

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__     :					str								=			True
       UpperCamelCase__     :					Union[str, Any]								=			""
except Exception as e:
       UpperCamelCase__     :					Optional[Any]								=			False
       UpperCamelCase__     :					Union[str, Any]								=			(
           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 					__snake_case     (			lowerCAmelCase__   ,       lowerCAmelCase__				):
  __lowerCAmelCase   :		List[Any]         =     'mel_config.json'

  @register_to_config
  def __init__(    self  ,						_A = 256  ,						_A = 256  ,						_A = 22050  ,						_A = 2048  ,						_A = 512  ,						_A = 80  ,						_A = 32  ,						):
      SCREAMING_SNAKE_CASE_													=  hop_length
      SCREAMING_SNAKE_CASE_													=  sample_rate
      SCREAMING_SNAKE_CASE_													=  n_fft
      SCREAMING_SNAKE_CASE_													=  top_db
      SCREAMING_SNAKE_CASE_													=  n_iter
      self.set_resolution(_A  ,						_A)
      SCREAMING_SNAKE_CASE_													=  None

      if not _librosa_can_be_imported:
          raise ValueError(_import_error)

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  x_res
      SCREAMING_SNAKE_CASE_													=  y_res
      SCREAMING_SNAKE_CASE_													=  self.y_res
      SCREAMING_SNAKE_CASE_													=  self.x_res * self.hop_length - 1

  def   lowerCAmelCase__   (    self  ,						_A = None  ,						_A = None):
      if audio_file is not None:
          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  librosa.load(_A  ,						mono=_A  ,						sr=self.sr)
      else:
          SCREAMING_SNAKE_CASE_													=  raw_audio

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

  def   lowerCAmelCase__   (    self):
      return len(self.audio) // self.slice_size

  def   lowerCAmelCase__   (    self  ,						_A = 0):
      return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

  def   lowerCAmelCase__   (    self):
      return self.sr

  def   lowerCAmelCase__   (    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  librosa.feature.melspectrogram(
          y=self.get_audio_slice(_A)  ,						sr=self.sr  ,						n_fft=self.n_fft  ,						hop_length=self.hop_length  ,						n_mels=self.n_mels)
      SCREAMING_SNAKE_CASE_													=  librosa.power_to_db(_A  ,						ref=np.max  ,						top_db=self.top_db)
      SCREAMING_SNAKE_CASE_													=  (((log_S + self.top_db) * 255 / self.top_db).clip(0  ,						255) + 0.5).astype(np.uinta)
      SCREAMING_SNAKE_CASE_													=  Image.fromarray(_A)
      return image


  def   lowerCAmelCase__   (    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  np.frombuffer(image.tobytes()  ,						dtype='uint8').reshape((image.height, image.width))
      SCREAMING_SNAKE_CASE_													=  bytedata.astype('float') * self.top_db / 255 - self.top_db
      SCREAMING_SNAKE_CASE_													=  librosa.db_to_power(_A)
      SCREAMING_SNAKE_CASE_													=  librosa.feature.inverse.mel_to_audio(
          _A  ,						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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, Any]								=			[
           "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CamembertForCausalLM",
           "CamembertForMaskedLM",
           "CamembertForMultipleChoice",
           "CamembertForQuestionAnswering",
           "CamembertForSequenceClassification",
           "CamembertForTokenClassification",
           "CamembertModel",
           "CamembertPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[Any]								=			[
           "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFCamembertForCausalLM",
           "TFCamembertForMaskedLM",
           "TFCamembertForMultipleChoice",
           "TFCamembertForQuestionAnswering",
           "TFCamembertForSequenceClassification",
           "TFCamembertForTokenClassification",
           "TFCamembertModel",
           "TFCamembertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

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


