
'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


_A:					Tuple							=				logging.get_logger(__name__)

_A:					Any							=				{
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}






class UpperCAmelCase							(		UpperCamelCase_							):
    _A :			Union[str, Any]						   =       """van"""



    def __init__(  self     ,			__A=224     ,			__A=3     ,			__A=[7, 3, 3, 3]     ,			__A=[4, 2, 2, 2]     ,			__A=[64, 128, 320, 512]     ,			__A=[3, 3, 12, 3]     ,			__A=[8, 8, 4, 4]     ,			__A="gelu"     ,			__A=0.0_2     ,			__A=1E-6     ,			__A=1E-2     ,			__A=0.0     ,			__A=0.0     ,			**__A     ,			):
     super().__init__(**__A					)
     __UpperCAmelCase					=   image_size
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   patch_sizes
     __UpperCAmelCase					=   strides
     __UpperCAmelCase					=   hidden_sizes
     __UpperCAmelCase					=   depths
     __UpperCAmelCase					=   mlp_ratios
     __UpperCAmelCase					=   hidden_act
     __UpperCAmelCase					=   initializer_range
     __UpperCAmelCase					=   layer_norm_eps
     __UpperCAmelCase					=   layer_scale_init_value
     __UpperCAmelCase					=   drop_path_rate
     __UpperCAmelCase					=   dropout_rate












'''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:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # 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					)





