




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    __lowerCAmelCase       (							lowerCAmelCase):
							_a 					=						(PNDMScheduler,)
							_a 					=						(('''num_inference_steps''', 50),)






							def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					**_lowerCAmelCase:					Tuple				):
									lowercase    :str      	=						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.00_01,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**_lowerCAmelCase				)
									return config






							def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					int=0       ,					**_lowerCAmelCase:					Dict				):
									lowercase    :Optional[Any]      	=						dict(self.forward_default_kwargs				)
									lowercase    :Optional[int]      	=						kwargs.pop("num_inference_steps"       ,					_lowerCAmelCase				)
									lowercase    :Optional[Any]      	=						self.dummy_sample
									lowercase    :Optional[Any]      	=						0.1 * sample
									lowercase    :List[str]      	=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
											lowercase    :Any      	=						self.get_scheduler_config(**_lowerCAmelCase				)
											lowercase    :List[Any]      	=						scheduler_class(**_lowerCAmelCase				)
											scheduler.set_timesteps(_lowerCAmelCase				)
											# copy over dummy past residuals
											lowercase    :Optional[Any]      	=						dummy_past_residuals[:]

											with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(_lowerCAmelCase				)
													lowercase    :Tuple      	=						scheduler_class.from_pretrained(_lowerCAmelCase				)
													new_scheduler.set_timesteps(_lowerCAmelCase				)
													# copy over dummy past residuals
													lowercase    :Optional[Any]      	=						dummy_past_residuals[:]

											lowercase    :int      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
											lowercase    :Optional[int]      	=						new_scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

											lowercase    :Dict      	=						scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
											lowercase    :Tuple      	=						new_scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






							def 					SCREAMING_SNAKE_CASE    (   self:					str				):
									pass






							def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					Optional[Any]=0       ,					**_lowerCAmelCase:					List[Any]				):
									lowercase    :Union[str, Any]      	=						dict(self.forward_default_kwargs				)
									lowercase    :Union[str, Any]      	=						kwargs.pop("num_inference_steps"       ,					_lowerCAmelCase				)
									lowercase    :Union[str, Any]      	=						self.dummy_sample
									lowercase    :List[Any]      	=						0.1 * sample
									lowercase    :List[str]      	=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
											lowercase    :List[str]      	=						self.get_scheduler_config()
											lowercase    :Any      	=						scheduler_class(**_lowerCAmelCase				)
											scheduler.set_timesteps(_lowerCAmelCase				)

											# copy over dummy past residuals (must be after setting timesteps)
											lowercase    :Any      	=						dummy_past_residuals[:]

											with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(_lowerCAmelCase				)
													lowercase    :Dict      	=						scheduler_class.from_pretrained(_lowerCAmelCase				)
													# copy over dummy past residuals
													new_scheduler.set_timesteps(_lowerCAmelCase				)

													# copy over dummy past residual (must be after setting timesteps)
													lowercase    :Union[str, Any]      	=						dummy_past_residuals[:]

											lowercase    :int      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
											lowercase    :List[str]      	=						new_scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

											lowercase    :str      	=						scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
											lowercase    :List[str]      	=						new_scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






							def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					**_lowerCAmelCase:					Any				):
									lowercase    :Optional[int]      	=						self.scheduler_classes[0]
									lowercase    :Optional[Any]      	=						self.get_scheduler_config(**_lowerCAmelCase				)
									lowercase    :str      	=						scheduler_class(**_lowerCAmelCase				)

									lowercase    :str      	=						10
									lowercase    :Any      	=						self.dummy_model()
									lowercase    :Tuple      	=						self.dummy_sample_deter
									scheduler.set_timesteps(_lowerCAmelCase				)

									for i, t in enumerate(scheduler.prk_timesteps				):
											lowercase    :str      	=						model(_lowerCAmelCase       ,					_lowerCAmelCase				)
											lowercase    :Any      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps				):
											lowercase    :Any      	=						model(_lowerCAmelCase       ,					_lowerCAmelCase				)
											lowercase    :List[str]      	=						scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				).prev_sample

									return sample






							def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
									lowercase    :Tuple      	=						dict(self.forward_default_kwargs				)

									lowercase    :List[str]      	=						kwargs.pop("num_inference_steps"       ,					_lowerCAmelCase				)

									for scheduler_class in self.scheduler_classes:
											lowercase    :List[str]      	=						self.get_scheduler_config()
											lowercase    :List[Any]      	=						scheduler_class(**_lowerCAmelCase				)

											lowercase    :List[str]      	=						self.dummy_sample
											lowercase    :Optional[Any]      	=						0.1 * sample

