






"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       (DPMSolverSinglestepScheduler,)
	__magic_name__			   =       (('''num_inference_steps''', 25),)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			**lowerCAmelCase_      :				List[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				Tuple      =      {
				    "num_train_timesteps": 1_000,
				    "beta_start": 0.0_0_0_1,
				    "beta_end": 0.0_2,
				    "beta_schedule": "linear",
				    "solver_order": 2,
				    "prediction_type": "epsilon",
				    "thresholding": False,
				    "sample_max_value": 1.0,
				    "algorithm_type": "dpmsolver++",
				    "solver_type": "midpoint",
				    "lambda_min_clipped": -float("inf"		),
				    "variance_type": None,
				}

				config.update(**lowerCAmelCase_		)
				return config





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Tuple=0			,			**lowerCAmelCase_      :				Any		)		->    int:
				UpperCAmelCase_  :				Tuple      =      dict(self.forward_default_kwargs		)
				UpperCAmelCase_  :				str      =      kwargs.pop("num_inference_steps"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      self.dummy_sample
				UpperCAmelCase_  :				Optional[int]      =      0.1 * sample
				UpperCAmelCase_  :				Union[str, Any]      =      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_  :				Dict      =      self.get_scheduler_config(**lowerCAmelCase_		)
							UpperCAmelCase_  :				int      =      scheduler_class(**lowerCAmelCase_		)
							scheduler.set_timesteps(lowerCAmelCase_		)
							# copy over dummy past residuals
							UpperCAmelCase_  :				Optional[int]      =      dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase_		)
										UpperCAmelCase_  :				Dict      =      scheduler_class.from_pretrained(lowerCAmelCase_		)
										new_scheduler.set_timesteps(lowerCAmelCase_		)
										# copy over dummy past residuals
										UpperCAmelCase_  :				List[Any]      =      dummy_past_residuals[: new_scheduler.config.solver_order]

							UpperCAmelCase_  ,			UpperCAmelCase_  :				Tuple      =      sample, sample
							for t in range(lowerCAmelCase_			,			time_step + scheduler.config.solver_order + 1		):
										UpperCAmelCase_  :				List[Any]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
										UpperCAmelCase_  :				List[Any]      =      new_scheduler.step(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      :				List[str]		)		->    Union[str, Any]:
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Union[str, Any]=0			,			**lowerCAmelCase_      :				int		)		->    int:
				UpperCAmelCase_  :				Dict      =      dict(self.forward_default_kwargs		)
				UpperCAmelCase_  :				List[Any]      =      kwargs.pop("num_inference_steps"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      self.dummy_sample
				UpperCAmelCase_  :				Any      =      0.1 * sample
				UpperCAmelCase_  :				List[str]      =      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_  :				int      =      self.get_scheduler_config()
							UpperCAmelCase_  :				Dict      =      scheduler_class(**lowerCAmelCase_		)
							scheduler.set_timesteps(lowerCAmelCase_		)

							# copy over dummy past residuals (must be after setting timesteps)
							UpperCAmelCase_  :				Optional[Any]      =      dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase_		)
										UpperCAmelCase_  :				Union[str, Any]      =      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)
										UpperCAmelCase_  :				List[Any]      =      dummy_past_residuals[: new_scheduler.config.solver_order]

							UpperCAmelCase_  :				int      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				Tuple      =      new_scheduler.step(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      :				Any			,			lowerCAmelCase_      :				Optional[Any]=None			,			**lowerCAmelCase_      :				Any		)		->    Optional[Any]:
				if scheduler is None:
							UpperCAmelCase_  :				List[str]      =      self.scheduler_classes[0]
							UpperCAmelCase_  :				Dict      =      self.get_scheduler_config(**lowerCAmelCase_		)
							UpperCAmelCase_  :				List[str]      =      scheduler_class(**lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_scheduler_config(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      scheduler_class(**lowerCAmelCase_		)

