import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase    :				int							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"encodec"




   def __init__(					self    :       Union[str, Any] ,			_lowercase    :       List[str]=[1.5, 3.0, 6.0, 12.0, 24.0] ,			_lowercase    :       List[str]=2_40_00 ,			_lowercase    :       Any=1 ,			_lowercase    :       Tuple=False ,			_lowercase    :       int=None ,			_lowercase    :       int=None ,			_lowercase    :       List[Any]=1_28 ,			_lowercase    :       Optional[int]=32 ,			_lowercase    :       Dict=1 ,			_lowercase    :       List[Any]=[8, 5, 4, 2] ,			_lowercase    :       Any="weight_norm" ,			_lowercase    :       Optional[Any]=7 ,			_lowercase    :       str=7 ,			_lowercase    :       str=3 ,			_lowercase    :       str=2 ,			_lowercase    :       List[str]=True ,			_lowercase    :       str="reflect" ,			_lowercase    :       int=2 ,			_lowercase    :       List[str]=2 ,			_lowercase    :       str=1.0 ,			_lowercase    :       str=10_24 ,			_lowercase    :       int=None ,			_lowercase    :       Optional[Any]=True ,			**_lowercase    :       Union[str, Any] ,			):



         """simple docstring"""






         SCREAMING_SNAKE_CASE__    							=   target_bandwidths
         SCREAMING_SNAKE_CASE__    							=   sampling_rate
         SCREAMING_SNAKE_CASE__    							=   audio_channels
         SCREAMING_SNAKE_CASE__    							=   normalize
         SCREAMING_SNAKE_CASE__    							=   chunk_length_s
         SCREAMING_SNAKE_CASE__    							=   overlap
         SCREAMING_SNAKE_CASE__    							=   hidden_size
         SCREAMING_SNAKE_CASE__    							=   num_filters
         SCREAMING_SNAKE_CASE__    							=   num_residual_layers
         SCREAMING_SNAKE_CASE__    							=   upsampling_ratios
         SCREAMING_SNAKE_CASE__    							=   norm_type
         SCREAMING_SNAKE_CASE__    							=   kernel_size
         SCREAMING_SNAKE_CASE__    							=   last_kernel_size
         SCREAMING_SNAKE_CASE__    							=   residual_kernel_size
         SCREAMING_SNAKE_CASE__    							=   dilation_growth_rate
         SCREAMING_SNAKE_CASE__    							=   use_causal_conv
         SCREAMING_SNAKE_CASE__    							=   pad_mode
         SCREAMING_SNAKE_CASE__    							=   compress
         SCREAMING_SNAKE_CASE__    							=   num_lstm_layers
         SCREAMING_SNAKE_CASE__    							=   trim_right_ratio
         SCREAMING_SNAKE_CASE__    							=   codebook_size
         SCREAMING_SNAKE_CASE__    							=   codebook_dim if codebook_dim is not None else hidden_size
         SCREAMING_SNAKE_CASE__    							=   use_conv_shortcut

         if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""   )

         super().__init__(**_lowercase   )




   @property
   def 			__a		(					self    :       str   ):



         """simple docstring"""






         if self.chunk_length_s is None:
               return None
         else:
               return int(self.chunk_length_s * self.sampling_rate   )




   @property
   def 			__a		(					self    :       Union[str, Any]   ):



         """simple docstring"""






         if self.chunk_length_s is None or self.overlap is None:
               return None
         else:
               return max(1 ,			int((1.0 - self.overlap) * self.chunk_length   )   )




   @property
   def 			__a		(					self    :       Any   ):



         """simple docstring"""






         SCREAMING_SNAKE_CASE__    							=   np.prod(self.upsampling_ratios   )
         return math.ceil(self.sampling_rate / hop_length   )




   @property
   def 			__a		(					self    :       List[str]   ):



         """simple docstring"""






         return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''InformerForPrediction''',
       '''InformerModel''',
       '''InformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase    :				Dict							     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
