



from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any				,   __lowerCamelCase	:    Union[str, Any]	)  ->						float:
     return math.sqrt(sum(pow(a - b				,   2	) for a, b in zip(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE	)	)	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any				,   __lowerCamelCase	:    List[Any]	)  ->						list[list[list[float] | float]]:

     if dataset.ndim != value_array.ndim:
          __lowerCAmelCase	   =(
              """Wrong input data's dimensions... """
              f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
          )
          raise ValueError(__SCREAMING_SNAKE_CASE	)

     try:
          if dataset.shape[1] != value_array.shape[1]:
               __lowerCAmelCase	   =(
                   """Wrong input data's shape... """
                   f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
               )
               raise ValueError(__SCREAMING_SNAKE_CASE	)
     except IndexError:
          if dataset.ndim != value_array.ndim:
               raise TypeError("""Wrong shape"""	)

     if dataset.dtype != value_array.dtype:
          __lowerCAmelCase	   =(
              """Input data have different datatype... """
              f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
          )
          raise TypeError(__SCREAMING_SNAKE_CASE	)

     __lowerCAmelCase	   =[]

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

          for dataset_value in dataset[1:]:
               __lowerCAmelCase	   =euclidean(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE	)

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

          answer.append([vector, dist]	)

     return answer






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    List[Any]	)  ->						float:
     return np.dot(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE	) / (norm(__SCREAMING_SNAKE_CASE	) * norm(__SCREAMING_SNAKE_CASE	))


if __name__ == "__main__":
    import doctest

    doctest.testmod()









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


lowercase_	       =			logging.get_logger(__name__)

lowercase_	       =			{
    '''google/realm-cc-news-pretrained-embedder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-encoder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-scorer''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-openqa''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'''
    ),
    '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json''',
    '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json''',
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "realm"



	def __init__(						self							:    Tuple				,	snake_case_							:    Tuple=3_05_22				,	snake_case_							:    List[Any]=7_68				,	snake_case_							:    Any=1_28				,	snake_case_							:    Union[str, Any]=12				,	snake_case_							:    List[str]=12				,	snake_case_							:    Any=8				,	snake_case_							:    Any=30_72				,	snake_case_							:    int="gelu_new"				,	snake_case_							:    Optional[int]=0.1				,	snake_case_							:    Tuple=0.1				,	snake_case_							:    List[Any]=5_12				,	snake_case_							:    List[Any]=2				,	snake_case_							:    Any=0.0_2				,	snake_case_							:    List[str]=1e-12				,	snake_case_							:    Optional[Any]=2_56				,	snake_case_							:    Dict=10				,	snake_case_							:    str=1e-3				,	snake_case_							:    List[str]=5				,	snake_case_							:    Optional[Any]=3_20				,	snake_case_							:    Optional[Any]=13_35_37_18				,	snake_case_							:    Tuple=50_00				,	snake_case_							:    Union[str, Any]=1				,	snake_case_							:    Union[str, Any]=0				,	snake_case_							:    List[str]=2				,	**snake_case_							:    List[str]				,	)->							str:
						super().__init__(pad_token_id=snake_case_				,	bos_token_id=snake_case_				,	eos_token_id=snake_case_				,	**snake_case_)

						# Common config
						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =retriever_proj_size
						__lowerCAmelCase	   =num_hidden_layers
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =num_candidates
						__lowerCAmelCase	   =intermediate_size
						__lowerCAmelCase	   =hidden_act
						__lowerCAmelCase	   =hidden_dropout_prob
						__lowerCAmelCase	   =attention_probs_dropout_prob
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =type_vocab_size
						__lowerCAmelCase	   =layer_norm_eps

						# Reader config
						__lowerCAmelCase	   =span_hidden_size
						__lowerCAmelCase	   =max_span_width
						__lowerCAmelCase	   =reader_layer_norm_eps
						__lowerCAmelCase	   =reader_beam_size
						__lowerCAmelCase	   =reader_seq_len

						# Retrieval config
						__lowerCAmelCase	   =num_block_records
						__lowerCAmelCase	   =searcher_beam_size




