






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

UpperCAmelCase__						:List[str]          =			typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
UpperCAmelCase__						:Optional[int]          =			typing.Union[np.floataa, int, float]  # noqa: UP007
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return np.sqrt(np.sum((np.asarray(_lowercase     ) - np.asarray(_lowercase     )) ** 2     )     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return sum((va - va) ** 2 for va, va in zip(_lowercase,						_lowercase     )     ) ** (1 / 2)


if __name__ == "__main__":
   def     __lowercase  ()   ->     None:


        """simple docstring"""
        from timeit import timeit

        print("""Without Numpy"""     )
        print(
            timeit(
                """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
        print("""With Numpy"""     )
        print(
            timeit(
                """euclidean_distance([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
   benchmark()











'''simple docstring'''

import functools
import operator

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


UpperCAmelCase__						:Optional[int]          =			logging.get_logger(__name__)

UpperCAmelCase__						:List[Any]          =			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Dict   		=			'sew-d'




       def __init__(					self      : Tuple	,    A__      : Optional[int]=32	,    A__      : Optional[int]=768	,    A__      : Any=12	,    A__      : List[str]=12	,    A__      : List[Any]=3072	,    A__      : str=2	,    A__      : Dict=512	,    A__      : Optional[Any]=256	,    A__      : Optional[Any]=True	,    A__      : Any=True	,    A__      : List[str]=("p2c", "c2p")	,    A__      : List[Any]="layer_norm"	,    A__      : Union[str, Any]="gelu_python"	,    A__      : int=0.1	,    A__      : Dict=0.1	,    A__      : int=0.1	,    A__      : Dict=0.0	,    A__      : Optional[Any]=0.1	,    A__      : Dict=0.02	,    A__      : Dict=1e-7	,    A__      : List[Any]=1e-5	,    A__      : Any="group"	,    A__      : Any="gelu"	,    A__      : Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)	,    A__      : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,    A__      : str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,    A__      : Optional[Any]=False	,    A__      : Any=128	,    A__      : Optional[Any]=16	,    A__      : Union[str, Any]=True	,    A__      : Any=0.05	,    A__      : List[str]=10	,    A__      : Union[str, Any]=2	,    A__      : Dict=0.0	,    A__      : str=10	,    A__      : Tuple=0	,    A__      : Any="mean"	,    A__      : Optional[int]=False	,    A__      : int=False	,    A__      : List[str]=256	,    A__      : Union[str, Any]=0	,    A__      : int=1	,    A__      : Optional[Any]=2	,    **A__      : List[Any]	,    ):


            """simple docstring"""







            super().__init__(**A__	,    pad_token_id=A__	,    bos_token_id=A__	,    eos_token_id=A__					)
            __lowerCamelCase						:       List[str]				   =				hidden_size
            __lowerCamelCase						:       Tuple				   =				feat_extract_norm
            __lowerCamelCase						:       Tuple				   =				feat_extract_activation
            __lowerCamelCase						:       List[Any]				   =				list(A__					)
            __lowerCamelCase						:       int				   =				list(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				list(A__					)
            __lowerCamelCase						:       Tuple				   =				conv_bias
            __lowerCamelCase						:       List[str]				   =				num_conv_pos_embeddings
            __lowerCamelCase						:       Tuple				   =				num_conv_pos_embedding_groups
            __lowerCamelCase						:       int				   =				len(self.conv_dim					)
            __lowerCamelCase						:       Optional[int]				   =				num_hidden_layers
            __lowerCamelCase						:       Dict				   =				intermediate_size
            __lowerCamelCase						:       Optional[int]				   =				squeeze_factor
            __lowerCamelCase						:       Union[str, Any]				   =				max_position_embeddings
            __lowerCamelCase						:       int				   =				position_buckets
            __lowerCamelCase						:       Tuple				   =				share_att_key
            __lowerCamelCase						:       Any				   =				relative_attention
            __lowerCamelCase						:       Any				   =				norm_rel_ebd
            __lowerCamelCase						:       Dict				   =				list(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				hidden_act
            __lowerCamelCase						:       List[str]				   =				num_attention_heads
            __lowerCamelCase						:       List[str]				   =				hidden_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				attention_dropout
            __lowerCamelCase						:       Tuple				   =				activation_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				feat_proj_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				final_dropout
            __lowerCamelCase						:       List[str]				   =				layer_norm_eps
            __lowerCamelCase						:       Tuple				   =				feature_layer_norm_eps
            __lowerCamelCase						:       Union[str, Any]				   =				initializer_range
            __lowerCamelCase						:       List[str]				   =				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
            __lowerCamelCase						:       Any				   =				apply_spec_augment
            __lowerCamelCase						:       str				   =				mask_time_prob
            __lowerCamelCase						:       Tuple				   =				mask_time_length
            __lowerCamelCase						:       Optional[int]				   =				mask_time_min_masks
            __lowerCamelCase						:       Dict				   =				mask_feature_prob
            __lowerCamelCase						:       Optional[Any]				   =				mask_feature_length
            __lowerCamelCase						:       str				   =				mask_feature_min_masks

            # ctc loss
            __lowerCamelCase						:       Any				   =				ctc_loss_reduction
            __lowerCamelCase						:       str				   =				ctc_zero_infinity

            # sequence classification
            __lowerCamelCase						:       Dict				   =				use_weighted_layer_sum
            __lowerCamelCase						:       List[Any]				   =				classifier_proj_size




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







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



