel_bins
															a__ :List[str]						  =    self.feature_extractor.pad(__A   ,     *__A   ,     **__A						)
															a__ :List[str]						  =    feature_size_hack
															a__ :List[str]						  =    targets["input_values"]
									else:
												a__ :Optional[Any]						  =    None

									if inputs is None:
												return targets

									if targets is not None:
												a__ :Any						  =    labels

												a__ :Optional[int]						  =    targets.get("attention_mask"						)
												if decoder_attention_mask is not None:
															a__ :List[Any]						  =    decoder_attention_mask

									return inputs




						def  _snake_case					(	self						:   Optional[Any]   ,     *__A						:   List[str]   ,     **__A						:   Tuple						)      ->str:






									"""simple docstring"""






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






						def  _snake_case					(	self						:   int   ,     *__A						:   Dict   ,     **__A						:   str						)      ->Union[str, Any]:






									"""simple docstring"""






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






# Copyright 2021 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 argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


snake_case__			 =						[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Tuple=None	,  a	:   int=None	,  a	:   List[str]=None				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    True
			while ask_again:
						a__ :str						  =    input(a				)
						try:
									if default is not None and len(a				) == 0:
												return default
									return convert_value(a				) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(a				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   Union[str, Any]=[]	,  a	:   str=None	,  a	:   str=0				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    BulletMenu(a	,  a				)
			a__ :int						  =    menu.run(default_choice=a				)
			return convert_value(a				) if convert_value is not None else result


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Dict:





			"""simple docstring"""
			a__ :str						  =    int(a				)
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	List[str]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    int(a				)
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	str:





			"""simple docstring"""
			a__ :int						  =    int(a				)
			return DynamoBackend(DYNAMO_BACKENDS[value]				).value


def 				lowerCamelCase__	(      a	:   Any				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)


def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	List[str]:





			"""simple docstring"""
			return {"yes": True, "no": False}[value.lower()]







class 					lowerCAmelCase_ (       argparse.RawDescriptionHelpFormatter):




						def  _snake_case					(	self						:   Tuple   ,     __A						:   Tuple   ,     __A						:   int   ,     __A						:   Tuple   ,     __A						:   Any						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    super()._format_usage(__A   ,     __A   ,     __A   ,     __A						)
									a__ :Any						  =    usage.replace("<command> [<args>] "   ,     ""						)
									return usage


