






"""simple docstring"""



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

 _lowerCAmelCase  :  Optional[Any]   =			True
 _lowerCAmelCase  :  Union[str, Any]   =			""""""
except Exception as e:
 _lowerCAmelCase  :  int   =			False
 _lowerCAmelCase  :  Optional[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  lowerCAmelCase__ (						__magic_name__       ,	__magic_name__      ):
   SCREAMING_SNAKE_CASE_         ='''mel_config.json'''
   @register_to_config
   def __init__(		self			:  Dict       , snake_case__			:  int = 2_5_6       , snake_case__			:  int = 2_5_6       , snake_case__			:  int = 2_2_0_5_0       , snake_case__			:  int = 2_0_4_8       , snake_case__			:  int = 5_1_2       , snake_case__			:  int = 8_0       , snake_case__			:  int = 3_2       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       hop_length
        UpperCAmelCase__	:						str       =       sample_rate
        UpperCAmelCase__	:						int       =       n_fft
        UpperCAmelCase__	:						Optional[int]       =       top_db
        UpperCAmelCase__	:						List[str]       =       n_iter
        self.set_resolution(snake_case__       , snake_case__       )
        UpperCAmelCase__	:						Optional[int]       =       None

        if not _librosa_can_be_imported:
             raise ValueError(_import_error       )
   def 			__a       (		self			:  Tuple       , snake_case__			:  int       , snake_case__			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[int]       =       x_res
        UpperCAmelCase__	:						List[str]       =       y_res
        UpperCAmelCase__	:						Tuple       =       self.y_res
        UpperCAmelCase__	:						Dict       =       self.x_res * self.hop_length - 1
   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  str = None       , snake_case__			:  np.ndarray = None       ):


        '''simple docstring'''
        if audio_file is not None:
             UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       librosa.load(snake_case__       , mono=snake_case__       , sr=self.sr       )
        else:
             UpperCAmelCase__	:						Dict       =       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 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        return len(self.audio       ) // self.slice_size
   def 			__a       (		self			:  Tuple       , snake_case__			:  int = 0       ):


        '''simple docstring'''
        return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
   def 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        return self.sr
   def 			__a       (		self			:  List[str]       , snake_case__			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						int       =       librosa.feature.melspectrogram(
            y=self.get_audio_slice(snake_case__       )       , sr=self.sr       , n_fft=self.n_fft       , hop_length=self.hop_length       , n_mels=self.n_mels       )
        UpperCAmelCase__	:						int       =       librosa.power_to_db(snake_case__       , ref=np.max       , top_db=self.top_db       )
        UpperCAmelCase__	:						Tuple       =       (((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0       , 2_5_5       ) + 0.5).astype(np.uinta       )
        UpperCAmelCase__	:						str       =       Image.fromarray(snake_case__       )
        return image




   def 			__a       (		self			:  List[Any]       , snake_case__			:  Image.Image       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       np.frombuffer(image.tobytes()       , dtype="uint8"       ).reshape((image.height, image.width)       )
        UpperCAmelCase__	:						Any       =       bytedata.astype("float"       ) * self.top_db / 2_5_5 - self.top_db
        UpperCAmelCase__	:						List[str]       =       librosa.db_to_power(snake_case__       )
        UpperCAmelCase__	:						List[Any]       =       librosa.feature.inverse.mel_to_audio(
            snake_case__       , sr=self.sr       , n_fft=self.n_fft       , hop_length=self.hop_length       , n_iter=self.n_iter       )
        return audio










"""simple docstring"""



from typing import TYPE_CHECKING

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


_lowerCAmelCase  :  List[Any]   =			{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  str   =			["""MarkupLMTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  List[str]   =			[
     """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """MarkupLMForQuestionAnswering""",
     """MarkupLMForSequenceClassification""",
     """MarkupLMForTokenClassification""",
     """MarkupLMModel""",
     """MarkupLMPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 _lowerCAmelCase  :  Dict   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


