"""simple docstring"""


import math
from typing import Optional

import numpy as np

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


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """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 				a (						__snake_case      ):
    _snake_case   :     int   							=							"""encodec"""


    def __init__(    self		:     str      , __lowerCAmelCase		:     Any=[1.5, 3.0, 6.0, 12.0, 24.0]      , __lowerCAmelCase		:     Tuple=2_4000      , __lowerCAmelCase		:     Optional[int]=1      , __lowerCAmelCase		:     List[Any]=False      , __lowerCAmelCase		:     List[Any]=None      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=128      , __lowerCAmelCase		:     Dict=32      , __lowerCAmelCase		:     str=1      , __lowerCAmelCase		:     int=[8, 5, 4, 2]      , __lowerCAmelCase		:     Tuple="weight_norm"      , __lowerCAmelCase		:     Tuple=7      , __lowerCAmelCase		:     Union[str, Any]=7      , __lowerCAmelCase		:     int=3      , __lowerCAmelCase		:     str=2      , __lowerCAmelCase		:     str=True      , __lowerCAmelCase		:     Dict="reflect"      , __lowerCAmelCase		:     Union[str, Any]=2      , __lowerCAmelCase		:     str=2      , __lowerCAmelCase		:     Dict=1.0      , __lowerCAmelCase		:     Dict=1024      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Dict=True      , **__lowerCAmelCase		:     List[Any]      , ):
      _UpperCAmelCase         =					target_bandwidths
      _UpperCAmelCase         =					sampling_rate
      _UpperCAmelCase         =					audio_channels
      _UpperCAmelCase         =					normalize
      _UpperCAmelCase         =					chunk_length_s
      _UpperCAmelCase         =					overlap
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_filters
      _UpperCAmelCase         =					num_residual_layers
      _UpperCAmelCase         =					upsampling_ratios
      _UpperCAmelCase         =					norm_type
      _UpperCAmelCase         =					kernel_size
      _UpperCAmelCase         =					last_kernel_size
      _UpperCAmelCase         =					residual_kernel_size
      _UpperCAmelCase         =					dilation_growth_rate
      _UpperCAmelCase         =					use_causal_conv
      _UpperCAmelCase         =					pad_mode
      _UpperCAmelCase         =					compress
      _UpperCAmelCase         =					num_lstm_layers
      _UpperCAmelCase         =					trim_right_ratio
      _UpperCAmelCase         =					codebook_size
      _UpperCAmelCase         =					codebook_dim if codebook_dim is not None else hidden_size
      _UpperCAmelCase         =					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__(**__lowerCAmelCase    )


    @property
    def    lowerCAmelCase_     (    self		:     str    ):
      if self.chunk_length_s is None:
        return None
      else:
        return int(self.chunk_length_s * self.sampling_rate    )


    @property
    def    lowerCAmelCase_     (    self		:     str    ):
      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		:     Optional[Any]    ):
      _UpperCAmelCase         =					np.prod(self.upsampling_ratios    )
      return math.ceil(self.sampling_rate / hop_length    )


    @property
    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )



import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use FlavaImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