											if num_inference_steps is not None and hasattr(_lowerCAmelCase       ,					"set_timesteps"				):
													scheduler.set_timesteps(_lowerCAmelCase				)
											elif num_inference_steps is not None and not hasattr(_lowerCAmelCase       ,					"set_timesteps"				):
													lowercase    :List[str]      	=						num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											lowercase    :Dict      	=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
											lowercase    :List[str]      	=						dummy_past_residuals[:]

											lowercase    :Tuple      	=						scheduler.step_prk(_lowerCAmelCase       ,					0       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
											lowercase    :Union[str, Any]      	=						scheduler.step_prk(_lowerCAmelCase       ,					1       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

											self.assertEqual(output_a.shape       ,					sample.shape				)
											self.assertEqual(output_a.shape       ,					output_a.shape				)

											lowercase    :Union[str, Any]      	=						scheduler.step_plms(_lowerCAmelCase       ,					0       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
											lowercase    :int      	=						scheduler.step_plms(_lowerCAmelCase       ,					1       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

											self.assertEqual(output_a.shape       ,					sample.shape				)
											self.assertEqual(output_a.shape       ,					output_a.shape				)






							def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
									for timesteps in [1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=_lowerCAmelCase				)






							def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=_lowerCAmelCase				)

									lowercase    :str      	=						self.scheduler_classes[0]
									lowercase    :Any      	=						self.get_scheduler_config(steps_offset=1				)
									lowercase    :Tuple      	=						scheduler_class(**_lowerCAmelCase				)
									scheduler.set_timesteps(10				)
									assert torch.equal(
									    scheduler.timesteps       ,					torch.LongTensor(
									        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]				)       ,					)






							def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
									for beta_start, beta_end in zip([0.00_01, 0.0_01]       ,					[0.0_02, 0.02]				):
											self.check_over_configs(beta_start=_lowerCAmelCase       ,					beta_end=_lowerCAmelCase				)






							def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=_lowerCAmelCase				)






							def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=_lowerCAmelCase				)






							def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
									for t in [1, 5, 10]:
											self.check_over_forward(time_step=_lowerCAmelCase				)






							def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
									for t, num_inference_steps in zip([1, 5, 10]       ,					[10, 50, 1_00]				):
											self.check_over_forward(num_inference_steps=_lowerCAmelCase				)






							def 					SCREAMING_SNAKE_CASE    (   self:					str				):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									lowercase    :List[str]      	=						27

									for scheduler_class in self.scheduler_classes:
											lowercase    :Dict      	=						self.dummy_sample
											lowercase    :Tuple      	=						0.1 * sample

											lowercase    :int      	=						self.get_scheduler_config()
											lowercase    :Optional[Any]      	=						scheduler_class(**_lowerCAmelCase				)

											scheduler.set_timesteps(_lowerCAmelCase				)

											# before power of 3 fix, would error on first step, so we only need to do two
											for i, t in enumerate(scheduler.prk_timesteps[:2]				):
													lowercase    :Any      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				).prev_sample






							def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
									with self.assertRaises(_lowerCAmelCase				):
											lowercase    :Optional[int]      	=						self.scheduler_classes[0]
											lowercase    :Union[str, Any]      	=						self.get_scheduler_config()
											lowercase    :Tuple      	=						scheduler_class(**_lowerCAmelCase				)

											scheduler.step_plms(self.dummy_sample       ,					1       ,					self.dummy_sample				).prev_sample






							def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
									lowercase    :Tuple      	=						self.full_loop()
									lowercase    :Optional[Any]      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
									lowercase    :Any      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

									assert abs(result_sum.item() - 1_98.13_18				) < 1e-2
									assert abs(result_mean.item() - 0.25_80				) < 1e-3






							def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
									lowercase    :str      	=						self.full_loop(prediction_type="v_prediction"				)
									lowercase    :Union[str, Any]      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
									lowercase    :Dict      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

									assert abs(result_sum.item() - 67.39_86				) < 1e-2
									assert abs(result_mean.item() - 0.08_78				) < 1e-3






							def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
									# We specify different beta, so that the first alpha is 0.99
									lowercase    :Union[str, Any]      	=						self.full_loop(set_alpha_to_one=_lowerCAmelCase       ,					beta_start=0.01				)
									lowercase    :str      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
									lowercase    :Union[str, Any]      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

									assert abs(result_sum.item() - 2_30.03_99				) < 1e-2
									assert abs(result_mean.item() - 0.29_95				) < 1e-3






							def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
									# We specify different beta, so that the first alpha is 0.99
									lowercase    :Optional[int]      	=						self.full_loop(set_alpha_to_one=_lowerCAmelCase       ,					beta_start=0.01				)
									lowercase    :Any      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
									lowercase    :Tuple      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

									assert abs(result_sum.item() - 1_86.94_82				) < 1e-2
									assert abs(result_mean.item() - 0.24_34				) < 1e-3









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



