






"""simple docstring"""



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


__A		:       Dict  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		{
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   "vit_mae"




					def __init__(				self     :						Dict    ,						A     :						List[str]=7_68    ,						A     :						Any=12    ,						A     :						Union[str, Any]=12    ,						A     :						Tuple=30_72    ,						A     :						Any="gelu"    ,						A     :						Tuple=0.0    ,						A     :						List[str]=0.0    ,						A     :						Tuple=0.02    ,						A     :						Tuple=1e-12    ,						A     :						int=2_24    ,						A     :						Dict=16    ,						A     :						int=3    ,						A     :						Tuple=True    ,						A     :						Tuple=16    ,						A     :						Optional[Any]=5_12    ,						A     :						Union[str, Any]=8    ,						A     :						List[Any]=20_48    ,						A     :						Dict=0.75    ,						A     :						Any=False    ,						**A     :						Optional[int]    ,						)  ->  Union[str, Any]:
							super().__init__(**A   )

							lowercase_ : List[Any]							       =   hidden_size
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : List[Any]							       =   num_attention_heads
							lowercase_ : Any							       =   intermediate_size
							lowercase_ : Optional[int]							       =   hidden_act
							lowercase_ : List[Any]							       =   hidden_dropout_prob
							lowercase_ : int							       =   attention_probs_dropout_prob
							lowercase_ : int							       =   initializer_range
							lowercase_ : Dict							       =   layer_norm_eps
							lowercase_ : Optional[Any]							       =   image_size
							lowercase_ : str							       =   patch_size
							lowercase_ : Dict							       =   num_channels
							lowercase_ : Any							       =   qkv_bias
							lowercase_ : Union[str, Any]							       =   decoder_num_attention_heads
							lowercase_ : Optional[Any]							       =   decoder_hidden_size
							lowercase_ : List[str]							       =   decoder_num_hidden_layers
							lowercase_ : List[Any]							       =   decoder_intermediate_size
							lowercase_ : Optional[Any]							       =   mask_ratio
							lowercase_ : Optional[Any]							       =   norm_pix_loss








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


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



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

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