



"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   0
     _UpperCAmelCase      :     List[Any]        =   len(lowercase_    ) - 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

          _UpperCAmelCase      :     Dict        =   left + ((item - sorted_collection[left]) * (right - left)) // (
              sorted_collection[right] - sorted_collection[left]
          )

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

          _UpperCAmelCase      :     str        =   sorted_collection[point]
          if current_item == item:
               return point
          else:
               if point < left:
                    _UpperCAmelCase      :     int        =   left
                    _UpperCAmelCase      :     Union[str, Any]        =   point
               elif point > right:
                    _UpperCAmelCase      :     Union[str, Any]        =   right
                    _UpperCAmelCase      :     List[Any]        =   point
               else:
                    if item < current_item:
                         _UpperCAmelCase      :     Any        =   point - 1
                    else:
                         _UpperCAmelCase      :     Dict        =   point + 1
     return None

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       int:





     '''simple docstring'''

     if sorted_collection[left] == sorted_collection[right]:
          if sorted_collection[left] == item:
               return left
          else:
               return None

     _UpperCAmelCase      :     Union[str, Any]        =   left + ((item - sorted_collection[left]) * (right - left)) // (
         sorted_collection[right] - sorted_collection[left]
     )

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

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       Tuple:





     '''simple docstring'''

     if collection != sorted(lowercase_    ):
          raise ValueError("Collection must be ascending sorted"    )
     return True


if __name__ == "__main__":
 import sys
 _lowerCAmelCase    : Any							=							0
 if debug == 1:
  _lowerCAmelCase    : Dict							=							[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")
  _lowerCAmelCase    : Union[str, Any]							=							67

 _lowerCAmelCase    : Optional[int]							=							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


_lowerCAmelCase    : Union[str, Any]							=							get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 _lowerCAmelCase    : Tuple							=							json.load(f)






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           return FSMTTokenizer.from_pretrained(A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   FSMTForConditionalGeneration.from_pretrained(A						).to(A						)
           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 						snake_case_ (					self					:     Any					,     A					:     Dict					,     A					:     List[str]						):
           # note: this test is not testing the best performance since it only evals a small batch
           # but it should be enough to detect a regression in the output quality
           _UpperCAmelCase      :     Any        =   f'facebook/wmt19-{pair}'
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer(A						)
           _UpperCAmelCase      :     Optional[int]        =   self.get_model(A						)

           _UpperCAmelCase      :     int        =   bleu_data[pair]["src"]
           _UpperCAmelCase      :     Optional[int]        =   bleu_data[pair]["tgt"]

           _UpperCAmelCase      :     List[str]        =   tokenizer(A					,     return_tensors="pt"					,     truncation=A					,     padding="longest"						).to(A						)
           _UpperCAmelCase      :     List[str]        =   model.generate(
               input_ids=batch.input_ids					,     num_beams=8					,     )
           _UpperCAmelCase      :     Any        =   tokenizer.batch_decode(
               A					,     skip_special_tokens=A					,     clean_up_tokenization_spaces=A						)
           _UpperCAmelCase      :     Any        =   calculate_bleu(A					,     A						)
           print(A						)
           self.assertGreaterEqual(scores["bleu"]					,     A						)
