'''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


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




@require_torch
class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    str   ,			__a    :    Union[str, Any] ):
         return FSMTTokenizer.from_pretrained(__a )



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   FSMTForConditionalGeneration.from_pretrained(__a ).to(__a )
         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 					_lowercase						(self    :    Tuple   ,			__a    :    str   ,			__a    :    int ):
         # 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
         UpperCAmelCase_           =   f"""facebook/wmt19-{pair}"""
         UpperCAmelCase_           =   self.get_tokenizer(__a )
         UpperCAmelCase_           =   self.get_model(__a )

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

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









"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
