


import copy
import os
from typing import Union

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


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "git_vision_model"


     def __init__(						self ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3 ,						__lowerCamelCase=2_2_4 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase="quick_gelu" ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0_2 ,						**__lowerCamelCase ,						)			->				Any:
            super().__init__(**__lowerCamelCase)

            _A      :       int				    = hidden_size
            _A      :       int				    = intermediate_size
            _A      :       int				    = num_hidden_layers
            _A      :       Dict				    = num_attention_heads
            _A      :       Dict				    = num_channels
            _A      :       List[Any]				    = patch_size
            _A      :       str				    = image_size
            _A      :       str				    = initializer_range
            _A      :       int				    = attention_dropout
            _A      :       Tuple				    = layer_norm_eps
            _A      :       Dict				    = hidden_act


     @classmethod
     def  _lowerCamelCase (						cls ,						__lowerCamelCase ,						**__lowerCamelCase)			->				"PretrainedConfig":
            cls._set_token_in_kwargs(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = cls.get_config_dict(__lowerCamelCase ,						**__lowerCamelCase)

            # get the vision config dict if we are loading from GITConfig
            if config_dict.get("model_type") == "git":
                   _A      :       Optional[int]				    = config_dict["vision_config"]

            if "model_type" in config_dict and hasattr(cls ,						"model_type") and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       F"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")

            return cls.from_dict(__lowerCamelCase ,						**__lowerCamelCase)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "git"


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=3_0_5_2_2 ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=6 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=1_0_2_4 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=0 ,						__lowerCamelCase="absolute" ,						__lowerCamelCase=True ,						__lowerCamelCase=False ,						__lowerCamelCase=1_0_1 ,						__lowerCamelCase=1_0_2 ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[Any]:
            super().__init__(bos_token_id=__lowerCamelCase ,						eos_token_id=__lowerCamelCase ,						pad_token_id=__lowerCamelCase ,						**__lowerCamelCase)

            if vision_config is None:
                   _A      :       List[str]				    = {}
                   logger.info("vision_config is None. initializing the GitVisionConfig with default values.")

            _A      :       str				    = GitVisionConfig(**__lowerCamelCase)
            _A      :       Tuple				    = vocab_size
            _A      :       str				    = hidden_size
            _A      :       Optional[int]				    = num_hidden_layers
            _A      :       List[str]				    = num_attention_heads
            _A      :       Optional[Any]				    = hidden_act
            _A      :       str				    = intermediate_size
            _A      :       Tuple				    = hidden_dropout_prob
            _A      :       Any				    = attention_probs_dropout_prob
            _A      :       int				    = max_position_embeddings
            _A      :       List[Any]				    = initializer_range
            _A      :       List[Any]				    = layer_norm_eps
            _A      :       Union[str, Any]				    = position_embedding_type
            _A      :       Union[str, Any]				    = use_cache
            _A      :       int				    = tie_word_embeddings
            _A      :       int				    = num_image_with_embedding

            _A      :       Optional[Any]				    = bos_token_id
            _A      :       str				    = eos_token_id


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       str				    = copy.deepcopy(self.__dict__)
            _A      :       Any				    = self.vision_config.to_dict()
            _A      :       Tuple				    = self.__class__.model_type
            return output






'''simple docstring'''







import math
from typing import Optional

import numpy as np

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


A     =logging.get_logger(__name__)

A     ={
    '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  (	__a ):
       __a				:     Union[str, Any]   				=					"""encodec"""
       def __init__(	self						:	Tuple   ,				lowercase						:	List[str]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,				lowercase						:	Any=24_000   ,				lowercase						:	str=1   ,				lowercase						:	Optional[int]=False   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	str=None   ,				lowercase						:	Tuple=128   ,				lowercase						:	Union[str, Any]=32   ,				lowercase						:	Union[str, Any]=1   ,				lowercase						:	Optional[Any]=[8, 5, 4, 2]   ,				lowercase						:	Any="weight_norm"   ,				lowercase						:	Tuple=7   ,				lowercase						:	int=7   ,				lowercase						:	Dict=3   ,				lowercase						:	List[Any]=2   ,				lowercase						:	str=True   ,				lowercase						:	List[str]="reflect"   ,				lowercase						:	List[Any]=2   ,				lowercase						:	Optional[Any]=2   ,				lowercase						:	int=1.0   ,				lowercase						:	Dict=1_024   ,				lowercase						:	str=None   ,				lowercase						:	Union[str, Any]=True   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           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__(**lowercase				)
       @property
       def     A      (	self						:	Dict				):



           '''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'''

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







       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)




