




"""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


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






@require_torch
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def      __snake_case							( self    :   Optional[Any]    ,			__UpperCAmelCase    :   Any):
      return FSMTTokenizer.from_pretrained(UpperCamelCase__)






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   List[Any]):
      a						:		Dict											=      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      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   int):
      a						:		List[Any]											=      f'''facebook/wmt19-{pair}'''
      a						:		Optional[int]											=      self.get_tokenizer(UpperCamelCase__)
      a						:		List[Any]											=      self.get_model(UpperCamelCase__)

      a						:		List[Any]											=      bleu_data[pair]['''src''']
      a						:		Union[str, Any]											=      bleu_data[pair]['''tgt''']

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










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

 __lowercase    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






