






"""simple docstring"""
from __future__ import annotations

from collections.abc import MutableSequence







class 			UpperCamelCase_       :





	def __init__(      self					: List[Any]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict						)						->			Any:

				if len(__a						) != degree + 1:
							raise ValueError(
							    "The number of coefficients should be equal to the degree + 1."						)

				UpperCAmelCase_     : list[float]			     =					list(__a						)
				UpperCAmelCase_     : Any			     =					degree





	def __add__(      self					: Dict	,	lowerCAmelCase_					: Optional[int]						)						->			Dict:

				if self.degree > polynomial_a.degree:
							UpperCAmelCase_     : Optional[Any]			     =					self.coefficients[:]
							for i in range(polynomial_a.degree + 1						):
										coefficients[i] += polynomial_a.coefficients[i]
							return Polynomial(self.degree	,	__a						)
				else:
							UpperCAmelCase_     : Union[str, Any]			     =					polynomial_a.coefficients[:]
							for i in range(self.degree + 1						):
										coefficients[i] += self.coefficients[i]
							return Polynomial(polynomial_a.degree	,	__a						)





	def __sub__(      self					: Dict	,	lowerCAmelCase_					: Dict						)						->			List[Any]:
				return self + polynomial_a * Polynomial(0	,	[-1]						)





	def __neg__(      self					: Dict						)						->			Union[str, Any]:
				return Polynomial(self.degree	,	[-c for c in self.coefficients]						)





	def __mul__(      self					: Union[str, Any]	,	lowerCAmelCase_					: Optional[Any]						)						->			List[str]:
				UpperCAmelCase_     : list[float]			     =					[0] * (self.degree + polynomial_a.degree + 1)
				for i in range(self.degree + 1						):
							for j in range(polynomial_a.degree + 1						):
										coefficients[i + j] += (
										    self.coefficients[i] * polynomial_a.coefficients[j]
										)

				return Polynomial(self.degree + polynomial_a.degree	,	__a						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: Optional[Any]						)						->			Optional[int]:
				UpperCAmelCase_     : int | float			     =					0
				for i in range(self.degree + 1						):
							result += self.coefficients[i] * (substitution**i)
				return result





	def __str__(      self					: List[str]						)						->			Optional[int]:
				UpperCAmelCase_     : List[Any]			     =					""
				for i in range(self.degree	,	-1	,	-1						):
							if self.coefficients[i] == 0:
										continue
							elif self.coefficients[i] > 0:
										if polynomial:
													polynomial += " + "
							else:
										polynomial += " - "

							if i == 0:
										polynomial += str(abs(self.coefficients[i]						)						)
							elif i == 1:
										polynomial += str(abs(self.coefficients[i]						)						) + "x"
							else:
										polynomial += str(abs(self.coefficients[i]						)						) + "x^" + str(__a						)

				return polynomial





	def __repr__(      self					: Tuple						)						->			Union[str, Any]:
				return self.__str__()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				UpperCAmelCase_     : list[float]			     =					[0] * self.degree
				for i in range(self.degree						):
							UpperCAmelCase_     : Optional[Any]			     =					self.coefficients[i + 1] * (i + 1)
				return Polynomial(self.degree - 1	,	__a						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Any = 0						)						->			List[Any]:
				UpperCAmelCase_     : list[float]			     =					[0] * (self.degree + 2)
				UpperCAmelCase_     : Dict			     =					constant
				for i in range(self.degree + 1						):
							UpperCAmelCase_     : Tuple			     =					self.coefficients[i] / (i + 1)
				return Polynomial(self.degree + 1	,	__a						)





	def __eq__(      self					: int	,	lowerCAmelCase_					: Tuple						)						->			Optional[Any]:

				if not isinstance(__a	,	__a						):
							return False

				if self.degree != polynomial_a.degree:
							return False

				for i in range(self.degree + 1						):
							if self.coefficients[i] != polynomial_a.coefficients[i]:
										return False

				return True





	def __ne__(      self					: Optional[Any]	,	lowerCAmelCase_					: List[Any]						)						->			Tuple:
				return not self.__eq__(__a						)








import functools
import operator

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


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'wav2vec2'







