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


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

@require_torch
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self       ,						lowerCamelCase__ ):
       return FSMTTokenizer.from_pretrained(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Dict       =	FSMTForConditionalGeneration.from_pretrained(lowerCamelCase__ ).to(lowerCamelCase__ )
       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      _a    (    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
       lowerCAmelCase_:							List[str]       =	F'''facebook/wmt19-{pair}'''
       lowerCAmelCase_:							Optional[Any]       =	self.get_tokenizer(lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	self.get_model(lowerCamelCase__ )

       lowerCAmelCase_:							List[Any]       =	bleu_data[pair]["src"]
       lowerCAmelCase_:							Dict       =	bleu_data[pair]["tgt"]

       lowerCAmelCase_:							Any       =	tokenizer(lowerCamelCase__       ,						return_tensors="pt"       ,						truncation=lowerCamelCase__       ,						padding="longest" ).to(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	model.generate(
           input_ids=batch.input_ids       ,						num_beams=8       ,						)
       lowerCAmelCase_:							List[Any]       =	tokenizer.batch_decode(
           lowerCamelCase__       ,						skip_special_tokens=lowerCamelCase__       ,						clean_up_tokenization_spaces=lowerCamelCase__ )
       lowerCAmelCase_:							str       =	calculate_bleu(lowerCamelCase__       ,						lowerCamelCase__ )
       print(lowerCamelCase__ )
       self.assertGreaterEqual(scores["bleu"]       ,						lowerCamelCase__ )
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a  :      int            =    {
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      Any            =    ["""BridgeTowerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    [
     """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BridgeTowerForContrastiveLearning""",
     """BridgeTowerForImageAndTextRetrieval""",
     """BridgeTowerForMaskedLM""",
     """BridgeTowerModel""",
     """BridgeTowerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 a  :      List[Any]            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)