
import math
from typing import Optional

import numpy as np

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


A__      :	List[str]          =    logging.get_logger(__name__)

A__      :	int          =    {
    """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   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'encodec'



     def __init__(      self ,			__UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0] ,			__UpperCamelCase=2_40_00 ,			__UpperCamelCase=1 ,			__UpperCamelCase=False ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=1_28 ,			__UpperCamelCase=32 ,			__UpperCamelCase=1 ,			__UpperCamelCase=[8, 5, 4, 2] ,			__UpperCamelCase="weight_norm" ,			__UpperCamelCase=7 ,			__UpperCamelCase=7 ,			__UpperCamelCase=3 ,			__UpperCamelCase=2 ,			__UpperCamelCase=True ,			__UpperCamelCase="reflect" ,			__UpperCamelCase=2 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1.0 ,			__UpperCamelCase=10_24 ,			__UpperCamelCase=None ,			__UpperCamelCase=True ,			**__UpperCamelCase ,			)->					str:
          UpperCAmelCase__							: Any 							=						target_bandwidths
          UpperCAmelCase__							: Tuple 							=						sampling_rate
          UpperCAmelCase__							: Union[str, Any] 							=						audio_channels
          UpperCAmelCase__							: List[Any] 							=						normalize
          UpperCAmelCase__							: Optional[Any] 							=						chunk_length_s
          UpperCAmelCase__							: int 							=						overlap
          UpperCAmelCase__							: Union[str, Any] 							=						hidden_size
          UpperCAmelCase__							: Dict 							=						num_filters
          UpperCAmelCase__							: Any 							=						num_residual_layers
          UpperCAmelCase__							: Tuple 							=						upsampling_ratios
          UpperCAmelCase__							: Dict 							=						norm_type
          UpperCAmelCase__							: Optional[int] 							=						kernel_size
          UpperCAmelCase__							: Optional[int] 							=						last_kernel_size
          UpperCAmelCase__							: str 							=						residual_kernel_size
          UpperCAmelCase__							: Union[str, Any] 							=						dilation_growth_rate
          UpperCAmelCase__							: str 							=						use_causal_conv
          UpperCAmelCase__							: Optional[Any] 							=						pad_mode
          UpperCAmelCase__							: Any 							=						compress
          UpperCAmelCase__							: Any 							=						num_lstm_layers
          UpperCAmelCase__							: Any 							=						trim_right_ratio
          UpperCAmelCase__							: List[str] 							=						codebook_size
          UpperCAmelCase__							: Optional[int] 							=						codebook_dim if codebook_dim is not None else hidden_size
          UpperCAmelCase__							: Optional[Any] 							=						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__(**__UpperCamelCase  )



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate  )



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          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  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Union[str, Any] 							=						np.prod(self.upsampling_ratios  )
          return math.ceil(self.sampling_rate / hop_length  )



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)  )





"""simple docstring"""




from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							''
     _A								=							'hf-legacy'  # "hf://"" is reserved for hffs



     def __init__(      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					str:
          super().__init__(self ,			**__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						repo_info
          UpperCAmelCase__							: Optional[int] 							=						token
          UpperCAmelCase__							: Optional[Any] 							=						None



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          if self.dir_cache is None:
               UpperCAmelCase__							: str 							=						{}
               for hf_file in self.repo_info.siblings:
                    # TODO(QL): add sizes
                    UpperCAmelCase__							: str 							=						{
                        "name": hf_file.rfilename,
                        "size": None,
                        "type": "file",
                    }
                    self.dir_cache.update(
                        {
                            str(__UpperCamelCase  ): {"name": str(__UpperCamelCase  ), "size": None, "type": "directory"}
                            for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
                        }  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = "rb" ,			**__UpperCamelCase ,			)->					List[Any]:
          if not isinstance(self.repo_info ,			__UpperCamelCase  ):
               raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"  )
          UpperCAmelCase__							: Union[str, Any] 							=						hf_hub_url(self.repo_info.id ,			__UpperCamelCase ,			revision=self.repo_info.sha  )
          return fsspec.open(
              __UpperCamelCase ,			mode=__UpperCamelCase ,			headers=get_authentication_headers_for_url(__UpperCamelCase ,			use_auth_token=self.token  ) ,			client_kwargs={"trust_env": True} ,			).open()



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			**__UpperCamelCase  )->					List[str]:
          self._get_dirs()
          UpperCAmelCase__							: Union[str, Any] 							=						self._strip_protocol(__UpperCamelCase  )
          if path in self.dir_cache:
               return self.dir_cache[path]
          else:
               raise FileNotFoundError(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False ,			**__UpperCamelCase  )->					str:
          self._get_dirs()
          UpperCAmelCase__							: str 							=						PurePosixPath(path.strip("/"  )  )
          UpperCAmelCase__							: Optional[Any] 							=						{}
          for p, f in self.dir_cache.items():
               UpperCAmelCase__							: Optional[int] 							=						PurePosixPath(p.strip("/"  )  )
               UpperCAmelCase__							: Dict 							=						p.parent
               if root == path:
                    UpperCAmelCase__							: Tuple 							=						f
          UpperCAmelCase__							: List[Any] 							=						list(paths.values()  )
          if detail:
               return out
          else:
               return sorted(f["name"] for f in out  )



