"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowerCAmelCase_     =	TypeVar('''T''')




class  _snake_case		(    Generic[T] ):







				"""simple docstring"""






				def __init__(      self							:							List[Any]      ,    _A							:							list[T]      ,    _A							:							Callable[[T, T], T]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any | T			  =    None

											_SCREAMING_SNAKE_CASE		:						int			  =    len(_A)
											_SCREAMING_SNAKE_CASE		:						list[T]			  =    [any_type for _ in range(self.N)] + arr
											_SCREAMING_SNAKE_CASE		:						Dict			  =    fnc
											self.build()





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											for p in range(self.N - 1      ,    0      ,    -1):
																		_SCREAMING_SNAKE_CASE		:						str			  =    self.fn(self.st[p * 2]      ,    self.st[p * 2 + 1])





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							int      ,    _A							:							T):



											"""simple docstring"""







											p += self.N
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    v
											while p > 1:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    p // 2
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.fn(self.st[p * 2]      ,    self.st[p * 2 + 1])





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int      ,    _A							:							int):  # noqa: E741



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    l + self.N, r + self.N

											_SCREAMING_SNAKE_CASE		:						T | None			  =    None
											while l <= r:
																		if l % 2 == 1:
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.st[l] if res is None else self.fn(_A      ,    self.st[l])
																		if r % 2 == 0:
																									_SCREAMING_SNAKE_CASE		:						str			  =    self.st[r] if res is None else self.fn(_A      ,    self.st[r])
																		_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    (l + 1) // 2, (r - 1) // 2
											return res


if __name__ == "__main__":
			from functools import reduce

			lowerCAmelCase_     =	[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

			lowerCAmelCase_     =	{
			    0: 7,
			    1: 2,
			    2: 6,
			    3: -14,
			    4: 5,
			    5: 4,
			    6: 7,
			    7: -10,
			    8: 9,
			    9: 10,
			    10: 12,
			    11: 1,
			}

			lowerCAmelCase_     =	SegmentTree(test_array, min)
			lowerCAmelCase_     =	SegmentTree(test_array, max)
			lowerCAmelCase_     =	SegmentTree(test_array, lambda a, b: a + b)



			def      lowerCamelCase_()->    None:
										for i in range(len(__SCREAMING_SNAKE_CASE  )  ):
																	for j in range(__SCREAMING_SNAKE_CASE     ,	len(__SCREAMING_SNAKE_CASE  )  ):
																								_SCREAMING_SNAKE_CASE		:						List[str]			  =    reduce(__SCREAMING_SNAKE_CASE     ,	test_array[i : j + 1]  )
																								_SCREAMING_SNAKE_CASE		:						int			  =    reduce(__SCREAMING_SNAKE_CASE     ,	test_array[i : j + 1]  )
																								_SCREAMING_SNAKE_CASE		:						int			  =    reduce(lambda __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  : a + b     ,	test_array[i : j + 1]  )
																								assert min_range == min_segment_tree.query(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																								assert max_range == max_segment_tree.query(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																								assert sum_range == sum_segment_tree.query(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

			test_all_segments()

			for index, value in test_updates.items():
						lowerCAmelCase_     =	value
						min_segment_tree.update(index, value)
						max_segment_tree.update(index, value)
						sum_segment_tree.update(index, value)
						test_all_segments()



"""simple docstring"""





import functools
import operator

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


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"sew"





				def __init__(      self							:							List[Any]      ,    _A							:							Tuple=3_2      ,    _A							:							str=7_6_8      ,    _A							:							Dict=1_2      ,    _A							:							Tuple=1_2      ,    _A							:							Optional[Any]=3_0_7_2      ,    _A							:							List[str]=2      ,    _A							:							Dict="gelu"      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.0      ,    _A							:							str=0.1      ,    _A							:							Tuple=0.1      ,    _A							:							Optional[int]=0.02      ,    _A							:							Dict=1e-5      ,    _A							:							str="group"      ,    _A							:							Tuple="gelu"      ,    _A							:							Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,    _A							:							Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,    _A							:							Any=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,    _A							:							Tuple=False      ,    _A							:							Tuple=1_2_8      ,    _A							:							int=1_6      ,    _A							:							Union[str, Any]=True      ,    _A							:							Optional[Any]=0.05      ,    _A							:							List[Any]=1_0      ,    _A							:							Union[str, Any]=2      ,    _A							:							Tuple=0.0      ,    _A							:							Union[str, Any]=1_0      ,    _A							:							Optional[int]=0      ,    _A							:							Union[str, Any]="mean"      ,    _A							:							Optional[int]=False      ,    _A							:							List[Any]=False      ,    _A							:							int=2_5_6      ,    _A							:							str=0      ,    _A							:							Optional[int]=1      ,    _A							:							List[Any]=2      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







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

											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)`,"""
																		    f"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
																		    f"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

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

											# ctc loss
											_SCREAMING_SNAKE_CASE		:						int			  =    ctc_loss_reduction
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ctc_zero_infinity

											# sequence classification
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_weighted_layer_sum
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    classifier_proj_size





				@property
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







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