							def __init__(   self,			__a=32,			__a=768,			__a=12,			__a=12,			__a=3072,			__a="gelu",			__a=0.1,			__a=0.1,			__a=0.1,			__a=0.0,			__a=0.0,			__a=0.1,			__a=0.1,			__a=0.02,			__a=1E-5,			__a="group",			__a="gelu",			__a=(512, 512, 512, 512, 512, 512, 512),			__a=(5, 2, 2, 2, 2, 2, 2),			__a=(10, 3, 3, 3, 3, 2, 2),			__a=False,			__a=128,			__a=16,			__a=False,			__a=True,			__a=0.05,			__a=10,			__a=2,			__a=0.0,			__a=10,			__a=0,			__a=320,			__a=2,			__a=0.1,			__a=100,			__a=256,			__a=256,			__a=0.1,			__a="sum",			__a=False,			__a=False,			__a=256,			__a=(512, 512, 512, 512, 1500),			__a=(5, 3, 3, 1, 1),			__a=(1, 2, 3, 1, 1),			__a=512,			__a=0,			__a=1,			__a=2,			__a=False,			__a=3,			__a=2,			__a=3,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a,			pad_token_id=__a,			bos_token_id=__a,			eos_token_id=__a)
														_lowerCAmelCase     :							str    =    hidden_size
														_lowerCAmelCase     :							Optional[int]    =    feat_extract_norm
														_lowerCAmelCase     :							Union[str, Any]    =    feat_extract_activation
														_lowerCAmelCase     :							Optional[Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    conv_bias
														_lowerCAmelCase     :							str    =    num_conv_pos_embeddings
														_lowerCAmelCase     :							List[Any]    =    num_conv_pos_embedding_groups
														_lowerCAmelCase     :							str    =    len(self.conv_dim)
														_lowerCAmelCase     :							List[str]    =    num_hidden_layers
														_lowerCAmelCase     :							str    =    intermediate_size
														_lowerCAmelCase     :							Any    =    hidden_act
														_lowerCAmelCase     :							int    =    num_attention_heads
														_lowerCAmelCase     :							Optional[Any]    =    hidden_dropout
														_lowerCAmelCase     :							List[str]    =    attention_dropout
														_lowerCAmelCase     :							Tuple    =    activation_dropout
														_lowerCAmelCase     :							int    =    feat_proj_dropout
														_lowerCAmelCase     :							List[str]    =    final_dropout
														_lowerCAmelCase     :							int    =    layerdrop
														_lowerCAmelCase     :							int    =    layer_norm_eps
														_lowerCAmelCase     :							Union[str, Any]    =    initializer_range
														_lowerCAmelCase     :							str    =    vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    do_stable_layer_norm
														_lowerCAmelCase     :							Any    =    use_weighted_layer_sum

														if (
														    (len(self.conv_stride) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel) != self.num_feat_extract_layers)
														    or (len(self.conv_dim) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																					    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																					    f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
																					    f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														_lowerCAmelCase     :							str    =    apply_spec_augment
														_lowerCAmelCase     :							Optional[Any]    =    mask_time_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_time_length
														_lowerCAmelCase     :							List[str]    =    mask_time_min_masks
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_length
														_lowerCAmelCase     :							List[str]    =    mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														_lowerCAmelCase     :							Union[str, Any]    =    num_codevectors_per_group
														_lowerCAmelCase     :							str    =    num_codevector_groups
														_lowerCAmelCase     :							Optional[int]    =    contrastive_logits_temperature
														_lowerCAmelCase     :							Optional[int]    =    feat_quantizer_dropout
														_lowerCAmelCase     :							Optional[int]    =    num_negatives
														_lowerCAmelCase     :							Union[str, Any]    =    codevector_dim
														_lowerCAmelCase     :							Any    =    proj_codevector_dim
														_lowerCAmelCase     :							Optional[int]    =    diversity_loss_weight

														# ctc loss
														_lowerCAmelCase     :							Tuple    =    ctc_loss_reduction
														_lowerCAmelCase     :							Tuple    =    ctc_zero_infinity

														# adapter
														_lowerCAmelCase     :							List[Any]    =    add_adapter
														_lowerCAmelCase     :							List[str]    =    adapter_kernel_size
														_lowerCAmelCase     :							str    =    adapter_stride
														_lowerCAmelCase     :							List[str]    =    num_adapter_layers
														_lowerCAmelCase     :							str    =    output_hidden_size or hidden_size
														_lowerCAmelCase     :							Tuple    =    adapter_attn_dim

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							Tuple    =    xvector_output_dim







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return functools.reduce(operator.mul,			self.conv_stride,			1)
