"""simple docstring"""







import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_SCREAMING_SNAKE_CASE	:		str   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   {
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    "vit-hybrid"







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=768 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=3072 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=1E-1_2 ,				_SCREAMING_SNAKE_CASE=224 ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=[1, 1024, 24, 24] ,				_SCREAMING_SNAKE_CASE=True ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      super().__init__(**_SCREAMING_SNAKE_CASE	)

      if backbone_config is None:
         logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'	)
         __lowerCamelCase    		=					{
             'global_padding': 'same',
             'layer_type': 'bottleneck',
             'depths': [3, 4, 9],
             'out_features': ['stage3'],
             'embedding_dynamic_padding': True,
         }

      if isinstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
         if "model_type" in backbone_config:
            __lowerCamelCase    		=					CONFIG_MAPPING[backbone_config['model_type']]
         else:
            logger.info(
                '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'	)
            __lowerCamelCase    		=					BitConfig
         __lowerCamelCase    		=					backbone_config_class(**_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					backbone_featmap_shape
      __lowerCamelCase    		=					backbone_config
      __lowerCamelCase    		=					hidden_size
      __lowerCamelCase    		=					num_hidden_layers
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					intermediate_size
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					layer_norm_eps
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					patch_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					qkv_bias







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					copy.deepcopy(self.__dict__	)
      __lowerCamelCase    		=					self.backbone_config.to_dict()
      __lowerCamelCase    		=					self.__class__.model_type
      return output










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

