






"""simple docstring"""

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_	:				Tuple            =							'''Wav2Vec2FeatureExtractor'''
		a_	:				Tuple            =							'''AutoTokenizer'''





		def __init__(   self							:	int						,			a_							:	Any						,			a_							:	Dict	):
				super().__init__(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__	)
				lowerCAmelCase_   :							Any									=			self.feature_extractor
				lowerCAmelCase_   :							Union[str, Any]									=			False





		@classmethod
		def    lowerCamelCase       (   cls							:	Optional[int]						,			a_							:	Dict						,			**a_							:	Union[str, Any]	):






				try:
						return super().from_pretrained(SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)
				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: "						,			SCREAMING_SNAKE_CASE__						,			)

						lowerCAmelCase_   :							int									=			WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)
						lowerCAmelCase_   :							int									=			WavaVecaCTCTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)

						return cls(feature_extractor=SCREAMING_SNAKE_CASE__						,			tokenizer=SCREAMING_SNAKE_CASE__	)





		def __call__(   self							:	Optional[int]						,			*a_							:	Tuple						,			**a_							:	Optional[Any]	):






				if self._in_target_context_manager:
						return self.current_processor(*SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)

				if "raw_speech" in kwargs:
						warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."	)
						lowerCAmelCase_   :							Optional[Any]									=			kwargs.pop("raw_speech"	)
				else:
						lowerCAmelCase_   :							Union[str, Any]									=			kwargs.pop("audio"						,			SCREAMING_SNAKE_CASE__	)
				lowerCAmelCase_   :							List[Any]									=			kwargs.pop("sampling_rate"						,			SCREAMING_SNAKE_CASE__	)
				lowerCAmelCase_   :							List[Any]									=			kwargs.pop("text"						,			SCREAMING_SNAKE_CASE__	)
				if len(SCREAMING_SNAKE_CASE__	) > 0:
						lowerCAmelCase_   :							int									=			args[0]
						lowerCAmelCase_   :							Optional[int]									=			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:
						lowerCAmelCase_   :							List[Any]									=			self.feature_extractor(SCREAMING_SNAKE_CASE__						,			*SCREAMING_SNAKE_CASE__						,			sampling_rate=SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)
				if text is not None:
						lowerCAmelCase_   :							str									=			self.tokenizer(SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)

				if text is None:
						return inputs
				elif audio is None:
						return encodings
				else:
						lowerCAmelCase_   :							str									=			encodings['''input_ids''']
						return inputs





		def    lowerCamelCase       (   self							:	Dict						,			*a_							:	Union[str, Any]						,			**a_							:	Optional[int]	):






				if self._in_target_context_manager:
						return self.current_processor.pad(*SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)

				lowerCAmelCase_   :							Optional[int]									=			kwargs.pop("input_features"						,			SCREAMING_SNAKE_CASE__	)
				lowerCAmelCase_   :							Optional[int]									=			kwargs.pop("labels"						,			SCREAMING_SNAKE_CASE__	)
				if len(SCREAMING_SNAKE_CASE__	) > 0:
						lowerCAmelCase_   :							Dict									=			args[0]
						lowerCAmelCase_   :							int									=			args[1:]

				if input_features is not None:
						lowerCAmelCase_   :							List[str]									=			self.feature_extractor.pad(SCREAMING_SNAKE_CASE__						,			*SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)
				if labels is not None:
						lowerCAmelCase_   :							List[str]									=			self.tokenizer.pad(SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)

				if labels is None:
						return input_features
				elif input_features is None:
						return labels
				else:
						lowerCAmelCase_   :							Optional[Any]									=			labels['''input_ids''']
						return input_features





		def    lowerCamelCase       (   self							:	Tuple						,			*a_							:	int						,			**a_							:	str	):
				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)





		def    lowerCamelCase       (   self							:	List[Any]						,			*a_							:	Optional[Any]						,			**a_							:	Optional[int]	):
				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__						,			**SCREAMING_SNAKE_CASE__	)





		@contextmanager
		def    lowerCamelCase       (   self							:	Optional[int]	):
				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."	)
				lowerCAmelCase_   :							List[Any]									=			True
				lowerCAmelCase_   :							Tuple									=			self.tokenizer
				yield
				lowerCAmelCase_   :							Dict									=			self.feature_extractor
				lowerCAmelCase_   :							Dict									=			False







"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		["DPTFeatureExtractor"]
  lowerCamelCase_								=		["DPTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DPTForDepthEstimation",
      "DPTForSemanticSegmentation",
      "DPTModel",
      "DPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

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