				UpperCAmelCase_  :				Any      =      10
				UpperCAmelCase_  :				Dict      =      self.dummy_model()
				UpperCAmelCase_  :				int      =      self.dummy_sample_deter
				scheduler.set_timesteps(lowerCAmelCase_		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase_  :				List[str]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				int      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				return sample





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				UpperCAmelCase_  :				List[Any]      =      DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
				UpperCAmelCase_  :				Dict      =      50
				UpperCAmelCase_  :				Any      =      self.dummy_model()
				UpperCAmelCase_  :				Dict      =      self.dummy_sample_deter
				scheduler.set_timesteps(lowerCAmelCase_		)

				# make sure that the first t is uneven
				for i, t in enumerate(scheduler.timesteps[3:]		):
							UpperCAmelCase_  :				List[Any]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				Optional[Any]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				UpperCAmelCase_  :				Dict      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_5_7_4		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Any:
				for timesteps in [25, 50, 100, 999, 1_000]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    str:
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				UpperCAmelCase_  :				List[str]      =      DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
				UpperCAmelCase_  :				Any      =      self.full_loop(scheduler=lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_7_9_1		) < 1e-3

				UpperCAmelCase_  :				Tuple      =      DEISMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase_  :				Tuple      =      DPMSolverMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase_  :				Any      =      UniPCMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase_  :				Optional[Any]      =      DPMSolverSinglestepScheduler.from_config(scheduler.config		)

				UpperCAmelCase_  :				Any      =      self.full_loop(scheduler=lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_7_9_1		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    int:
				self.check_over_configs(thresholding=lowerCAmelCase_		)
				for order in [1, 2, 3]:
							for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    thresholding=lowerCAmelCase_			,			prediction_type=lowerCAmelCase_			,			sample_max_value=lowerCAmelCase_			,			algorithm_type="dpmsolver++"			,			solver_order=lowerCAmelCase_			,			solver_type=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Any:
				for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Union[str, Any]:
				for algorithm_type in ["dpmsolver", "dpmsolver++"]:
							for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    solver_order=lowerCAmelCase_			,			solver_type=lowerCAmelCase_			,			prediction_type=lowerCAmelCase_			,			algorithm_type=lowerCAmelCase_			,			)
																UpperCAmelCase_  :				str      =      self.full_loop(
																    solver_order=lowerCAmelCase_			,			solver_type=lowerCAmelCase_			,			prediction_type=lowerCAmelCase_			,			algorithm_type=lowerCAmelCase_			,			)
																assert not torch.isnan(lowerCAmelCase_		).any(), "Samples have nan numbers"





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Optional[Any]:
				self.check_over_configs(lower_order_final=lowerCAmelCase_		)
				self.check_over_configs(lower_order_final=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Any:
				self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
				self.check_over_configs(lambda_min_clipped=-5.1		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Dict:
				self.check_over_configs(variance_type=lowerCAmelCase_		)
				self.check_over_configs(variance_type="learned_range"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    int:
				for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
							self.check_over_forward(num_inference_steps=lowerCAmelCase_			,			time_step=0		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.full_loop()
				UpperCAmelCase_  :				List[str]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_7_9_1		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				Any      =      self.full_loop(use_karras_sigmas=lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_2_4_8		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    int:
				UpperCAmelCase_  :				List[str]      =      self.full_loop(prediction_type="v_prediction"		)
				UpperCAmelCase_  :				Tuple      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.1_4_5_3		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Optional[Any]:
				UpperCAmelCase_  :				Any      =      self.full_loop(prediction_type="v_prediction"			,			use_karras_sigmas=lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.0_6_4_9		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_scheduler_config(thresholding=lowerCAmelCase_			,			dynamic_thresholding_ratio=0		)
				UpperCAmelCase_  :				Any      =      scheduler_class(**lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      10
				UpperCAmelCase_  :				Optional[int]      =      self.dummy_model()
				UpperCAmelCase_  :				Optional[Any]      =      self.dummy_sample_deter.half()
				scheduler.set_timesteps(lowerCAmelCase_		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase_  :				List[Any]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				assert sample.dtype == torch.floataa











"""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


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4



