


import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
  import librosa  # noqa: E402

  __UpperCAmelCase			:			Union[str, Any]			      =					True
  __UpperCAmelCase			:			Optional[int]			      =					""
except Exception as e:
  __UpperCAmelCase			:			Union[str, Any]			      =					False
  __UpperCAmelCase			:			Tuple			      =					(
      f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
  )


from PIL import Image  # noqa: E402



class  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "mel_config.json"






       @register_to_config
       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 22_050			,       __SCREAMING_SNAKE_CASE = 2_048			,       __SCREAMING_SNAKE_CASE = 512			,       __SCREAMING_SNAKE_CASE = 80			,       __SCREAMING_SNAKE_CASE = 32			,       ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		hop_length
              UpperCamelCase     :						List[str] 						=		sample_rate
              UpperCamelCase     :						List[Any] 						=		n_fft
              UpperCamelCase     :						List[str] 						=		top_db
              UpperCamelCase     :						Tuple 						=		n_iter
              self.set_resolution(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		None

              if not _librosa_can_be_imported:
                     raise ValueError(_import_error    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		x_res
              UpperCamelCase     :						Union[str, Any] 						=		y_res
              UpperCamelCase     :						Any 						=		self.y_res
              UpperCamelCase     :						Union[str, Any] 						=		self.x_res * self.hop_length - 1






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              if audio_file is not None:
                     UpperCamelCase						,			UpperCamelCase     :						Tuple 						=		librosa.load(__SCREAMING_SNAKE_CASE			,       mono=__SCREAMING_SNAKE_CASE			,       sr=self.sr    )
              else:
                     UpperCamelCase     :						Tuple 						=		raw_audio

              # Pad with silence if necessary.
              if len(self.audio    ) < self.x_res * self.hop_length:
                     UpperCamelCase     :						List[str] 						=		np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio    ),)    )]    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              return len(self.audio    ) // self.slice_size






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = 0    ):




              """simple docstring"""




              return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.sr






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		librosa.feature.melspectrogram(
                  y=self.get_audio_slice(__SCREAMING_SNAKE_CASE    )			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_mels=self.n_mels    )
              UpperCamelCase     :						Optional[int] 						=		librosa.power_to_db(__SCREAMING_SNAKE_CASE			,       ref=np.max			,       top_db=self.top_db    )
              UpperCamelCase     :						Optional[int] 						=		(((log_S + self.top_db) * 255 / self.top_db).clip(0			,       255    ) + 0.5).astype(np.uinta    )
              UpperCamelCase     :						List[Any] 						=		Image.fromarray(__SCREAMING_SNAKE_CASE    )
              return image






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		np.frombuffer(image.tobytes()			,       dtype='''uint8'''    ).reshape((image.height, image.width)    )
              UpperCamelCase     :						Tuple 						=		bytedata.astype('''float'''    ) * self.top_db / 255 - self.top_db
              UpperCamelCase     :						List[Any] 						=		librosa.db_to_power(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		librosa.feature.inverse.mel_to_audio(
                  __SCREAMING_SNAKE_CASE			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_iter=self.n_iter    )
              return audio





from typing import TYPE_CHECKING

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


__UpperCAmelCase			:			int			      =					{"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase			:			Optional[Any]			      =					["ViltFeatureExtractor"]
  __UpperCAmelCase			:			Union[str, Any]			      =					["ViltImageProcessor"]
  __UpperCAmelCase			:			Dict			      =					["ViltProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase			:			List[Any]			      =					[
      "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ViltForImageAndTextRetrieval",
      "ViltForImagesAndTextClassification",
      "ViltForTokenClassification",
      "ViltForMaskedLM",
      "ViltForQuestionAnswering",
      "ViltLayer",
      "ViltModel",
      "ViltPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vilt import ViltFeatureExtractor
    from .image_processing_vilt import ViltImageProcessor
    from .processing_vilt import ViltProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vilt import (
        VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViltForImageAndTextRetrieval,
        ViltForImagesAndTextClassification,
        ViltForMaskedLM,
        ViltForQuestionAnswering,
        ViltForTokenClassification,
        ViltLayer,
        ViltModel,
        ViltPreTrainedModel,
    )


else:
  import sys

  __UpperCAmelCase			:			Any			      =					_LazyModule(__name__, globals()["__file__"], _import_structure)

