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







@require_torch
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Tuple     )  ->			str:
        return FSMTTokenizer.from_pretrained(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			str     )  ->			Any:
        _A    		=  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 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Dict     )  ->			List[str]:
        # 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
        _A    		=  f'facebook/wmt19-{pair}'
        _A    		=  self.get_tokenizer(UpperCamelCase__     )
        _A    		=  self.get_model(UpperCamelCase__     )

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

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




'''simple docstring'''






_UpperCAmelCase    :     Any         =       '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich



