


'''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


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




@require_torch
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return FSMTTokenizer.from_pretrained(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														lowerCAmelCase__			  =      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_  )			->				Tuple:
														# 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__			  =      F"""facebook/wmt19-{pair}"""
														lowerCAmelCase__			  =      self.get_tokenizer(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_model(lowerCamelCase_  )

														lowerCAmelCase__			  =      bleu_data[pair]['''src''']
														lowerCAmelCase__			  =      bleu_data[pair]['''tgt''']

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






from typing import TYPE_CHECKING

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


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





