

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[Any]						      =    (DPMSolverMultistepScheduler,)
			SCREAMING_SNAKE_CASE					:       Tuple						      =    (("num_inference_steps", 25),)







			def     snake_case__(					self			:    Dict							,       **_UpperCamelCase			:    Dict							)				->Union[str, Any]:
									snake_case_       =     {
									    '''num_train_timesteps''': 1_0_0_0,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''solver_order''': 2,
									    '''prediction_type''': '''epsilon''',
									    '''thresholding''': False,
									    '''sample_max_value''': 1.0,
									    '''algorithm_type''': '''dpmsolver++''',
									    '''solver_type''': '''midpoint''',
									    '''lower_order_final''': False,
									    '''lambda_min_clipped''': -float('''inf'''							),
									    '''variance_type''': None,
									}

									config.update(**_UpperCamelCase							)
									return config







			def     snake_case__(					self			:    int							,       _UpperCamelCase			:    Tuple=0							,       **_UpperCamelCase			:    Union[str, Any]							)				->Dict:
									snake_case_       =     dict(self.forward_default_kwargs							)
									snake_case_       =     kwargs.pop('''num_inference_steps'''							,       _UpperCamelCase							)
									snake_case_       =     self.dummy_sample
									snake_case_       =     0.1 * sample
									snake_case_       =     [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
															snake_case_       =     self.get_scheduler_config(**_UpperCamelCase							)
															snake_case_       =     scheduler_class(**_UpperCamelCase							)
															scheduler.set_timesteps(_UpperCamelCase							)
															# copy over dummy past residuals
															snake_case_       =     dummy_past_residuals[: scheduler.config.solver_order]

															with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_UpperCamelCase							)
																					snake_case_       =     scheduler_class.from_pretrained(_UpperCamelCase							)
																					new_scheduler.set_timesteps(_UpperCamelCase							)
																					# copy over dummy past residuals
																					snake_case_       =     dummy_past_residuals[: new_scheduler.config.solver_order]

															snake_case_,   snake_case_       =     sample, sample
															for t in range(_UpperCamelCase							,       time_step + scheduler.config.solver_order + 1							):
																					snake_case_       =     scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       **_UpperCamelCase							).prev_sample
																					snake_case_       =     new_scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       **_UpperCamelCase							).prev_sample

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







			def     snake_case__(					self			:    List[Any]							)				->List[str]:
									pass







			def     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    Union[str, Any]=0							,       **_UpperCamelCase			:    Optional[Any]							)				->List[Any]:
									snake_case_       =     dict(self.forward_default_kwargs							)
									snake_case_       =     kwargs.pop('''num_inference_steps'''							,       _UpperCamelCase							)
									snake_case_       =     self.dummy_sample
									snake_case_       =     0.1 * sample
									snake_case_       =     [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
															snake_case_       =     self.get_scheduler_config()
															snake_case_       =     scheduler_class(**_UpperCamelCase							)
															scheduler.set_timesteps(_UpperCamelCase							)

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

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

																					# copy over dummy past residual (must be after setting timesteps)
																					snake_case_       =     dummy_past_residuals[: new_scheduler.config.solver_order]

															snake_case_       =     scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       **_UpperCamelCase							).prev_sample
															snake_case_       =     new_scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       **_UpperCamelCase							).prev_sample

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







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    List[str]=None							,       **_UpperCamelCase			:    List[str]							)				->Optional[int]:
									if scheduler is None:
															snake_case_       =     self.scheduler_classes[0]
															snake_case_       =     self.get_scheduler_config(**_UpperCamelCase							)
															snake_case_       =     scheduler_class(**_UpperCamelCase							)

									snake_case_       =     1_0
									snake_case_       =     self.dummy_model()
									snake_case_       =     self.dummy_sample_deter
									scheduler.set_timesteps(_UpperCamelCase							)

