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


_A       =					logging.get_logger(__name__)

_A       =					{
    "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      _lowerCAmelCase (     __a	):
				_lowercase      		='''nystromformer'''




				def __init__( self    ,    _UpperCamelCase=30_000    ,    _UpperCamelCase=768    ,    _UpperCamelCase=12    ,    _UpperCamelCase=12    ,    _UpperCamelCase=3_072    ,    _UpperCamelCase="gelu_new"    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=510    ,    _UpperCamelCase=2    ,    _UpperCamelCase=64    ,    _UpperCamelCase=64    ,    _UpperCamelCase=65    ,    _UpperCamelCase=False    ,    _UpperCamelCase=0.02    ,    _UpperCamelCase=1e-5    ,    _UpperCamelCase=1    ,    _UpperCamelCase=0    ,    _UpperCamelCase=2    ,    **_UpperCamelCase    ,    )    ->				Optional[int]:
							lowerCAmelCase_      =     vocab_size
							lowerCAmelCase_      =     max_position_embeddings
							lowerCAmelCase_      =     hidden_size
							lowerCAmelCase_      =     num_hidden_layers
							lowerCAmelCase_      =     num_attention_heads
							lowerCAmelCase_      =     intermediate_size
							lowerCAmelCase_      =     hidden_act
							lowerCAmelCase_      =     hidden_dropout_prob
							lowerCAmelCase_      =     attention_probs_dropout_prob
							lowerCAmelCase_      =     initializer_range
							lowerCAmelCase_      =     type_vocab_size
							lowerCAmelCase_      =     segment_means_seq_len
							lowerCAmelCase_      =     num_landmarks
							lowerCAmelCase_      =     conv_kernel_size
							lowerCAmelCase_      =     inv_coeff_init_option
							lowerCAmelCase_      =     layer_norm_eps
							super().__init__(pad_token_id=_UpperCamelCase    ,    bos_token_id=_UpperCamelCase    ,    eos_token_id=_UpperCamelCase    ,    **_UpperCamelCase					)





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							pass



