
"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Any     )     ->						Optional[Any]:
    a__		=			0
    a__		=			len(UpperCamelCase     ) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            else:
                return None

        a__		=			left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(UpperCamelCase     ):
            return None

        a__		=			sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                a__		=			left
                a__		=			point
            elif point > right:
                a__		=			right
                a__		=			point
            else:
                if item < current_item:
                    a__		=			point - 1
                else:
                    a__		=			point + 1
    return None



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		str							,		UpperCamelCase     :		Dict							,		UpperCamelCase     :		Tuple     )     ->						List[Any]:

    # avoid divided by 0 during interpolation
    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        else:
            return None

    a__		=			left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(UpperCamelCase     ):
        return None

    if sorted_collection[point] == item:
        return point
    elif point < left:
        return interpolation_search_by_recursion(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    elif point > right:
        return interpolation_search_by_recursion(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    else:
        if sorted_collection[point] > item:
            return interpolation_search_by_recursion(
                UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		point - 1     )
        else:
            return interpolation_search_by_recursion(
                UpperCamelCase							,		UpperCamelCase							,		point + 1							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		Optional[int]     )     ->						str:
    if collection != sorted(UpperCamelCase     ):
        raise ValueError('Collection must be ascending sorted'     )
    return True


if __name__ == "__main__":
    import sys
    a		:       Optional[int]     						=       0
    if debug == 1:
        a		:       List[str]     						=       [10, 30, 40, 45, 50, 66, 77, 93]
        try:
            __assert_sorted(collection)
        except ValueError:
            sys.exit('Sequence must be ascending sorted to apply interpolation search')
        a		:       Dict     						=       67

    a		:       Dict     						=       interpolation_search(collection, target)
    if result is not None:
        print(F'''{target} found at positions: {result}''')
    else:
        print('Not found')




"""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   )