									for i, t in enumerate(scheduler.timesteps							):
															snake_case_       =     model(_UpperCamelCase							,       _UpperCamelCase							)
															snake_case_       =     scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							).prev_sample

									return sample







			def     snake_case__(					self			:    Optional[int]							)				->Optional[int]:
									snake_case_       =     dict(self.forward_default_kwargs							)

									snake_case_       =     kwargs.pop('''num_inference_steps'''							,       _UpperCamelCase							)

									for scheduler_class in self.scheduler_classes:
															snake_case_       =     self.get_scheduler_config()
															snake_case_       =     scheduler_class(**_UpperCamelCase							)

															snake_case_       =     self.dummy_sample
															snake_case_       =     0.1 * sample

															if num_inference_steps is not None and hasattr(_UpperCamelCase							,       '''set_timesteps'''							):
																					scheduler.set_timesteps(_UpperCamelCase							)
															elif num_inference_steps is not None and not hasattr(_UpperCamelCase							,       '''set_timesteps'''							):
																					snake_case_       =     num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															snake_case_       =     [residual + 0.2, residual + 0.15, residual + 0.10]
															snake_case_       =     dummy_past_residuals[: scheduler.config.solver_order]

															snake_case_       =     scheduler.timesteps[5]
															snake_case_       =     scheduler.timesteps[6]

															snake_case_       =     scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       **_UpperCamelCase							).prev_sample
															snake_case_       =     scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       **_UpperCamelCase							).prev_sample

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







			def     snake_case__(					self			:    Dict							)				->Union[str, Any]:
									for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_UpperCamelCase							)







			def     snake_case__(					self			:    int							)				->Any:
									self.check_over_configs(thresholding=_UpperCamelCase							)
									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=_UpperCamelCase							,       prediction_type=_UpperCamelCase							,       sample_max_value=_UpperCamelCase							,       algorithm_type='''dpmsolver++'''							,       solver_order=_UpperCamelCase							,       solver_type=_UpperCamelCase							,       )







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







			def     snake_case__(					self			:    Dict							)				->Optional[Any]:
									for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
															for solver_type in ["midpoint", "heun"]:
																					for order in [1, 2, 3]:
																											for prediction_type in ["epsilon", "sample"]:
																																	if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																							if order == 3:
																																													continue
																																	else:
																																							self.check_over_configs(
																																							    solver_order=_UpperCamelCase							,       solver_type=_UpperCamelCase							,       prediction_type=_UpperCamelCase							,       algorithm_type=_UpperCamelCase							,       )
																																	snake_case_       =     self.full_loop(
																																	    solver_order=_UpperCamelCase							,       solver_type=_UpperCamelCase							,       prediction_type=_UpperCamelCase							,       algorithm_type=_UpperCamelCase							,       )
																																	assert not torch.isnan(_UpperCamelCase							).any(), "Samples have nan numbers"







			def     snake_case__(					self			:    Tuple							)				->str:
									self.check_over_configs(lower_order_final=_UpperCamelCase							)
									self.check_over_configs(lower_order_final=_UpperCamelCase							)







			def     snake_case__(					self			:    Union[str, Any]							)				->List[str]:
									self.check_over_configs(lambda_min_clipped=-float('''inf'''							)							)
									self.check_over_configs(lambda_min_clipped=-5.1							)







			def     snake_case__(					self			:    Union[str, Any]							)				->Any:
									self.check_over_configs(variance_type=_UpperCamelCase							)
									self.check_over_configs(variance_type='''learned_range'''							)







			def     snake_case__(					self			:    Optional[Any]							)				->Optional[int]:
									for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_forward(num_inference_steps=_UpperCamelCase							,       time_step=0							)







			def     snake_case__(					self			:    Optional[int]							)				->List[str]:
									snake_case_       =     self.full_loop()
									snake_case_       =     torch.mean(torch.abs(_UpperCamelCase							)							)

