
"""simple docstring"""





from ...processing_utils import ProcessorMixin






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'SpeechT5FeatureExtractor'
		lowercase__      =				'SpeechT5Tokenizer'



		def __init__( self    ,		__a    ,		__a)	->	int:






							'''simple docstring'''
							super().__init__(__a    ,		__a)



		def __call__( self    ,		*__a    ,		**__a)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  kwargs.pop('''audio'''    ,		__a)
							_UpperCamelCase										=  kwargs.pop('''text'''    ,		__a)
							_UpperCamelCase										=  kwargs.pop('''text_target'''    ,		__a)
							_UpperCamelCase										=  kwargs.pop('''audio_target'''    ,		__a)
							_UpperCamelCase										=  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:
												_UpperCamelCase										=  self.feature_extractor(__a    ,		*__a    ,		sampling_rate=__a    ,		**__a)
							elif text is not None:
												_UpperCamelCase										=  self.tokenizer(__a    ,		**__a)
							else:
												_UpperCamelCase										=  None

							if audio_target is not None:
												_UpperCamelCase										=  self.feature_extractor(audio_target=__a    ,		*__a    ,		sampling_rate=__a    ,		**__a)
												_UpperCamelCase										=  targets['''input_values''']
							elif text_target is not None:
												_UpperCamelCase										=  self.tokenizer(__a    ,		**__a)
												_UpperCamelCase										=  targets['''input_ids''']
							else:
												_UpperCamelCase										=  None

							if inputs is None:
												return targets

							if targets is not None:
												_UpperCamelCase										=  labels

												_UpperCamelCase										=  targets.get('''attention_mask''')
												if decoder_attention_mask is not None:
																	_UpperCamelCase										=  decoder_attention_mask

							return inputs



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  kwargs.pop('''input_values'''    ,		__a)
							_UpperCamelCase										=  kwargs.pop('''input_ids'''    ,		__a)
							_UpperCamelCase										=  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:
												_UpperCamelCase										=  self.feature_extractor.pad(__a    ,		*__a    ,		**__a)
							elif input_ids is not None:
												_UpperCamelCase										=  self.tokenizer.pad(__a    ,		**__a)
							else:
												_UpperCamelCase										=  None

							if labels is not None:
												if "input_ids" in labels or (isinstance(__a    ,		__a) and "input_ids" in labels[0]):
																	_UpperCamelCase										=  self.tokenizer.pad(__a    ,		**__a)
																	_UpperCamelCase										=  targets['''input_ids''']
												else:
																	_UpperCamelCase										=  self.feature_extractor.feature_size
																	_UpperCamelCase										=  self.feature_extractor.num_mel_bins
																	_UpperCamelCase										=  self.feature_extractor.pad(__a    ,		*__a    ,		**__a)
																	_UpperCamelCase										=  feature_size_hack
																	_UpperCamelCase										=  targets['''input_values''']
							else:
												_UpperCamelCase										=  None

							if inputs is None:
												return targets

							if targets is not None:
												_UpperCamelCase										=  labels

												_UpperCamelCase										=  targets.get('''attention_mask''')
												if decoder_attention_mask is not None:
																	_UpperCamelCase										=  decoder_attention_mask

							return inputs



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Optional[Any]:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a    ,		**__a)




		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Dict:






							'''simple docstring'''
							return self.tokenizer.decode(*__a    ,		**__a)








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bert'



		def __init__( self    ,		__a=3_05_22    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=2    ,		__a=0.02    ,		__a=1e-12    ,		__a=0    ,		__a="absolute"    ,		__a=True    ,		__a=None    ,		**__a    ,		)	->	Optional[Any]:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  classifier_dropout






class    _UpperCAmelCase(   lowerCamelCase     ):



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase										=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ])






