



from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 						UpperCamelCase      (  snake_case__			:       np.ndarray			,	snake_case__			:       np.ndarray      )      ->	float:
   return math.sqrt(sum(pow(a - b			,	2      ) for a, b in zip(snake_case__			,	snake_case__      )      )      )
def 						UpperCamelCase      (  snake_case__			:       np.ndarray			,	snake_case__			:       np.ndarray      )      ->	list[list[list[float] | float]]:

   if dataset.ndim != value_array.ndim:
      UpperCamelCase       :    int		      =      (
          'Wrong input data\'s dimensions... '
          F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
      )
      raise ValueError(snake_case__      )

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

   if dataset.dtype != value_array.dtype:
      UpperCamelCase       :    Dict		      =      (
          'Input data have different datatype... '
          F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
      )
      raise TypeError(snake_case__      )

   UpperCamelCase       :    List[Any]		      =      []

   for value in value_array:
      UpperCamelCase       :    Optional[Any]		      =      euclidean(snake_case__			,	dataset[0]      )
      UpperCamelCase       :    Dict		      =      dataset[0].tolist()

      for dataset_value in dataset[1:]:
         UpperCamelCase       :    Union[str, Any]		      =      euclidean(snake_case__			,	snake_case__      )

         if dist > temp_dist:
            UpperCamelCase       :    str		      =      temp_dist
            UpperCamelCase       :    List[str]		      =      dataset_value.tolist()

      answer.append([vector, dist]      )

   return answer
def 						UpperCamelCase      (  snake_case__			:       np.ndarray			,	snake_case__			:       np.ndarray      )      ->	float:
   return np.dot(snake_case__			,	snake_case__      ) / (norm(snake_case__      ) * norm(snake_case__      ))


if __name__ == "__main__":
    import doctest

    doctest.testmod()





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