									assert abs(result_mean.item() - 0.3301							) < 1e-3







			def     snake_case__(					self			:    Any							)				->Optional[Any]:
									snake_case_       =     self.full_loop(thresholding=_UpperCamelCase							,       dynamic_thresholding_ratio=0.87							,       sample_max_value=0.5							)
									snake_case_       =     torch.mean(torch.abs(_UpperCamelCase							)							)

									assert abs(result_mean.item() - 1.1364							) < 1e-3







			def     snake_case__(					self			:    Tuple							)				->str:
									snake_case_       =     self.full_loop(prediction_type='''v_prediction'''							)
									snake_case_       =     torch.mean(torch.abs(_UpperCamelCase							)							)

									assert abs(result_mean.item() - 0.2251							) < 1e-3







			def     snake_case__(					self			:    Tuple							)				->Optional[Any]:
									snake_case_       =     self.full_loop(prediction_type='''v_prediction'''							,       use_karras_sigmas=_UpperCamelCase							)
									snake_case_       =     torch.mean(torch.abs(_UpperCamelCase							)							)

									assert abs(result_mean.item() - 0.2096							) < 1e-3







			def     snake_case__(					self			:    Tuple							)				->List[Any]:
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									snake_case_       =     DPMSolverMultistepScheduler(**self.get_scheduler_config()							)
									snake_case_       =     self.full_loop(scheduler=_UpperCamelCase							)
									snake_case_       =     torch.mean(torch.abs(_UpperCamelCase							)							)

									assert abs(result_mean.item() - 0.3301							) < 1e-3

									snake_case_       =     DPMSolverSinglestepScheduler.from_config(scheduler.config							)
									snake_case_       =     UniPCMultistepScheduler.from_config(scheduler.config							)
									snake_case_       =     DEISMultistepScheduler.from_config(scheduler.config							)
									snake_case_       =     DPMSolverMultistepScheduler.from_config(scheduler.config							)

									snake_case_       =     self.full_loop(scheduler=_UpperCamelCase							)
									snake_case_       =     torch.mean(torch.abs(_UpperCamelCase							)							)

									assert abs(result_mean.item() - 0.3301							) < 1e-3







			def     snake_case__(					self			:    Tuple							)				->Any:
									snake_case_       =     self.scheduler_classes[0]
									snake_case_       =     self.get_scheduler_config(thresholding=_UpperCamelCase							,       dynamic_thresholding_ratio=0							)
									snake_case_       =     scheduler_class(**_UpperCamelCase							)

									snake_case_       =     1_0
									snake_case_       =     self.dummy_model()
									snake_case_       =     self.dummy_sample_deter.half()
									scheduler.set_timesteps(_UpperCamelCase							)

									for i, t in enumerate(scheduler.timesteps							):
															snake_case_       =     model(_UpperCamelCase							,       _UpperCamelCase							)
															snake_case_       =     scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							).prev_sample

									assert sample.dtype == torch.floataa







			def     snake_case__(					self			:    Tuple							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[Any]:
									for scheduler_class in self.scheduler_classes:
															snake_case_       =     self.get_scheduler_config(**_UpperCamelCase							)
															snake_case_       =     scheduler_class(**_UpperCamelCase							)

															scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
															assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps


import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None	):
						snake_case_       =     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												snake_case_       =     weight.view(torch.intaa	)
												snake_case_       =     '''bfloat16'''
						snake_case_       =     weight.cpu().numpy()
						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		F'''{weight_name}.dat'''	)
						if index is not None:
												if dtype is None:
																		snake_case_       =     str(array.dtype	)
												snake_case_       =     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												snake_case_       =     array[None]
						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=array.dtype  ,		mode='''w+'''  ,		shape=array.shape	)
						snake_case_       =     array[:]
						file_array.flush()
						return index


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												snake_case_       =     (1,)

						snake_case_       =     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												snake_case_       =     '''int16'''

