



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase							:Optional[Any]       = {
    '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
    '''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:Union[str, Any]       = ['''Speech2TextTokenizer''']

try:
						if not is_speech_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:List[Any]       = ['''Speech2TextFeatureExtractor''']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:Dict       = [
						    '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFSpeech2TextForConditionalGeneration''',
						    '''TFSpeech2TextModel''',
						    '''TFSpeech2TextPreTrainedModel''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:int       = [
						    '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''Speech2TextForConditionalGeneration''',
						    '''Speech2TextModel''',
						    '''Speech2TextPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
						from .processing_speech_to_text import SpeechaTextProcessor

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_speech_to_text import SpeechaTextTokenizer

						try:
												if not is_speech_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_speech_to_text import (
												    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSpeechaTextForConditionalGeneration,
												    TFSpeechaTextModel,
												    TFSpeechaTextPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text import (
												    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextForConditionalGeneration,
												    SpeechaTextModel,
												    SpeechaTextPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase							:Optional[Any]       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)