

import math
from typing import Optional

import numpy as np

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


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''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       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       int														=     """encodec"""





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





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





    @property
    def       snake_case_				(      self):
         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       snake_case_				(      self):
         lowercase__    :   Any      =       np.prod(self.upsampling_ratios)
         return math.ceil(self.sampling_rate / hop_length)





    @property
    def       snake_case_				(      self):
         return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			  = {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = [
           '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''InstructBlipQFormerModel''',
           '''InstructBlipPreTrainedModel''',
           '''InstructBlipForConditionalGeneration''',
           '''InstructBlipVisionModel''',
       ]

if TYPE_CHECKING:
       from .configuration_instructblip import (
           INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           InstructBlipConfig,
           InstructBlipQFormerConfig,
           InstructBlipVisionConfig,
       )
       from .processing_instructblip import InstructBlipProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_instructblip import (
                  INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  InstructBlipForConditionalGeneration,
                  InstructBlipPreTrainedModel,
                  InstructBlipQFormerModel,
                  InstructBlipVisionModel,
              )

else:
       import sys

       snake_case_			  = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

