




'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class     lowerCAmelCase_ (			__A ):







  '''simple docstring'''







  lowerCAmelCase_					:       Optional[Any]					  =    (UnCLIPScheduler,)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Union[str, Any]    ,		**_UpperCAmelCase     :						List[str]			):






   """simple docstring"""






   UpperCAmelCase__      				=     {
       """num_train_timesteps""": 10_00,
       """variance_type""": """fixed_small_log""",
       """clip_sample""": True,
       """clip_sample_range""": 1.0,
       """prediction_type""": """epsilon""",
   }

   config.update(**snake_case_			)
   return config


  def 				SCREAMING_SNAKE_CASE__      (    self     :						int			):






   """simple docstring"""






   for timesteps in [1, 5, 1_00, 10_00]:
    self.check_over_configs(num_train_timesteps=snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[int]			):






   """simple docstring"""






   for variance in ["fixed_small_log", "learned_range"]:
    self.check_over_configs(variance_type=snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						int			):






   """simple docstring"""






   for clip_sample in [True, False]:
    self.check_over_configs(clip_sample=snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[Any]			):






   """simple docstring"""






   for clip_sample_range in [1, 5, 10, 20]:
    self.check_over_configs(clip_sample_range=snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						str			):






   """simple docstring"""






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


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Tuple			):






   """simple docstring"""






   for time_step in [0, 5_00, 9_99]:
    for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
     if prev_timestep is not None and prev_timestep >= time_step:
      continue

     self.check_over_forward(time_step=snake_case_    ,		prev_timestep=snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Union[str, Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.scheduler_classes[0]
   UpperCAmelCase__      				=     self.get_scheduler_config(variance_type="""fixed_small_log"""			)
   UpperCAmelCase__      				=     scheduler_class(**snake_case_			)

   assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000E-10			)			) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.054_9625			)			) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.999_4987			)			) < 1E-5


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Tuple			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.scheduler_classes[0]
   UpperCAmelCase__      				=     self.get_scheduler_config(variance_type="""learned_range"""			)
   UpperCAmelCase__      				=     scheduler_class(**snake_case_			)

   UpperCAmelCase__      				=     0.5

   assert scheduler._get_variance(1    ,		predicted_variance=snake_case_			) - -10.171_2790 < 1E-5
   assert scheduler._get_variance(4_87    ,		predicted_variance=snake_case_			) - -5.799_8052 < 1E-5
   assert scheduler._get_variance(9_99    ,		predicted_variance=snake_case_			) - -0.001_0011 < 1E-5


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.scheduler_classes[0]
   UpperCAmelCase__      				=     self.get_scheduler_config()
   UpperCAmelCase__      				=     scheduler_class(**snake_case_			)

   UpperCAmelCase__      				=     scheduler.timesteps

   UpperCAmelCase__      				=     self.dummy_model()
   UpperCAmelCase__      				=     self.dummy_sample_deter
   UpperCAmelCase__      				=     torch.manual_seed(0			)

   for i, t in enumerate(snake_case_			):
    # 1. predict noise residual
    UpperCAmelCase__      				=     model(snake_case_    ,		snake_case_			)

    # 2. predict previous mean of sample x_t-1
    UpperCAmelCase__      				=     scheduler.step(snake_case_    ,		snake_case_    ,		snake_case_    ,		generator=snake_case_			).prev_sample

    UpperCAmelCase__      				=     pred_prev_sample

   UpperCAmelCase__      				=     torch.sum(torch.abs(snake_case_			)			)
   UpperCAmelCase__      				=     torch.mean(torch.abs(snake_case_			)			)

   assert abs(result_sum.item() - 2_52.2_68_24_95			) < 1E-2
   assert abs(result_mean.item() - 0.328_4743			) < 1E-3


  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.scheduler_classes[0]
   UpperCAmelCase__      				=     self.get_scheduler_config()
   UpperCAmelCase__      				=     scheduler_class(**snake_case_			)

   scheduler.set_timesteps(25			)

   UpperCAmelCase__      				=     scheduler.timesteps

   UpperCAmelCase__      				=     self.dummy_model()
   UpperCAmelCase__      				=     self.dummy_sample_deter
   UpperCAmelCase__      				=     torch.manual_seed(0			)

   for i, t in enumerate(snake_case_			):
    # 1. predict noise residual
    UpperCAmelCase__      				=     model(snake_case_    ,		snake_case_			)

    if i + 1 == timesteps.shape[0]:
     UpperCAmelCase__      				=     None
    else:
     UpperCAmelCase__      				=     timesteps[i + 1]

    # 2. predict previous mean of sample x_t-1
    UpperCAmelCase__      				=     scheduler.step(
        snake_case_    ,		snake_case_    ,		snake_case_    ,		prev_timestep=snake_case_    ,		generator=snake_case_			).prev_sample

    UpperCAmelCase__      				=     pred_prev_sample

   UpperCAmelCase__      				=     torch.sum(torch.abs(snake_case_			)			)
   UpperCAmelCase__      				=     torch.mean(torch.abs(snake_case_			)			)

   assert abs(result_sum.item() - 2_58.2_04_49_83			) < 1E-2
   assert abs(result_mean.item() - 0.336_2038			) < 1E-3


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any			):






   """simple docstring"""






   pass



  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[Any]			):






   """simple docstring"""






   pass

"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





