



from __future__ import annotations

import math

lowerCAmelCase__		:					str							='''2020.9.26'''
lowerCAmelCase__		:					Tuple							='''xcodz-dot, cclaus, dhruvmanila'''






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__       )    ->			tuple[float, float]:
							if not all(isinstance(a__							,       (float, int)       ) for val in locals().values()       ):
														__SCREAMING_SNAKE_CASE									= f"""Input values must either be float or int: {list(locals().values()       )}"""
														raise TypeError(a__       )
							__SCREAMING_SNAKE_CASE									= ((x * distance) / (z + distance)) * scale
							__SCREAMING_SNAKE_CASE									= ((y * distance) / (z + distance)) * scale
							return projected_x, projected_y






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__       )    ->			tuple[float, float, float]:
							if not isinstance(a__							,       a__       ):
														raise TypeError('Axis must be a str'       )
							__SCREAMING_SNAKE_CASE									= locals()
							del input_variables["axis"]
							if not all(isinstance(a__							,       (float, int)       ) for val in input_variables.values()       ):
														__SCREAMING_SNAKE_CASE									= (
														    'Input values except axis must either be float or int: '
														    f"""{list(input_variables.values()       )}"""
														)
														raise TypeError(a__       )
							__SCREAMING_SNAKE_CASE									= (angle % 3_60) / 4_50 * 1_80 / math.pi
							if axis == "z":
														__SCREAMING_SNAKE_CASE									= x * math.cos(a__       ) - y * math.sin(a__       )
														__SCREAMING_SNAKE_CASE									= y * math.cos(a__       ) + x * math.sin(a__       )
														__SCREAMING_SNAKE_CASE									= z
							elif axis == "x":
														__SCREAMING_SNAKE_CASE									= y * math.cos(a__       ) - z * math.sin(a__       )
														__SCREAMING_SNAKE_CASE									= z * math.cos(a__       ) + y * math.sin(a__       )
														__SCREAMING_SNAKE_CASE									= x
							elif axis == "y":
														__SCREAMING_SNAKE_CASE									= x * math.cos(a__       ) - z * math.sin(a__       )
														__SCREAMING_SNAKE_CASE									= z * math.cos(a__       ) + x * math.sin(a__       )
														__SCREAMING_SNAKE_CASE									= y
							else:
														raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''       )

							return new_x, new_y, new_z


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(F'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
			print(F'''{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }''')











import functools
import operator

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


lowerCAmelCase__		:					Dict							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    '''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        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''sew'''






						def __init__(   self				,							_A=32				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A=2				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=0.0				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_A=1e-5				,							_A="group"				,							_A="gelu"				,							_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,							_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,							_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,							_A=False				,							_A=128				,							_A=16				,							_A=True				,							_A=0.0_5				,							_A=10				,							_A=2				,							_A=0.0				,							_A=10				,							_A=0				,							_A="mean"				,							_A=False				,							_A=False				,							_A=256				,							_A=0				,							_A=1				,							_A=2				,							**_A				,							):







													'''simple docstring'''

													super().__init__(**_A				,							pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A		)
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= feat_extract_norm
													__SCREAMING_SNAKE_CASE									= feat_extract_activation
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= conv_bias
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embeddings
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embedding_groups
													__SCREAMING_SNAKE_CASE									= len(self.conv_dim		)
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= squeeze_factor
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= hidden_dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= activation_dropout
													__SCREAMING_SNAKE_CASE									= feat_proj_dropout
													__SCREAMING_SNAKE_CASE									= final_dropout
													__SCREAMING_SNAKE_CASE									= layerdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= 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									= apply_spec_augment
													__SCREAMING_SNAKE_CASE									= mask_time_prob
													__SCREAMING_SNAKE_CASE									= mask_time_length
													__SCREAMING_SNAKE_CASE									= mask_time_min_masks
													__SCREAMING_SNAKE_CASE									= mask_feature_prob
													__SCREAMING_SNAKE_CASE									= mask_feature_length
													__SCREAMING_SNAKE_CASE									= mask_feature_min_masks

													# ctc loss
													__SCREAMING_SNAKE_CASE									= ctc_loss_reduction
													__SCREAMING_SNAKE_CASE									= ctc_zero_infinity

													# sequence classification
													__SCREAMING_SNAKE_CASE									= use_weighted_layer_sum
													__SCREAMING_SNAKE_CASE									= classifier_proj_size






						@property
						def 	_A     (   self		):







													'''simple docstring'''

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






