"""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__			=    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 							a					(		unittest.TestCase ):


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	int       ):
      return FSMTTokenizer.from_pretrained(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   FSMTForConditionalGeneration.from_pretrained(__lowerCAmelCase       ).to(__lowerCAmelCase       )
      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   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	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    			=   f'''facebook/wmt19-{pair}'''
      _UpperCAmelCase    			=   self.get_tokenizer(__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.get_model(__lowerCAmelCase       )

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

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

"""simple docstring"""


from __future__ import annotations

import typing
from collections import Counter






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   Counter()
  for base in range(1 ,max_perimeter + 1		):
    for perpendicular in range(lowercase ,max_perimeter + 1		):
      _UpperCAmelCase    			=   (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(lowercase		):
        _UpperCAmelCase    			=   int(base + perpendicular + hypotenuse		)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets






def      __UpperCAmelCase       (      lowercase = 10_00		):







  """simple docstring"""
  _UpperCAmelCase    			=   pythagorean_triple(lowercase		)
  return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
    print(F'''Perimeter {solution()} has maximum solutions''')
