

"""simple docstring"""





from ... import PretrainedConfig


_a       =							{
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Optional[Any]   		=	NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
 __UpperCAmelCase	:					Optional[Any]   		=	"nezha"


 def __init__(			self							:			Tuple,					UpperCAmelCase__							:			List[Any]=2_1_1_2_8,					UpperCAmelCase__							:			Tuple=7_6_8,					UpperCAmelCase__							:			List[str]=1_2,					UpperCAmelCase__							:			List[Any]=1_2,					UpperCAmelCase__							:			str=3_0_7_2,					UpperCAmelCase__							:			str="gelu",					UpperCAmelCase__							:			Dict=0.1,					UpperCAmelCase__							:			int=0.1,					UpperCAmelCase__							:			List[str]=5_1_2,					UpperCAmelCase__							:			List[Any]=6_4,					UpperCAmelCase__							:			Optional[int]=2,					UpperCAmelCase__							:			str=0.02,					UpperCAmelCase__							:			Optional[int]=1E-12,					UpperCAmelCase__							:			int=0.1,					UpperCAmelCase__							:			str=0,					UpperCAmelCase__							:			Optional[Any]=2,					UpperCAmelCase__							:			List[Any]=3,					UpperCAmelCase__							:			str=True,					**UpperCAmelCase__							:			int,					):
    super().__init__(pad_token_id=UpperCAmelCase__,					bos_token_id=UpperCAmelCase__,					eos_token_id=UpperCAmelCase__,					**UpperCAmelCase__	)

    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						hidden_act
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						max_relative_position
    __lowercase								=						type_vocab_size
    __lowercase								=						initializer_range
    __lowercase								=						layer_norm_eps
    __lowercase								=						classifier_dropout
    __lowercase								=						use_cache









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 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          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 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		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
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

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



