'''simple docstring'''







from ...processing_utils import ProcessorMixin






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






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											super().__init__(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def __call__(       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	kwargs.pop('''audio'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	kwargs.pop('''text'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[Any]													=	kwargs.pop('''text_target'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					str													=	kwargs.pop('''audio_target'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	kwargs.pop('''sampling_rate'''    ,							__SCREAMING_SNAKE_CASE		)

											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:
																	lowercase_     :					Any													=	self.feature_extractor(__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							sampling_rate=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											elif text is not None:
																	lowercase_     :					Optional[Any]													=	self.tokenizer(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_     :					List[str]													=	None

											if audio_target is not None:
																	lowercase_     :					Any													=	self.feature_extractor(audio_target=__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							sampling_rate=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Dict													=	targets['''input_values''']
											elif text_target is not None:
																	lowercase_     :					Dict													=	self.tokenizer(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					List[Any]													=	targets['''input_ids''']
											else:
																	lowercase_     :					str													=	None

											if inputs is None:
																	return targets

											if targets is not None:
																	lowercase_     :					Dict													=	labels

																	lowercase_     :					Optional[Any]													=	targets.get('''attention_mask'''		)
																	if decoder_attention_mask is not None:
																							lowercase_     :					Any													=	decoder_attention_mask

											return inputs






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					int													=	kwargs.pop('''input_values'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[Any]													=	kwargs.pop('''input_ids'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	kwargs.pop('''labels'''    ,							__SCREAMING_SNAKE_CASE		)

											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:
																	lowercase_     :					List[str]													=	self.feature_extractor.pad(__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											elif input_ids is not None:
																	lowercase_     :					Optional[int]													=	self.tokenizer.pad(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_     :					str													=	None

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

											if inputs is None:
																	return targets

											if targets is not None:
																	lowercase_     :					List[str]													=	labels

																	lowercase_     :					List[str]													=	targets.get('''attention_mask'''		)
																	if decoder_attention_mask is not None:
																							lowercase_     :					Dict													=	decoder_attention_mask

											return inputs






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  List[Any]   							=	{
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  int   							=	[
							    "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "XCLIPModel",
							    "XCLIPPreTrainedModel",
							    "XCLIPTextModel",
							    "XCLIPVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_x_clip import (
							    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XCLIPConfig,
							    XCLIPTextConfig,
							    XCLIPVisionConfig,
							)
							from .processing_x_clip import XCLIPProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_x_clip import (
														    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XCLIPModel,
														    XCLIPPreTrainedModel,
														    XCLIPTextModel,
														    XCLIPVisionModel,
														)

else:
							import sys

							_lowercase			:  List[str]   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



