



'''simple docstring'''
# Algorithm for the pigeonhole sorting








def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		List[str] 				=   min(UpperCAmelCase_							)  # min() finds the minimum value
						_UpperCamelCase					:		Union[str, Any] 				=   max(UpperCAmelCase_							)  # max() finds the maximum value

						_UpperCamelCase					:		List[Any] 				=   max_val - min_val + 1  # size is difference of max and min values plus one

						# list of pigeonholes of size equal to the variable size
						_UpperCamelCase					:		str 				=   [0] * size

						# Populate the pigeonholes.
						for x in a:
												assert isinstance(UpperCAmelCase_  ,  UpperCAmelCase_							), "integers only please"
												holes[x - min_val] += 1

						# Putting the elements back into the array in an order.
						_UpperCamelCase					:		Tuple 				=   0
						for count in range(UpperCAmelCase_							):
												while holes[count] > 0:
																		holes[count] -= 1
																		_UpperCamelCase					:		List[Any] 				=   count + min_val
																		i += 1






def A__    (     ):
						_UpperCamelCase					:		Tuple 				=   [8, 3, 2, 7, 4, 6, 8]
						pigeonhole_sort(UpperCAmelCase_							)
						print('Sorted order is:'  ,  ' '.join(UpperCAmelCase_							)							)


if __name__ == "__main__":
					main()







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


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



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	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 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # 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    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

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



