
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  (DDPMScheduler,)



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   int:





								'''simple docstring'''






								__UpperCamelCase       =			{
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**__lowerCAmelCase			)
								return config



							def     A__		(  self			)->   List[Any]:





								'''simple docstring'''






								for timesteps in [1, 5, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__lowerCAmelCase			)



							def     A__		(  self			)->   Tuple:





								'''simple docstring'''






								for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]     ,	[0.0_0_2, 0.0_2, 0.2, 2]			):
									self.check_over_configs(beta_start=__lowerCAmelCase     ,	beta_end=__lowerCAmelCase			)



							def     A__		(  self			)->   int:





								'''simple docstring'''






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



							def     A__		(  self			)->   Dict:





								'''simple docstring'''






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



							def     A__		(  self			)->   List[str]:





								'''simple docstring'''






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



							def     A__		(  self			)->   Optional[int]:





								'''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     A__		(  self			)->   Optional[Any]:





								'''simple docstring'''






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



							def     A__		(  self			)->   Dict:





								'''simple docstring'''






								for t in [0, 500, 999]:
									self.check_over_forward(time_step=__lowerCAmelCase			)



							def     A__		(  self			)->   int:





								'''simple docstring'''






								__UpperCamelCase       =			self.scheduler_classes[0]
								__UpperCamelCase       =			self.get_scheduler_config()
								__UpperCamelCase       =			scheduler_class(**__lowerCAmelCase			)

								assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_0_9_7_9			)			) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.0_2			)			) < 1E-5



							def     A__		(  self			)->   List[Any]:





								'''simple docstring'''






								__UpperCamelCase       =			self.scheduler_classes[0]
								__UpperCamelCase       =			self.get_scheduler_config()
								__UpperCamelCase       =			scheduler_class(**__lowerCAmelCase			)

								__UpperCamelCase       =			len(__lowerCAmelCase			)

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

								for t in reversed(range(__lowerCAmelCase			)			):
									# 1. predict noise residual
									__UpperCamelCase       =			model(__lowerCAmelCase     ,	__lowerCAmelCase			)

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

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									__UpperCamelCase       =			pred_prev_sample

								__UpperCamelCase       =			torch.sum(torch.abs(__lowerCAmelCase			)			)
								__UpperCamelCase       =			torch.mean(torch.abs(__lowerCAmelCase			)			)

								assert abs(result_sum.item() - 2_5_8.9_6_0_6			) < 1E-2
								assert abs(result_mean.item() - 0.3_3_7_2			) < 1E-3



							def     A__		(  self			)->   List[str]:





								'''simple docstring'''






								__UpperCamelCase       =			self.scheduler_classes[0]
								__UpperCamelCase       =			self.get_scheduler_config(prediction_type='''v_prediction'''			)
								__UpperCamelCase       =			scheduler_class(**__lowerCAmelCase			)

								__UpperCamelCase       =			len(__lowerCAmelCase			)

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

								for t in reversed(range(__lowerCAmelCase			)			):
									# 1. predict noise residual
									__UpperCamelCase       =			model(__lowerCAmelCase     ,	__lowerCAmelCase			)

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

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									__UpperCamelCase       =			pred_prev_sample

								__UpperCamelCase       =			torch.sum(torch.abs(__lowerCAmelCase			)			)
								__UpperCamelCase       =			torch.mean(torch.abs(__lowerCAmelCase			)			)

								assert abs(result_sum.item() - 2_0_2.0_2_9_6			) < 1E-2
								assert abs(result_mean.item() - 0.2_6_3_1			) < 1E-3



							def     A__		(  self			)->   int:





								'''simple docstring'''






								__UpperCamelCase       =			self.scheduler_classes[0]
								__UpperCamelCase       =			self.get_scheduler_config()
								__UpperCamelCase       =			scheduler_class(**__lowerCAmelCase			)

								__UpperCamelCase       =			[100, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=__lowerCAmelCase			)

								__UpperCamelCase       =			scheduler.timesteps

								for i, timestep in enumerate(__lowerCAmelCase			):
									if i == len(__lowerCAmelCase			) - 1:
										__UpperCamelCase       =			-1
									else:
										__UpperCamelCase       =			timesteps[i + 1]

									__UpperCamelCase       =			scheduler.previous_timestep(__lowerCAmelCase			)
									__UpperCamelCase       =			prev_t.item()

									self.assertEqual(__lowerCAmelCase     ,	__lowerCAmelCase			)



							def     A__		(  self			)->   Optional[int]:





								'''simple docstring'''






								__UpperCamelCase       =			self.scheduler_classes[0]
								__UpperCamelCase       =			self.get_scheduler_config()
								__UpperCamelCase       =			scheduler_class(**__lowerCAmelCase			)

								__UpperCamelCase       =			[100, 87, 50, 51, 0]

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



							def     A__		(  self			)->   List[str]:





								'''simple docstring'''






								__UpperCamelCase       =			self.scheduler_classes[0]
								__UpperCamelCase       =			self.get_scheduler_config()
								__UpperCamelCase       =			scheduler_class(**__lowerCAmelCase			)

								__UpperCamelCase       =			[100, 87, 50, 1, 0]
								__UpperCamelCase       =			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     A__		(  self			)->   int:





								'''simple docstring'''






								__UpperCamelCase       =			self.scheduler_classes[0]
								__UpperCamelCase       =			self.get_scheduler_config()
								__UpperCamelCase       =			scheduler_class(**__lowerCAmelCase			)

								__UpperCamelCase       =			[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			)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__							:							int									=			{
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GitForCausalLM",
				    "GitModel",
				    "GitPreTrainedModel",
				    "GitVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
				from .processing_git import GitProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_git import (
								    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GitForCausalLM,
								    GitModel,
								    GitPreTrainedModel,
								    GitVisionModel,
								)

else:
				import sys

				lowercase__							:							Optional[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




