



"""simple docstring"""





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


SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     Dict          =	{
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}


class       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   'yoso'







   def __init__(self       ,	lowerCamelCase=50_265       ,	lowerCamelCase=768       ,	lowerCamelCase=12       ,	lowerCamelCase=12       ,	lowerCamelCase=3_072       ,	lowerCamelCase="gelu"       ,	lowerCamelCase=0.1       ,	lowerCamelCase=0.1       ,	lowerCamelCase=4_096       ,	lowerCamelCase=1       ,	lowerCamelCase=0.02       ,	lowerCamelCase=1e-12       ,	lowerCamelCase="absolute"       ,	lowerCamelCase=True       ,	lowerCamelCase=9       ,	lowerCamelCase=64       ,	lowerCamelCase=None       ,	lowerCamelCase=True       ,	lowerCamelCase=True       ,	lowerCamelCase=1       ,	lowerCamelCase=0       ,	lowerCamelCase=2       ,	**lowerCamelCase       ,	):




      '''simple docstring'''




      super().__init__(pad_token_id=lowerCamelCase       ,	bos_token_id=lowerCamelCase       ,	eos_token_id=lowerCamelCase       ,	**lowerCamelCase				)

      _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					     =		layer_norm_eps
      _lowerCAmelCase					     =		position_embedding_type
      _lowerCAmelCase					     =		use_expectation
      _lowerCAmelCase					     =		hash_code_len
      _lowerCAmelCase					     =		num_hash
      _lowerCAmelCase					     =		conv_window
      _lowerCAmelCase					     =		use_fast_hash
      _lowerCAmelCase					     =		lsh_backward




"""simple docstring"""





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   (HeunDiscreteScheduler,)
   __UpperCamelCase									=   10







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		{
          """num_train_timesteps""": 1_100,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**lowerCamelCase				)
      return config







   def 							A__						(self				):




      '''simple docstring'''




      for timesteps in [10, 50, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,	[0.0002, 0.002, 0.02]				):
         self.check_over_configs(beta_start=lowerCamelCase       ,	beta_end=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for schedule in ["linear", "scaled_linear", "exp"]:
         self.check_over_configs(beta_schedule=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for clip_sample_range in [1.0, 2.0, 3.0]:
         self.check_over_configs(clip_sample_range=lowerCamelCase       ,	clip_sample=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for prediction_type in ["epsilon", "v_prediction", "sample"]:
         self.check_over_configs(prediction_type=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3
      else:
         # CUDA
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config(prediction_type="""v_prediction"""				)
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6934e-07				) < 1e-2
         assert abs(result_mean.item() - 6.1112e-10				) < 1e-3
      else:
         # CUDA
         assert abs(result_sum.item() - 4.693428650170972e-07				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps       ,	device=lowerCamelCase				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if str(lowerCamelCase				).startswith("""cpu"""				):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3
      elif str(lowerCamelCase				).startswith("""mps"""				):
         # Larger tolerance on mps
         assert abs(result_mean.item() - 0.0002				) < 1e-2
      else:
         # CUDA
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase       ,	use_karras_sigmas=lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps       ,	device=lowerCamelCase				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      assert abs(result_sum.item() - 0.0_0015				) < 1e-2
      assert abs(result_mean.item() - 1.9869554535034695e-07				) < 1e-2