
"""simple docstring"""





import functools
import operator

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


UpperCamelCase_					:						List[Any]				= logging.get_logger(__name__)

UpperCamelCase_					:						Any				= {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}



class __lowerCAmelCase				(					__A      ):




   """simple docstring"""







   snake_case	      =				"unispeech"




   def __init__(				self						:					int       ,   _snake_case						:					Optional[Any]=32       ,   _snake_case						:					List[str]=768       ,   _snake_case						:					Union[str, Any]=12       ,   _snake_case						:					List[str]=12       ,   _snake_case						:					str=3_072       ,   _snake_case						:					Tuple="gelu"       ,   _snake_case						:					Union[str, Any]=0.1       ,   _snake_case						:					Dict=0.1       ,   _snake_case						:					Dict=0.1       ,   _snake_case						:					int=0.0       ,   _snake_case						:					List[Any]=0.0       ,   _snake_case						:					Optional[int]=0.1       ,   _snake_case						:					List[str]=0.1       ,   _snake_case						:					List[str]=0.0_2       ,   _snake_case						:					Any=1e-5       ,   _snake_case						:					Tuple="group"       ,   _snake_case						:					Optional[Any]="gelu"       ,   _snake_case						:					Optional[Any]=(512, 512, 512, 512, 512, 512, 512)       ,   _snake_case						:					Dict=(5, 2, 2, 2, 2, 2, 2)       ,   _snake_case						:					Optional[Any]=(10, 3, 3, 3, 3, 2, 2)       ,   _snake_case						:					Optional[int]=False       ,   _snake_case						:					Dict=128       ,   _snake_case						:					Optional[int]=16       ,   _snake_case						:					Optional[Any]=False       ,   _snake_case						:					List[str]=True       ,   _snake_case						:					Tuple=0.0_5       ,   _snake_case						:					Any=10       ,   _snake_case						:					List[str]=2       ,   _snake_case						:					Optional[int]=0.0       ,   _snake_case						:					Tuple=10       ,   _snake_case						:					Optional[int]=0       ,   _snake_case						:					str=320       ,   _snake_case						:					str=2       ,   _snake_case						:					List[Any]=0.1       ,   _snake_case						:					Optional[int]=100       ,   _snake_case						:					int=256       ,   _snake_case						:					Optional[Any]=256       ,   _snake_case						:					int=0.1       ,   _snake_case						:					Optional[Any]="mean"       ,   _snake_case						:					Tuple=False       ,   _snake_case						:					List[str]=False       ,   _snake_case						:					Union[str, Any]=256       ,   _snake_case						:					int=80       ,   _snake_case						:					List[str]=0       ,   _snake_case						:					Union[str, Any]=1       ,   _snake_case						:					Dict=2       ,   _snake_case						:					Dict=0.5       ,   **_snake_case						:					List[str]       ,   )      -> int:







        """simple docstring"""
        super().__init__(**_snake_case       ,   pad_token_id=_snake_case       ,   bos_token_id=_snake_case       ,   eos_token_id=_snake_case	)
        A_				 =						hidden_size
        A_				 =						feat_extract_norm
        A_				 =						feat_extract_activation
        A_				 =						list(_snake_case	)
        A_				 =						list(_snake_case	)
        A_				 =						list(_snake_case	)
        A_				 =						conv_bias
        A_				 =						num_conv_pos_embeddings
        A_				 =						num_conv_pos_embedding_groups
        A_				 =						len(self.conv_dim	)
        A_				 =						num_hidden_layers
        A_				 =						intermediate_size
        A_				 =						hidden_act
        A_				 =						num_attention_heads
        A_				 =						hidden_dropout
        A_				 =						attention_dropout
        A_				 =						activation_dropout
        A_				 =						feat_proj_dropout
        A_				 =						final_dropout
        A_				 =						layerdrop
        A_				 =						layer_norm_eps
        A_				 =						initializer_range
        A_				 =						num_ctc_classes
        A_				 =						vocab_size
        A_				 =						do_stable_layer_norm
        A_				 =						use_weighted_layer_sum
        A_				 =						classifier_proj_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)`, 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
        A_				 =						apply_spec_augment
        A_				 =						mask_time_prob
        A_				 =						mask_time_length
        A_				 =						mask_time_min_masks
        A_				 =						mask_feature_prob
        A_				 =						mask_feature_length
        A_				 =						mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        A_				 =						num_codevectors_per_group
        A_				 =						num_codevector_groups
        A_				 =						contrastive_logits_temperature
        A_				 =						feat_quantizer_dropout
        A_				 =						num_negatives
        A_				 =						codevector_dim
        A_				 =						proj_codevector_dim
        A_				 =						diversity_loss_weight

        # ctc loss
        A_				 =						ctc_loss_reduction
        A_				 =						ctc_zero_infinity

        # pretraining loss
        A_				 =						replace_prob






   @property
   def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







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






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						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) = }""")




