


"""simple docstring"""


from ...processing_utils import ProcessorMixin
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[int]          ="""SpeechT5FeatureExtractor"""
							__UpperCAmelCase		:					List[Any]          ="""SpeechT5Tokenizer"""

							def __init__(  self					,     __a					,     __a			):
										super().__init__(__a					,     __a			)

							def __call__(  self					,     *__a					,     **__a			):
										__lowerCAmelCase       					=    kwargs.pop("audio"					,     __a			)
										__lowerCAmelCase       					=    kwargs.pop("text"					,     __a			)
										__lowerCAmelCase       					=    kwargs.pop("text_target"					,     __a			)
										__lowerCAmelCase       					=    kwargs.pop("audio_target"					,     __a			)
										__lowerCAmelCase       					=    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:
													__lowerCAmelCase       					=    self.feature_extractor(__a					,     *__a					,     sampling_rate=__a					,     **__a			)
										elif text is not None:
													__lowerCAmelCase       					=    self.tokenizer(__a					,     **__a			)
										else:
													__lowerCAmelCase       					=    None

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

										if inputs is None:
													return targets

										if targets is not None:
													__lowerCAmelCase       					=    labels

													__lowerCAmelCase       					=    targets.get("attention_mask"			)
													if decoder_attention_mask is not None:
																__lowerCAmelCase       					=    decoder_attention_mask

										return inputs

							def     snake_case       (  self					,     *__a					,     **__a			):
										__lowerCAmelCase       					=    kwargs.pop("input_values"					,     __a			)
										__lowerCAmelCase       					=    kwargs.pop("input_ids"					,     __a			)
										__lowerCAmelCase       					=    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:
													__lowerCAmelCase       					=    self.feature_extractor.pad(__a					,     *__a					,     **__a			)
										elif input_ids is not None:
													__lowerCAmelCase       					=    self.tokenizer.pad(__a					,     **__a			)
										else:
													__lowerCAmelCase       					=    None

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

										if inputs is None:
													return targets

										if targets is not None:
													__lowerCAmelCase       					=    labels

													__lowerCAmelCase       					=    targets.get("attention_mask"			)
													if decoder_attention_mask is not None:
																__lowerCAmelCase       					=    decoder_attention_mask

										return inputs

							def     snake_case       (  self					,     *__a					,     **__a			):
										return self.tokenizer.batch_decode(*__a					,     **__a			)



							def     snake_case       (  self					,     *__a					,     **__a			):
										return self.tokenizer.decode(*__a					,     **__a			)








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A  :     Union[str, 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:
	A  :     Optional[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

	A  :     Union[str, Any]    					=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




