

"""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 lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[str]:
          return FSMTTokenizer.from_pretrained(lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Optional[Any]:
          A				  =		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 UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      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
          A				  =		f'facebook/wmt19-{pair}'
          A				  =		self.get_tokenizer(lowerCamelCase_    )
          A				  =		self.get_model(lowerCamelCase_    )

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

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







"""simple docstring"""


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects



def   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]     ,   args[1:]				)]				)
                  raise TypeError(
                      f'Batch size was passed into `{function.__name__}` as the first argument when called.'
                      f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError("""No executable batch size found, reached zero."""				)
                  try:
                        return function(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator




