



'''simple docstring'''




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	str    					=	(DDPMParallelScheduler,)

			def UpperCamelCase__      (							self  :				str							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	{
					    """num_train_timesteps""": 1_0_0_0,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					    """variance_type""": """fixed_small""",
					    """clip_sample""": True,
					}

					config.update(**lowerCAmelCase__		)
					return config

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]							,  [0.0_02, 0.02, 0.2, 2]		):
							self.check_over_configs(beta_start=lowerCAmelCase__							,  beta_end=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






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

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





					"""simple docstring"""






					self.check_over_configs(thresholding=lowerCAmelCase__		)
					for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(
									    thresholding=lowerCAmelCase__							,  prediction_type=lowerCAmelCase__							,  sample_max_value=lowerCAmelCase__							,  )

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	str					 =	scheduler_class(**lowerCAmelCase__		)

					assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.0_09_79		)		) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.02		)		) < 1E-5

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	len(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	int					 =	self.dummy_sample_deter
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.dummy_sample_deter + 0.1
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.dummy_sample_deter - 0.1

					__SCREAMING_SNAKE_CASE :	str					 =	samplea.shape[0]
					__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.stack([samplea, samplea, samplea]							,  dim=0		)
					__SCREAMING_SNAKE_CASE :	Any					 =	torch.arange(lowerCAmelCase__		)[0:3, None].repeat(1							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(samples.flatten(0							,  1		)							,  timesteps.flatten(0							,  1		)		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler.batch_step_no_noise(lowerCAmelCase__							,  timesteps.flatten(0							,  1		)							,  samples.flatten(0							,  1		)		)

					__SCREAMING_SNAKE_CASE :	Any					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					assert abs(result_sum.item() - 1_1_5_3.1_8_3_3		) < 1E-2
					assert abs(result_mean.item() - 0.50_05		) < 1E-3

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	len(lowerCAmelCase__		)

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

					for t in reversed(range(lowerCAmelCase__		)		):
							# 1. predict noise residual
							__SCREAMING_SNAKE_CASE :	List[str]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							# 2. predict previous mean of sample x_t-1
							__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  generator=lowerCAmelCase__		).prev_sample

							__SCREAMING_SNAKE_CASE :	int					 =	pred_prev_sample

					__SCREAMING_SNAKE_CASE :	str					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					assert abs(result_sum.item() - 2_5_8.9_6_0_6		) < 1E-2
					assert abs(result_mean.item() - 0.33_72		) < 1E-3

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.get_scheduler_config(prediction_type="""v_prediction"""		)
					__SCREAMING_SNAKE_CASE :	int					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	len(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Any					 =	self.dummy_sample_deter
					__SCREAMING_SNAKE_CASE :	int					 =	torch.manual_seed(0		)

					for t in reversed(range(lowerCAmelCase__		)		):
							# 1. predict noise residual
							__SCREAMING_SNAKE_CASE :	Tuple					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							# 2. predict previous mean of sample x_t-1
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  generator=lowerCAmelCase__		).prev_sample

							__SCREAMING_SNAKE_CASE :	int					 =	pred_prev_sample

					__SCREAMING_SNAKE_CASE :	Any					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					assert abs(result_sum.item() - 2_0_2.0_2_9_6		) < 1E-2
					assert abs(result_mean.item() - 0.26_31		) < 1E-3

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	int					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[1_0_0, 8_7, 5_0, 1, 0]

					scheduler.set_timesteps(timesteps=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	scheduler.timesteps

					for i, timestep in enumerate(lowerCAmelCase__		):
							if i == len(lowerCAmelCase__		) - 1:
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	-1
							else:
									__SCREAMING_SNAKE_CASE :	Dict					 =	timesteps[i + 1]

							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.previous_timestep(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Dict					 =	prev_t.item()

							self.assertEqual(lowerCAmelCase__							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[1_0_0, 8_7, 5_0, 5_1, 0]

					with self.assertRaises(lowerCAmelCase__							,  msg="""`custom_timesteps` must be in descending order."""		):
							scheduler.set_timesteps(timesteps=lowerCAmelCase__		)

