'''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


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


@require_torch
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[str]:
        return FSMTTokenizer.from_pretrained(_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        lowercase_					:       Any     				=					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 		_lowerCamelCase (self       ,				_a       ,				_a				)    ->     Any:
        # 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
        lowercase_					:       Optional[int]     				=					f'''facebook/wmt19-{pair}'''
        lowercase_					:       int     				=					self.get_tokenizer(_a				)
        lowercase_					:       List[Any]     				=					self.get_model(_a				)

        lowercase_					:       int     				=					bleu_data[pair]['src']
        lowercase_					:       List[Any]     				=					bleu_data[pair]['tgt']

        lowercase_					:       Union[str, Any]     				=					tokenizer(_a       ,				return_tensors='pt'       ,				truncation=_a       ,				padding='longest'				).to(_a				)
        lowercase_					:       Tuple     				=					model.generate(
            input_ids=batch.input_ids       ,				num_beams=8       ,				)
        lowercase_					:       List[Any]     				=					tokenizer.batch_decode(
            _a       ,				skip_special_tokens=_a       ,				clean_up_tokenization_spaces=_a				)
        lowercase_					:       List[str]     				=					calculate_bleu(_a       ,				_a				)
        print(_a				)
        self.assertGreaterEqual(scores['bleu']       ,				_a				)





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A								=   {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BigBirdPegasusForCausalLM',
     'BigBirdPegasusForConditionalGeneration',
     'BigBirdPegasusForQuestionAnswering',
     'BigBirdPegasusForSequenceClassification',
     'BigBirdPegasusModel',
     'BigBirdPegasusPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

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




