


"""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


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





@require_torch
class __snake_case		(       unittest.TestCase   ):







   """simple docstring"""




   def SCREAMING_SNAKE_CASE_   (		self				:Union[str, Any]					,	UpperCamelCase__				:List[Any]							):
          return FSMTTokenizer.from_pretrained(__UpperCAmelCase							)




   def SCREAMING_SNAKE_CASE_   (		self				:int					,	UpperCamelCase__				:Any							):
          _a							 =					FSMTForConditionalGeneration.from_pretrained(__UpperCAmelCase							).to(__UpperCAmelCase							)
          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 SCREAMING_SNAKE_CASE_   (		self				:List[Any]					,	UpperCamelCase__				:int					,	UpperCamelCase__				:Dict							):
          _a							 =					f'facebook/wmt19-{pair}'
          _a							 =					self.get_tokenizer(__UpperCAmelCase							)
          _a							 =					self.get_model(__UpperCAmelCase							)

          _a							 =					bleu_data[pair]["src"]
          _a							 =					bleu_data[pair]["tgt"]

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





from __future__ import annotations





def 		a__      (		_UpperCamelCase      :			int | float | str			,_UpperCamelCase      :			int | float | str				):
   if nth_term == "":
      return [""]
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   __lowerCamelCase    				=     []
   for temp in range(int(_UpperCamelCase				)				):
      series.append(F"""1 / {pow(temp + 1			,int(_UpperCamelCase				)				)}""" if series else '''1'''				)
   return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_								=       int(input("""Enter the last number (nth term) of the P-Series"""))
  a_								=       int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))