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	int					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[1_0_0, 8_7, 5_0, 1, 0]
					__SCREAMING_SNAKE_CASE :	str					 =	len(lowerCAmelCase__		)

					with self.assertRaises(lowerCAmelCase__							,  msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""		):
							scheduler.set_timesteps(num_inference_steps=lowerCAmelCase__							,  timesteps=lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	List[str]					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	[scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    lowerCAmelCase__							,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""							,  ):
							scheduler.set_timesteps(timesteps=lowerCAmelCase__		)





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__ :			Any												=				logging.get_logger(__name__)

UpperCamelCase__ :			Dict												=				{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	'''beit'''

			def __init__(							self  :				Tuple							,  lowerCAmelCase__  :				List[Any]=8_1_9_2							,  lowerCAmelCase__  :				List[Any]=7_6_8							,  lowerCAmelCase__  :				List[Any]=1_2							,  lowerCAmelCase__  :				Union[str, Any]=1_2							,  lowerCAmelCase__  :				Dict=3_0_7_2							,  lowerCAmelCase__  :				List[Any]="gelu"							,  lowerCAmelCase__  :				List[Any]=0.0							,  lowerCAmelCase__  :				int=0.0							,  lowerCAmelCase__  :				Tuple=0.02							,  lowerCAmelCase__  :				Any=1E-12							,  lowerCAmelCase__  :				Dict=2_2_4							,  lowerCAmelCase__  :				List[str]=1_6							,  lowerCAmelCase__  :				int=3							,  lowerCAmelCase__  :				Union[str, Any]=False							,  lowerCAmelCase__  :				Union[str, Any]=False							,  lowerCAmelCase__  :				Any=False							,  lowerCAmelCase__  :				Union[str, Any]=False							,  lowerCAmelCase__  :				Optional[int]=0.1							,  lowerCAmelCase__  :				Dict=0.1							,  lowerCAmelCase__  :				Tuple=True							,  lowerCAmelCase__  :				str=[3, 5, 7, 1_1]							,  lowerCAmelCase__  :				str=[1, 2, 3, 6]							,  lowerCAmelCase__  :				Dict=True							,  lowerCAmelCase__  :				Dict=0.4							,  lowerCAmelCase__  :				int=2_5_6							,  lowerCAmelCase__  :				Any=1							,  lowerCAmelCase__  :				str=False							,  lowerCAmelCase__  :				Union[str, Any]=2_5_5							,  **lowerCAmelCase__  :				Optional[int]							,  ):





					"""simple docstring"""






					super().__init__(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	int					 =	hidden_size
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_hidden_layers
					__SCREAMING_SNAKE_CASE :	List[str]					 =	num_attention_heads
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	intermediate_size
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	hidden_act
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	hidden_dropout_prob
					__SCREAMING_SNAKE_CASE :	Any					 =	attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE :	Dict					 =	initializer_range
					__SCREAMING_SNAKE_CASE :	Any					 =	layer_norm_eps

					__SCREAMING_SNAKE_CASE :	Dict					 =	image_size
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	patch_size
					__SCREAMING_SNAKE_CASE :	int					 =	num_channels
					__SCREAMING_SNAKE_CASE :	Tuple					 =	use_mask_token
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	use_absolute_position_embeddings
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	use_relative_position_bias
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	use_shared_relative_position_bias
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	layer_scale_init_value
					__SCREAMING_SNAKE_CASE :	Any					 =	drop_path_rate
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	use_mean_pooling
					# decode head attributes (semantic segmentation)
					__SCREAMING_SNAKE_CASE :	Any					 =	out_indices
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pool_scales
					# auxiliary head attributes (semantic segmentation)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	use_auxiliary_head
					__SCREAMING_SNAKE_CASE :	str					 =	auxiliary_loss_weight
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	auxiliary_channels
					__SCREAMING_SNAKE_CASE :	str					 =	auxiliary_num_convs
					__SCREAMING_SNAKE_CASE :	int					 =	auxiliary_concat_input
					__SCREAMING_SNAKE_CASE :	Dict					 =	semantic_loss_ignore_index






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	List[str]    					=	version.parse('''1.11'''					)

			@property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return OrderedDict(
					    [
					        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
					    ]		)



			@property
			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					return 1E-4