

'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    List[Any]							     =     ['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE       :    int							     =     'CLIPImageProcessor'
  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')






  def __init__( self  :	int   ,lowercase__  :	int=None   ,lowercase__  :	List[str]=None   ,**lowercase__  :	Tuple     ):
   __lowercase				    =					None
   if "feature_extractor" in kwargs:
    warnings.warn(
        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
        ''' instead.'''   ,lowercase__   ,)
    __lowercase				    =					kwargs.pop('''feature_extractor'''     )

   __lowercase				    =					image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError('''You need to specify an `image_processor`.'''     )
   if tokenizer is None:
    raise ValueError('''You need to specify a `tokenizer`.'''     )

   super().__init__(lowercase__   ,lowercase__     )






  def __call__( self  :	List[str]   ,lowercase__  :	List[Any]=None   ,lowercase__  :	Tuple=None   ,lowercase__  :	List[str]=None   ,**lowercase__  :	Union[str, Any]     ):

   if text is None and images is None:
    raise ValueError('''You have to specify either text or images. Both cannot be none.'''     )

   if text is not None:
    __lowercase				    =					self.tokenizer(lowercase__   ,return_tensors=lowercase__   ,**lowercase__     )

   if images is not None:
    __lowercase				    =					self.image_processor(lowercase__   ,return_tensors=lowercase__   ,**lowercase__     )

   if text is not None and images is not None:
    __lowercase				    =					image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__     )   ,tensor_type=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,*lowercase__  :	Dict   ,**lowercase__  :	Optional[int]     ):
   return self.tokenizer.batch_decode(*lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,*lowercase__  :	List[str]   ,**lowercase__  :	Dict     ):
   return self.tokenizer.decode(*lowercase__   ,**lowercase__     )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.tokenizer.model_input_names
   __lowercase				    =					self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


import math
from typing import Optional

import numpy as np

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "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        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __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=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		codebook_dim if codebook_dim is not None else hidden_size
												UpperCamelCase_		:							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__(**__UpperCamelCase						)





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





						@property
						def        A_							(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        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






