import math
from typing import Optional

import numpy as np

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


SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Optional[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    snake_case       (   a_    ):
  lowercase_								=  '''encodec'''




  def __init__(   self  :						Union[str, Any]  ,			a_  :						Dict=[1.5, 3.0, 6.0, 12.0, 24.0]  ,			a_  :						Optional[int]=2_4000  ,			a_  :						Optional[Any]=1  ,			a_  :						Union[str, Any]=False  ,			a_  :						Any=None  ,			a_  :						Any=None  ,			a_  :						Optional[Any]=128  ,			a_  :						List[Any]=32  ,			a_  :						int=1  ,			a_  :						Union[str, Any]=[8, 5, 4, 2]  ,			a_  :						Union[str, Any]="weight_norm"  ,			a_  :						Tuple=7  ,			a_  :						int=7  ,			a_  :						int=3  ,			a_  :						Any=2  ,			a_  :						int=True  ,			a_  :						Union[str, Any]="reflect"  ,			a_  :						Optional[Any]=2  ,			a_  :						Tuple=2  ,			a_  :						Dict=1.0  ,			a_  :						Dict=1024  ,			a_  :						Any=None  ,			a_  :						Union[str, Any]=True  ,			**a_  :						Any  ,			)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		target_bandwidths
       SCREAMING_SNAKE_CASE__					:					int      =		sampling_rate
       SCREAMING_SNAKE_CASE__					:					int      =		audio_channels
       SCREAMING_SNAKE_CASE__					:					List[str]      =		normalize
       SCREAMING_SNAKE_CASE__					:					List[str]      =		chunk_length_s
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		overlap
       SCREAMING_SNAKE_CASE__					:					str      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					Dict      =		num_filters
       SCREAMING_SNAKE_CASE__					:					int      =		num_residual_layers
       SCREAMING_SNAKE_CASE__					:					int      =		upsampling_ratios
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		norm_type
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		kernel_size
       SCREAMING_SNAKE_CASE__					:					int      =		last_kernel_size
       SCREAMING_SNAKE_CASE__					:					List[str]      =		residual_kernel_size
       SCREAMING_SNAKE_CASE__					:					Any      =		dilation_growth_rate
       SCREAMING_SNAKE_CASE__					:					str      =		use_causal_conv
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pad_mode
       SCREAMING_SNAKE_CASE__					:					Any      =		compress
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		num_lstm_layers
       SCREAMING_SNAKE_CASE__					:					str      =		trim_right_ratio
       SCREAMING_SNAKE_CASE__					:					Any      =		codebook_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		codebook_dim if codebook_dim is not None else hidden_size
       SCREAMING_SNAKE_CASE__					:					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__(**lowerCAmelCase_					)




  @property
  def 						__lowercase(   self  :						Any					)->							Optional[int]:




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




  @property
  def 						__lowercase(   self  :						Optional[int]					)->							Optional[int]:




       """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 						__lowercase(   self  :						List[str]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		np.prod(self.upsampling_ratios					)
       return math.ceil(self.sampling_rate / hop_length					)




  @property
  def 						__lowercase(   self  :						List[Any]					)->							int:




       """simple docstring"""
       return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)







'''simple docstring'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
			__A=	np.exp(outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1,keepdims=_SCREAMING_SNAKE_CASE )






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

						__A=	np.argmax(lowerCAmelCase_	)
						__A=	self.model.config.idalabel[best_class]
						__A=	probabilities[best_class].item()
						__A=	logits.tolist()
						return {"label": label, "score": score, "logits": logits}



