



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "Wav2Vec2FeatureExtractor"
						a__   =  "AutoTokenizer"







						def __init__(	self      :			Tuple	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Tuple )      ->				Optional[int]:
									super().__init__(__lowerCamelCase	,	__lowerCamelCase )
									A							:					Dict              =						self.feature_extractor
									A							:					List[str]              =						False







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Tuple	,	__lowerCamelCase      :			Union[str, Any]	,	**__lowerCamelCase      :			Tuple )      ->				str:
									try:
												return super().from_pretrained(__lowerCamelCase	,	**__lowerCamelCase )
									except OSError:
												warnings.warn(
												    F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
												    " include a `tokenizer_class` attribute is deprecated and will be "
												    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
												    " attribute to either your `config.json` or `tokenizer_config.json` "
												    "file to suppress this warning: "	,	__lowerCamelCase	,	)

												A							:					List[str]              =						WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase	,	**__lowerCamelCase )
												A							:					Dict              =						WavaVecaCTCTokenizer.from_pretrained(__lowerCamelCase	,	**__lowerCamelCase )

												return cls(feature_extractor=__lowerCamelCase	,	tokenizer=__lowerCamelCase )







						def __call__(	self      :			Optional[int]	,	*__lowerCamelCase      :			List[str]	,	**__lowerCamelCase      :			Tuple )      ->				List[Any]:
									# For backward compatibility
									if self._in_target_context_manager:
												return self.current_processor(*__lowerCamelCase	,	**__lowerCamelCase )

									if "raw_speech" in kwargs:
												warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
												A							:					List[str]              =						kwargs.pop("raw_speech" )
									else:
												A							:					Optional[int]              =						kwargs.pop("audio"	,	__lowerCamelCase )
									A							:					str              =						kwargs.pop("sampling_rate"	,	__lowerCamelCase )
									A							:					Dict              =						kwargs.pop("text"	,	__lowerCamelCase )
									if len(__lowerCamelCase ) > 0:
												A							:					Any              =						args[0]
												A							:					List[str]              =						args[1:]

									if audio is None and text is None:
												raise ValueError("You need to specify either an `audio` or `text` input to process." )

									if audio is not None:
												A							:					int              =						self.feature_extractor(__lowerCamelCase	,	*__lowerCamelCase	,	sampling_rate=__lowerCamelCase	,	**__lowerCamelCase )
									if text is not None:
												A							:					str              =						self.tokenizer(__lowerCamelCase	,	**__lowerCamelCase )

									if text is None:
												return inputs
									elif audio is None:
												return encodings
									else:
												A							:					Optional[Any]              =						encodings["input_ids"]
												return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	*__lowerCamelCase      :			Any	,	**__lowerCamelCase      :			Union[str, Any] )      ->				str:
									# For backward compatibility
									if self._in_target_context_manager:
												return self.current_processor.pad(*__lowerCamelCase	,	**__lowerCamelCase )

									A							:					Tuple              =						kwargs.pop("input_features"	,	__lowerCamelCase )
									A							:					Tuple              =						kwargs.pop("labels"	,	__lowerCamelCase )
									if len(__lowerCamelCase ) > 0:
												A							:					Optional[Any]              =						args[0]
												A							:					Tuple              =						args[1:]

									if input_features is not None:
												A							:					int              =						self.feature_extractor.pad(__lowerCamelCase	,	*__lowerCamelCase	,	**__lowerCamelCase )
									if labels is not None:
												A							:					Tuple              =						self.tokenizer.pad(__lowerCamelCase	,	**__lowerCamelCase )

									if labels is None:
												return input_features
									elif input_features is None:
												return labels
									else:
												A							:					Any              =						labels["input_ids"]
												return input_features







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	*__lowerCamelCase      :			Any	,	**__lowerCamelCase      :			List[str] )      ->				List[Any]:
									return self.tokenizer.batch_decode(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			Any )      ->				List[Any]:
									return self.tokenizer.decode(*__lowerCamelCase	,	**__lowerCamelCase )







						@contextmanager
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Union[str, Any]:
									warnings.warn(
									    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
									    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
									    "your audio inputs, or in a separate call." )
									A							:					int              =						True
									A							:					int              =						self.tokenizer
									yield
									A							:					Tuple              =						self.feature_extractor
									A							:					Any              =						False




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE           =  {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """InstructBlipQFormerModel""",
							    """InstructBlipPreTrainedModel""",
							    """InstructBlipForConditionalGeneration""",
							    """InstructBlipVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

							__SCREAMING_SNAKE_CASE           =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)