




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	float:
		return math.sqrt(sum(pow(a - b    ,						2 ) for a, b in zip(lowerCAmelCase    ,						lowerCAmelCase ) ) )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				SCREAMING_SNAKE_CASE_					      =							(
				    'Wrong input data\'s dimensions... '
				    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
				)
				raise ValueError(lowerCAmelCase )

		try:
				if dataset.shape[1] != value_array.shape[1]:
						SCREAMING_SNAKE_CASE_					      =							(
						    'Wrong input data\'s shape... '
						    F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
						)
						raise ValueError(lowerCAmelCase )
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape' )

		if dataset.dtype != value_array.dtype:
				SCREAMING_SNAKE_CASE_					      =							(
				    'Input data have different datatype... '
				    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
				)
				raise TypeError(lowerCAmelCase )

		SCREAMING_SNAKE_CASE_					      =							[]

		for value in value_array:
				SCREAMING_SNAKE_CASE_					      =							euclidean(lowerCAmelCase    ,						dataset[0] )
				SCREAMING_SNAKE_CASE_					      =							dataset[0].tolist()

				for dataset_value in dataset[1:]:
						SCREAMING_SNAKE_CASE_					      =							euclidean(lowerCAmelCase    ,						lowerCAmelCase )

						if dist > temp_dist:
								SCREAMING_SNAKE_CASE_					      =							temp_dist
								SCREAMING_SNAKE_CASE_					      =							dataset_value.tolist()

				answer.append([vector, dist] )

		return answer


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	float:
		return np.dot(lowerCAmelCase    ,						lowerCAmelCase ) / (norm(lowerCAmelCase ) * norm(lowerCAmelCase ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


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

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="umt5"
						lowerCAmelCase__			      =["past_key_values"]



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[str, Any]=25_01_12					,							snake_case__	:   Optional[int]=5_12					,							snake_case__	:   Optional[Any]=64					,							snake_case__	:   str=10_24					,							snake_case__	:   Dict=8					,							snake_case__	:   Union[str, Any]=None					,							snake_case__	:   List[Any]=6					,							snake_case__	:   List[Any]=32					,							snake_case__	:   List[Any]=1_28					,							snake_case__	:   Optional[int]=0.1					,							snake_case__	:   List[str]=1e-6					,							snake_case__	:   Optional[Any]=1.0					,							snake_case__	:   Optional[int]="gated-gelu"					,							snake_case__	:   Any=True					,							snake_case__	:   List[Any]=True					,							snake_case__	:   List[str]="T5Tokenizer"					,							snake_case__	:   List[str]=True					,							snake_case__	:   Tuple=0					,							snake_case__	:   Optional[int]=1					,							snake_case__	:   List[str]=0					,							**snake_case__	:   Any					,							):

								"""simple docstring"""

								super().__init__(
								    is_encoder_decoder=snake_case__					,							tokenizer_class=snake_case__					,							tie_word_embeddings=snake_case__					,							pad_token_id=snake_case__					,							eos_token_id=snake_case__					,							decoder_start_token_id=snake_case__					,							**snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							d_model
								SCREAMING_SNAKE_CASE_					      =							d_kv
								SCREAMING_SNAKE_CASE_					      =							d_ff
								SCREAMING_SNAKE_CASE_					      =							num_layers
								SCREAMING_SNAKE_CASE_					      =							(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								SCREAMING_SNAKE_CASE_					      =							num_heads
								SCREAMING_SNAKE_CASE_					      =							relative_attention_num_buckets
								SCREAMING_SNAKE_CASE_					      =							relative_attention_max_distance
								SCREAMING_SNAKE_CASE_					      =							dropout_rate
								SCREAMING_SNAKE_CASE_					      =							layer_norm_epsilon
								SCREAMING_SNAKE_CASE_					      =							initializer_factor
								SCREAMING_SNAKE_CASE_					      =							feed_forward_proj
								SCREAMING_SNAKE_CASE_					      =							use_cache

								SCREAMING_SNAKE_CASE_					      =							self.feed_forward_proj.split('-'  )
								SCREAMING_SNAKE_CASE_					      =							act_info[-1]
								SCREAMING_SNAKE_CASE_					      =							act_info[0] == 'gated'

								if len(snake_case__  ) > 1 and act_info[0] != "gated" or len(snake_case__  ) > 2:
										raise ValueError(
										    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
										    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
										    '\'gated-gelu\' or \'relu\''  )

								if feed_forward_proj == "gated-gelu":
										SCREAMING_SNAKE_CASE_					      =							'gelu_new'



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.d_model



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.num_heads






						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.num_layers



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{
								    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
								    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
								}
								if self.use_past:
										SCREAMING_SNAKE_CASE_					      =							'past_encoder_sequence + sequence'
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
								else:
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'decoder_sequence'}
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'decoder_sequence'}

								if self.use_past:
										self.fill_with_past_key_values_(snake_case__					,							direction='inputs'  )

								return common_inputs



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return 13






						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return 5e-4