'''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  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Dict							):



    """simple docstring"""



    return FSMTTokenizer.from_pretrained(_lowercase							)



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Any							= 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   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= f"""facebook/wmt19-{pair}"""
    _UpperCamelCase:     Tuple							= self.get_tokenizer(_lowercase							)
    _UpperCamelCase:     int							= self.get_model(_lowercase							)

    _UpperCamelCase:     Optional[int]							= bleu_data[pair]['''src''']
    _UpperCamelCase:     Optional[Any]							= bleu_data[pair]['''tgt''']

    _UpperCamelCase:     Dict							= tokenizer(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase		,  padding='''longest'''							).to(_lowercase							)
    _UpperCamelCase:     str							= model.generate(
        input_ids=batch.input_ids		,  num_beams=8		,  )
    _UpperCamelCase:     Dict							= tokenizer.batch_decode(
        _lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase							)
    _UpperCamelCase:     Dict							= calculate_bleu(_lowercase		,  _lowercase							)
    print(_lowercase							)
    self.assertGreaterEqual(scores['''bleu''']		,  _lowercase							)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLNetForMultipleChoice''',
      '''XLNetForQuestionAnswering''',
      '''XLNetForQuestionAnsweringSimple''',
      '''XLNetForSequenceClassification''',
      '''XLNetForTokenClassification''',
      '''XLNetLMHeadModel''',
      '''XLNetModel''',
      '''XLNetPreTrainedModel''',
      '''load_tf_weights_in_xlnet''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLNetForMultipleChoice''',
      '''TFXLNetForQuestionAnsweringSimple''',
      '''TFXLNetForSequenceClassification''',
      '''TFXLNetForTokenClassification''',
      '''TFXLNetLMHeadModel''',
      '''TFXLNetMainLayer''',
      '''TFXLNetModel''',
      '''TFXLNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)