


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				:    str       				=     get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
     snake_case				:    Union[str, Any]       				=     json.load(f)







@require_torch
class 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return FSMTTokenizer.from_pretrained(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Tuple 							=       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       # 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						:Tuple 							=       F'''facebook/wmt19-{pair}'''
       a						:Optional[Any] 							=       self.get_tokenizer(_lowerCamelCase    )
       a						:str 							=       self.get_model(_lowerCamelCase    )

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

       a						:str 							=       tokenizer(_lowerCamelCase					,  return_tensors='''pt'''					,  truncation=_lowerCamelCase					,  padding='''longest'''    ).to(_lowerCamelCase    )
       a						:Optional[Any] 							=       model.generate(
           input_ids=batch.input_ids					,  num_beams=8					,  )
       a						:str 							=       tokenizer.batch_decode(
           _lowerCamelCase					,  skip_special_tokens=_lowerCamelCase					,  clean_up_tokenization_spaces=_lowerCamelCase    )
       a						:int 							=       calculate_bleu(_lowerCamelCase					,  _lowerCamelCase    )
       print(_lowerCamelCase    )
       self.assertGreaterEqual(scores['''bleu''']					,  _lowerCamelCase    )













"""simple docstring"""




from typing import TYPE_CHECKING

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


_snake_case          =   {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   ['PerceiverFeatureExtractor']
 _snake_case          =   ['PerceiverImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'PerceiverForImageClassificationConvProcessing',
     'PerceiverForImageClassificationFourier',
     'PerceiverForImageClassificationLearned',
     'PerceiverForMaskedLM',
     'PerceiverForMultimodalAutoencoding',
     'PerceiverForOpticalFlow',
     'PerceiverForSequenceClassification',
     'PerceiverLayer',
     'PerceiverModel',
     'PerceiverPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

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

