






"""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


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




@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						List[str]):




          '''simple docstring'''


          return FSMTTokenizer.from_pretrained(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	FSMTForConditionalGeneration.from_pretrained(lowercase_).to(lowercase_)
          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  :						Union[str, Any]		,			lowercase_  :						Dict		,			lowercase_  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	F'facebook/wmt19-{pair}'
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.get_tokenizer(lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.get_model(lowercase_)

          SCREAMING_SNAKE_CASE_   :						str							=	bleu_data[pair]['''src''']
          SCREAMING_SNAKE_CASE_   :						List[str]							=	bleu_data[pair]['''tgt''']

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










"""simple docstring"""






from __future__ import annotations

import requests



def       _A    (__a							)				-> dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
    return requests.get(__a							).json()



def       _A    (__a = 10							)				-> list[dict]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    SCREAMING_SNAKE_CASE_   :						Dict							=	requests.get(__a							).json()[:max_stories]
    return [get_hackernews_story(__a							) for story_id in story_ids]



def       _A    (__a = 10							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	hackernews_top_stories(__a							)
    return "\n".join('''* [{title}]({url})'''.format(**__a							) for story in stories							)


if __name__ == "__main__":
  print(hackernews_top_stories_as_markdown())


