





def 							SCREAMING_SNAKE_CASE__						(    __a = "The quick brown fox jumps over the lazy dog"   , ):
 snake_case_		:			Optional[int]											=	set()

 # Replace all the whitespace in our sentence
 snake_case_		:			int											=	input_str.replace(' '   , ''   )
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()   )
 return len(__a   ) == 26


def 							SCREAMING_SNAKE_CASE__						(    __a = "The quick brown fox jumps over the lazy dog"   , ):
 snake_case_		:			List[str]											=	[False] * 26
 for char in input_str:
  if char.islower():
   snake_case_		:			List[str]											=	True
  elif char.isupper():
   snake_case_		:			Optional[Any]											=	True
 return all(__a   )


def 							SCREAMING_SNAKE_CASE__						(    __a = "The quick brown fox jumps over the lazy dog"   , ):
 return len({char for char in input_str.lower() if char.isalpha()}   ) == 26


def 							SCREAMING_SNAKE_CASE__						(    ):
 from timeit import timeit

 snake_case_		:			str											=	'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
 print(timeit('is_pangram()'   , setup=__a   )   )
 print(timeit('is_pangram_faster()'   , setup=__a   )   )
 print(timeit('is_pangram_fastest()'   , setup=__a   )   )
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()








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 SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

							snake_case_		:			Optional[int]											=	tokenizer(_A		,   return_tensors='pt'		,   truncation=_A		,   padding='longest'	).to(_A	)
							snake_case_		:			List[Any]											=	model.generate(
							    input_ids=batch.input_ids		,   num_beams=8		,   )
							snake_case_		:			Dict											=	tokenizer.batch_decode(
							    _A		,   skip_special_tokens=_A		,   clean_up_tokenization_spaces=_A	)
							snake_case_		:			Tuple											=	calculate_bleu(_A		,   _A	)
							print(_A	)
							self.assertGreaterEqual(scores['bleu']		,   _A	)

