


"""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


__snake_case		=       get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
   __snake_case		=       json.load(f)


@require_torch
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[Any]:
          return FSMTTokenizer.from_pretrained(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				FSMTForConditionalGeneration.from_pretrained(__UpperCAmelCase							).to(__UpperCAmelCase							)
          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    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							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(__UpperCAmelCase							)
          _a    	=				self.get_model(__UpperCAmelCase							)

          _a    	=				bleu_data[pair]['''src''']
          _a    	=				bleu_data[pair]['''tgt''']

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









"""simple docstring"""






from typing import TYPE_CHECKING

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


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

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

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''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

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