






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


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



@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						str:
           return FSMTTokenizer.from_pretrained(UpperCamelCase__						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Any:
           __lowerCAmelCase             =       FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__						).to(UpperCamelCase__						)
           if torch_device == "cuda":
                model.half()
           return model


      @parameterized.expand(
          [
              ["en-ru", 2_6.0],
              ["ru-en", 2_2.0],
              ["en-de", 2_2.0],
              ["de-en", 2_9.0],
          ]						)
      @slow
      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						List[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
           __lowerCAmelCase             =       F"""facebook/wmt19-{pair}"""
           __lowerCAmelCase             =       self.get_tokenizer(UpperCamelCase__						)
           __lowerCAmelCase             =       self.get_model(UpperCamelCase__						)

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

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







'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase    :     List[Any]         =       {
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2TokenizerFast''']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       ['''LayoutLMv2FeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2ImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       [
       '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LayoutLMv2ForQuestionAnswering''',
       '''LayoutLMv2ForSequenceClassification''',
       '''LayoutLMv2ForTokenClassification''',
       '''LayoutLMv2Layer''',
       '''LayoutLMv2Model''',
       '''LayoutLMv2PreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

   _UpperCAmelCase    :     List[Any]         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



