
"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Dict						   =		'''xlm-roberta-xl'''




		def __init__(     self							,  lowerCAmelCase__=2_5_0_8_8_0							,  lowerCAmelCase__=2_5_6_0							,  lowerCAmelCase__=3_6							,  lowerCAmelCase__=3_2							,  lowerCAmelCase__=1_0_2_4_0							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_4							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1E-05							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  lowerCAmelCase__=2							,  lowerCAmelCase__="absolute"							,  lowerCAmelCase__=True							,  lowerCAmelCase__=None							,  **lowerCAmelCase__							,  ):
				super().__init__(pad_token_id=lowerCAmelCase__							,  bos_token_id=lowerCAmelCase__							,  eos_token_id=lowerCAmelCase__							,  **lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = type_vocab_size
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = position_embedding_type
				__SCREAMING_SNAKE_CASE            = use_cache
				__SCREAMING_SNAKE_CASE            = classifier_dropout



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		@property
		def 	snake_case_     (     self):
				if self.task == "multiple-choice":
						__SCREAMING_SNAKE_CASE            = {0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
						__SCREAMING_SNAKE_CASE            = {0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ])










'''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)				)




