



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


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class       _snake_case				(     UpperCAmelCase__ ):






   '''simple docstring'''






   A__      : int       =						"nystromformer"


   def __init__(    self:     int  ,lowerCamelCase_:     Optional[int]=30000  ,lowerCamelCase_:     Optional[int]=768  ,lowerCamelCase_:     Any=12  ,lowerCamelCase_:     Optional[int]=12  ,lowerCamelCase_:     List[Any]=3072  ,lowerCamelCase_:     List[Any]="gelu_new"  ,lowerCamelCase_:     Union[str, Any]=0.1  ,lowerCamelCase_:     Any=0.1  ,lowerCamelCase_:     List[str]=510  ,lowerCamelCase_:     Union[str, Any]=2  ,lowerCamelCase_:     str=64  ,lowerCamelCase_:     Tuple=64  ,lowerCamelCase_:     Union[str, Any]=65  ,lowerCamelCase_:     Tuple=False  ,lowerCamelCase_:     Dict=0.0_2  ,lowerCamelCase_:     Any=1e-5  ,lowerCamelCase_:     List[Any]=1  ,lowerCamelCase_:     Optional[Any]=0  ,lowerCamelCase_:     Optional[int]=2  ,**lowerCamelCase_:     List[Any]  ,)	->      List[Any]:
       UpperCAmelCase_     :       Dict  		=					vocab_size
       UpperCAmelCase_     :       Dict  		=					max_position_embeddings
       UpperCAmelCase_     :       List[str]  		=					hidden_size
       UpperCAmelCase_     :       Optional[int]  		=					num_hidden_layers
       UpperCAmelCase_     :       Dict  		=					num_attention_heads
       UpperCAmelCase_     :       int  		=					intermediate_size
       UpperCAmelCase_     :       List[str]  		=					hidden_act
       UpperCAmelCase_     :       Union[str, Any]  		=					hidden_dropout_prob
       UpperCAmelCase_     :       Any  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       Union[str, Any]  		=					initializer_range
       UpperCAmelCase_     :       List[Any]  		=					type_vocab_size
       UpperCAmelCase_     :       Tuple  		=					segment_means_seq_len
       UpperCAmelCase_     :       Optional[int]  		=					num_landmarks
       UpperCAmelCase_     :       List[str]  		=					conv_kernel_size
       UpperCAmelCase_     :       Any  		=					inv_coeff_init_option
       UpperCAmelCase_     :       Dict  		=					layer_norm_eps
       super().__init__(pad_token_id=snake_case__  ,bos_token_id=snake_case__  ,eos_token_id=snake_case__  ,**snake_case__							)






import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					List[str]						=	(VQDiffusionScheduler,)







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "num_vec_classes": 4_097,
									    "num_train_timesteps": 100,
									}

									config.update(**snake_case__ )
									return config







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	8
									_UpperCAmelCase						=	8

									_UpperCAmelCase						=	torch.randint(0       ,       snake_case__       ,       (batch_size, height * width) )

									return sample







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									assert False







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									def model(snake_case__       :     Dict       ,       snake_case__       :     Tuple       ,       *snake_case__       :     str ):
															_UpperCAmelCase				,					_UpperCAmelCase						=	sample.shape
															_UpperCAmelCase						=	torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels) )
															_UpperCAmelCase						=	F.log_softmax(logits.double()       ,       dim=1 ).float()
															return return_value

									return model







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for timesteps in [2, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for num_vec_classes in [5, 100, 1_000, 4_000]:
															self.check_over_configs(num_vec_classes=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									for t in [0, 50, 99]:
															self.check_over_forward(time_step=snake_case__ )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass




