"""simple docstring"""



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[Any]					     =				(UnCLIPScheduler,)





			def 		lowerCamelCase					(			self   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     List[Any]								=			{
										    """num_train_timesteps""": 1_000,
										    """variance_type""": """fixed_small_log""",
										    """clip_sample""": True,
										    """clip_sample_range""": 1.0,
										    """prediction_type""": """epsilon""",
										}

										config.update(**snake_case_							)
										return config





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for time_step in [0, 500, 999]:
																	for prev_timestep in [None, 5, 100, 250, 500, 750]:
																								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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			self.scheduler_classes[0]
										A__       :     Optional[Any]								=			self.get_scheduler_config(variance_type="""fixed_small_log"""							)
										A__       :     int								=			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(487							) - 0.0_54_96_25							)							) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.9_99_49_87							)							) < 1E-5





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.scheduler_classes[0]
										A__       :     Any								=			self.get_scheduler_config(variance_type="""learned_range"""							)
										A__       :     List[str]								=			scheduler_class(**snake_case_							)

										A__       :     Optional[Any]								=			0.5

										assert scheduler._get_variance(1   ,      predicted_variance=snake_case_							) - -10.1_71_27_90 < 1E-5
										assert scheduler._get_variance(487   ,      predicted_variance=snake_case_							) - -5.7_99_80_52 < 1E-5
										assert scheduler._get_variance(999   ,      predicted_variance=snake_case_							) - -0.0_01_00_11 < 1E-5





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			self.scheduler_classes[0]
										A__       :     Dict								=			self.get_scheduler_config()
										A__       :     str								=			scheduler_class(**snake_case_							)

										A__       :     Any								=			scheduler.timesteps

										A__       :     int								=			self.dummy_model()
										A__       :     Union[str, Any]								=			self.dummy_sample_deter
										A__       :     Dict								=			torch.manual_seed(0							)

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

																	# 2. predict previous mean of sample x_t-1
																	A__       :     List[Any]								=			scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      generator=snake_case_							).prev_sample

																	A__       :     Optional[int]								=			pred_prev_sample

										A__       :     Any								=			torch.sum(torch.abs(snake_case_							)							)
										A__       :     Optional[Any]								=			torch.mean(torch.abs(snake_case_							)							)

										assert abs(result_sum.item() - 2_52.2_68_24_95							) < 1E-2
										assert abs(result_mean.item() - 0.3_28_47_43							) < 1E-3





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			self.scheduler_classes[0]
										A__       :     List[Any]								=			self.get_scheduler_config()
										A__       :     Dict								=			scheduler_class(**snake_case_							)

										scheduler.set_timesteps(25							)

										A__       :     Optional[int]								=			scheduler.timesteps

										A__       :     int								=			self.dummy_model()
										A__       :     Any								=			self.dummy_sample_deter
										A__       :     List[Any]								=			torch.manual_seed(0							)

										for i, t in enumerate(snake_case_							):
																	# 1. predict noise residual
																	A__       :     Optional[Any]								=			model(snake_case_   ,      snake_case_							)

																	if i + 1 == timesteps.shape[0]:
																								A__       :     Tuple								=			None
																	else:
																								A__       :     List[Any]								=			timesteps[i + 1]

																	# 2. predict previous mean of sample x_t-1
																	A__       :     List[str]								=			scheduler.step(
																	    snake_case_   ,      snake_case_   ,      snake_case_   ,      prev_timestep=snake_case_   ,      generator=snake_case_							).prev_sample

																	A__       :     Tuple								=			pred_prev_sample

										A__       :     Optional[int]								=			torch.sum(torch.abs(snake_case_							)							)
										A__       :     Optional[int]								=			torch.mean(torch.abs(snake_case_							)							)

										assert abs(result_sum.item() - 2_58.2_04_49_83							) < 1E-2
										assert abs(result_mean.item() - 0.3_36_20_38							) < 1E-3





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''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





