





import functools
import operator

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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Union[str, Any]       =       "unispeech-sat"






						def __init__(      self      :      Optional[int]		,   _A      :      List[str]=32		,   _A      :      Union[str, Any]=768		,   _A      :      List[str]=12		,   _A      :      Optional[int]=12		,   _A      :      Union[str, Any]=3072		,   _A      :      Any="gelu"		,   _A      :      Dict=0.1		,   _A      :      str=0.1		,   _A      :      List[str]=0.1		,   _A      :      int=0.0		,   _A      :      Tuple=0.0		,   _A      :      int=0.1		,   _A      :      int=0.1		,   _A      :      List[str]=0.0_2		,   _A      :      Optional[int]=1E-5		,   _A      :      Union[str, Any]="group"		,   _A      :      Union[str, Any]="gelu"		,   _A      :      str=(512, 512, 512, 512, 512, 512, 512)		,   _A      :      Tuple=(5, 2, 2, 2, 2, 2, 2)		,   _A      :      Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)		,   _A      :      Optional[int]=False		,   _A      :      List[str]=128		,   _A      :      Any=16		,   _A      :      int=False		,   _A      :      Union[str, Any]=True		,   _A      :      str=0.0_5		,   _A      :      List[str]=10		,   _A      :      Any=2		,   _A      :      Union[str, Any]=0.0		,   _A      :      Tuple=10		,   _A      :      Tuple=0		,   _A      :      Dict=320		,   _A      :      List[str]=2		,   _A      :      List[str]=0.1		,   _A      :      str=100		,   _A      :      List[str]=256		,   _A      :      Optional[int]=256		,   _A      :      Optional[int]=0.1		,   _A      :      int="mean"		,   _A      :      Union[str, Any]=False		,   _A      :      List[str]=False		,   _A      :      Any=256		,   _A      :      Tuple=(512, 512, 512, 512, 1500)		,   _A      :      Tuple=(5, 3, 3, 1, 1)		,   _A      :      Any=(1, 2, 3, 1, 1)		,   _A      :      Union[str, Any]=512		,   _A      :      Any=0		,   _A      :      str=1		,   _A      :      str=2		,   _A      :      List[Any]=504		,   **_A      :      List[str]		,   )							->    Optional[Any]:







							"""simple docstring"""


							super().__init__(**_A		,   pad_token_id=_A		,   bos_token_id=_A		,   eos_token_id=_A	)
							snake_case_		:			Tuple											=	hidden_size
							snake_case_		:			Dict											=	feat_extract_norm
							snake_case_		:			Any											=	feat_extract_activation
							snake_case_		:			Any											=	list(_A	)
							snake_case_		:			Optional[Any]											=	list(_A	)
							snake_case_		:			Any											=	list(_A	)
							snake_case_		:			Optional[Any]											=	conv_bias
							snake_case_		:			Optional[int]											=	num_conv_pos_embeddings
							snake_case_		:			int											=	num_conv_pos_embedding_groups
							snake_case_		:			Tuple											=	len(self.conv_dim	)
							snake_case_		:			Any											=	num_hidden_layers
							snake_case_		:			List[str]											=	intermediate_size
							snake_case_		:			List[str]											=	hidden_act
							snake_case_		:			List[Any]											=	num_attention_heads
							snake_case_		:			Optional[Any]											=	hidden_dropout
							snake_case_		:			List[Any]											=	attention_dropout
							snake_case_		:			List[Any]											=	activation_dropout
							snake_case_		:			Union[str, Any]											=	feat_proj_dropout
							snake_case_		:			Optional[Any]											=	final_dropout
							snake_case_		:			str											=	layerdrop
							snake_case_		:			List[str]											=	layer_norm_eps
							snake_case_		:			Optional[int]											=	initializer_range
							snake_case_		:			List[str]											=	vocab_size
							snake_case_		:			Optional[int]											=	num_clusters
							snake_case_		:			List[str]											=	do_stable_layer_norm
							snake_case_		:			Tuple											=	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
							snake_case_		:			str											=	apply_spec_augment
							snake_case_		:			str											=	mask_time_prob
							snake_case_		:			Optional[Any]											=	mask_time_length
							snake_case_		:			Dict											=	mask_time_min_masks
							snake_case_		:			Optional[Any]											=	mask_feature_prob
							snake_case_		:			str											=	mask_feature_length
							snake_case_		:			Tuple											=	mask_feature_min_masks

							# parameters for pretraining with codevector quantized representations
							snake_case_		:			int											=	num_codevectors_per_group
							snake_case_		:			Optional[Any]											=	num_codevector_groups
							snake_case_		:			Optional[Any]											=	contrastive_logits_temperature
							snake_case_		:			Any											=	feat_quantizer_dropout
							snake_case_		:			Dict											=	num_negatives
							snake_case_		:			Optional[Any]											=	codevector_dim
							snake_case_		:			Optional[Any]											=	proj_codevector_dim
							snake_case_		:			Tuple											=	diversity_loss_weight

							# ctc loss
							snake_case_		:			Union[str, Any]											=	ctc_loss_reduction
							snake_case_		:			Optional[Any]											=	ctc_zero_infinity

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							snake_case_		:			Optional[int]											=	classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							snake_case_		:			Optional[Any]											=	list(_A	)
							snake_case_		:			Optional[int]											=	list(_A	)
							snake_case_		:			Optional[Any]											=	list(_A	)
							snake_case_		:			Dict											=	xvector_output_dim






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    str:







							"""simple docstring"""


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








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

