

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 ):



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	Tuple       ):






      '''simple docstring'''




      return FSMTTokenizer.from_pretrained(A_       )

 def    __magic_name__       (		self :	int     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    FSMTForConditionalGeneration.from_pretrained(A_       ).to(A_       )
      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    __magic_name__       (		self :	List[str]     ,						A_ :	Optional[Any]     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    F'''facebook/wmt19-{pair}'''
      _lowerCAmelCase   :					str        =    self.get_tokenizer(A_       )
      _lowerCAmelCase   :					List[Any]        =    self.get_model(A_       )

      _lowerCAmelCase   :					Tuple        =    bleu_data[pair]["src"]
      _lowerCAmelCase   :					Dict        =    bleu_data[pair]["tgt"]

      _lowerCAmelCase   :					Tuple        =    tokenizer(A_     ,						return_tensors="pt"     ,						truncation=A_     ,						padding="longest"       ).to(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    model.generate(
          input_ids=batch.input_ids     ,						num_beams=8     ,						)
      _lowerCAmelCase   :					Dict        =    tokenizer.batch_decode(
          A_     ,						skip_special_tokens=A_     ,						clean_up_tokenization_spaces=A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    calculate_bleu(A_     ,						A_       )
      print(A_       )
      self.assertGreaterEqual(scores["bleu"]     ,						A_       )









from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

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