						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=SCREAMING_SNAKE_CASE__  ,		shape=SCREAMING_SNAKE_CASE__  ,		mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												snake_case_       =     weight[0]
						snake_case_       =     torch.tensor(SCREAMING_SNAKE_CASE__	)
						if weight_info["dtype"] == "bfloat16":
												snake_case_       =     weight.view(torch.bfloataa	)

						return weight


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						if index is None or len(SCREAMING_SNAKE_CASE__	) == 0:
												# Nothing to save
												return

						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		'''index.json'''	)
						if os.path.isfile(SCREAMING_SNAKE_CASE__	):
												with open(SCREAMING_SNAKE_CASE__  ,		'''r'''  ,		encoding='''utf-8'''	) as f:
																		snake_case_       =     json.load(SCREAMING_SNAKE_CASE__	)
						else:
												snake_case_       =     {}
						current_index.update(SCREAMING_SNAKE_CASE__	)

						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''  ,		encoding='''utf-8'''	) as f:
												json.dump(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		indent=2	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						os.makedirs(SCREAMING_SNAKE_CASE__  ,		exist_ok=SCREAMING_SNAKE_CASE__	)
						snake_case_       =     {}
						for name, parameter in state_dict.items():
												snake_case_       =     offload_weight(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		index=SCREAMING_SNAKE_CASE__	)

						# Update index
						save_offload_index(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)





class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Mapping							,       _UpperCamelCase			:    str							)				->int:
									snake_case_       =     dataset
									snake_case_       =     prefix







			def __getitem__(					self			:    List[Any]							,       _UpperCamelCase			:    Any							)				->Tuple:
									return self.dataset[f'''{self.prefix}{key}''']







			def __iter__(					self			:    Optional[int]							)				->int:
									return iter([key for key in self.dataset if key.startswith(self.prefix							)]							)







			def __len__(					self			:    Dict							)				->int:
									return len(self.dataset							)






class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    str							,       _UpperCamelCase			:    Dict[str, torch.Tensor] = None							,       _UpperCamelCase			:    Optional[Union[str, os.PathLike]] = None							,       _UpperCamelCase			:    Mapping = None							,       _UpperCamelCase			:    Tuple=None							,       )				->Dict:
									if state_dict is None and save_folder is None:
															raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''							)

									snake_case_       =     {} if state_dict is None else state_dict
									snake_case_       =     save_folder
									if index is None and save_folder is not None:
															with open(os.path.join(_UpperCamelCase							,       '''index.json'''							)							) as f:
																					snake_case_       =     json.load(_UpperCamelCase							)
									snake_case_       =     {} if index is None else index
									snake_case_       =     list(self.state_dict.keys()							)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]							)
									snake_case_       =     device







			def __getitem__(					self			:    int							,       _UpperCamelCase			:    str							)				->Union[str, Any]:
									# State dict gets priority
									if key in self.state_dict:
															return self.state_dict[key]
									snake_case_       =     self.index[key]
									if weight_info.get('''safetensors_file'''							) is not None:
															if not is_safetensors_available():
																					raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''							)

															from safetensors import safe_open

															snake_case_       =     '''cpu''' if self.device is None else self.device
															with safe_open(weight_info['''safetensors_file''']							,       framework='''pt'''							,       device=_UpperCamelCase							) as f:
																					snake_case_       =     f.get_tensor(weight_info.get('''weight_name'''							,       _UpperCamelCase							)							)

															if "dtype" in weight_info:
																					return tensor.to(getattr(_UpperCamelCase							,       weight_info['''dtype''']							)							)
															else:
																					return tensor

									snake_case_       =     os.path.join(self.save_folder							,       f'''{key}.dat'''							)
									return load_offloaded_weight(_UpperCamelCase							,       _UpperCamelCase							)







			def __iter__(					self			:    int							)				->List[Any]:
									return iter(self.all_keys							)







			def __len__(					self			:    Optional[Any]							)				->Optional[Any]:
									return len(self.all_keys							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result