
"""simple docstring"""




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: str		    =							(DDIMScheduler,)
				UpperCAmelCase_							: Tuple		    =							(("eta", 0.0), ("num_inference_steps", 50))


				def 				A   (							self						,      **lowercase__     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'clip_sample': True,
									}

									config.update(**lowercase__     )
									return config


				def 				A   (							self						,      **lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =      self.get_scheduler_config(**lowercase__     )
									SCREAMING_SNAKE_CASE           =      scheduler_class(**lowercase__     )

									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      10, 0.0

									SCREAMING_SNAKE_CASE           =      self.dummy_model()
									SCREAMING_SNAKE_CASE           =      self.dummy_sample_deter

									scheduler.set_timesteps(lowercase__     )

									for t in scheduler.timesteps:
														SCREAMING_SNAKE_CASE           =      model(lowercase__						,      lowercase__     )
														SCREAMING_SNAKE_CASE           =      scheduler.step(lowercase__						,      lowercase__						,      lowercase__						,      lowercase__     ).prev_sample

									return sample


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									for timesteps in [100, 500, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase__     )


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=lowercase__     )

									SCREAMING_SNAKE_CASE           =      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =      self.get_scheduler_config(steps_offset=1     )
									SCREAMING_SNAKE_CASE           =      scheduler_class(**lowercase__     )
									scheduler.set_timesteps(5     )
									assert torch.equal(scheduler.timesteps						,      torch.LongTensor([801, 601, 401, 201, 1]     )     )


				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,      [0.002, 0.02, 0.2, 2]     ):
														self.check_over_configs(beta_start=lowercase__						,      beta_end=lowercase__     )


				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=lowercase__     )


				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=lowercase__     )


				def 				A   (							self     )       ->       str:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									for t in [1, 10, 49]:
														self.check_over_forward(time_step=lowercase__     )


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]						,      [10, 50, 500]     ):
														self.check_over_forward(time_step=lowercase__						,      num_inference_steps=lowercase__     )


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									for t, eta in zip([1, 10, 49]						,      [0.0, 0.5, 1.0]     ):
														self.check_over_forward(time_step=lowercase__						,      eta=lowercase__     )


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =      self.get_scheduler_config()
									SCREAMING_SNAKE_CASE           =      scheduler_class(**lowercase__     )

									assert torch.sum(torch.abs(scheduler._get_variance(0						,      0     ) - 0.0     )     ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(420						,      400     ) - 0.14_771     )     ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(980						,      960     ) - 0.32_460     )     ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(0						,      0     ) - 0.0     )     ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487						,      486     ) - 0.00_979     )     ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999						,      998     ) - 0.02     )     ) < 1E-5


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.full_loop()

									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(lowercase__     )     )

									assert abs(result_sum.item() - 172.0_067     ) < 1E-2
									assert abs(result_mean.item() - 0.223_967     ) < 1E-3


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.full_loop(prediction_type='v_prediction'     )

									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(lowercase__     )     )

									assert abs(result_sum.item() - 52.5_302     ) < 1E-2
									assert abs(result_mean.item() - 0.0_684     ) < 1E-3


				def 				A   (							self     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.full_loop(set_alpha_to_one=lowercase__						,      beta_start=0.01     )
									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(lowercase__     )     )

									assert abs(result_sum.item() - 149.8_295     ) < 1E-2
									assert abs(result_mean.item() - 0.1_951     ) < 1E-3




				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.full_loop(set_alpha_to_one=lowercase__						,      beta_start=0.01     )
									SCREAMING_SNAKE_CASE           =      torch.sum(torch.abs(lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      torch.mean(torch.abs(lowercase__     )     )

									assert abs(result_sum.item() - 149.0_784     ) < 1E-2
									assert abs(result_mean.item() - 0.1_941     ) < 1E-3


"""simple docstring"""




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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=None     ):
					SCREAMING_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.
										SCREAMING_SNAKE_CASE           =      weight.view(torch.intaa     )
										SCREAMING_SNAKE_CASE           =      'bfloat16'
					SCREAMING_SNAKE_CASE           =      weight.cpu().numpy()
					SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	f'''{weight_name}.dat'''     )
					if index is not None:
										if dtype is None:
															SCREAMING_SNAKE_CASE           =      str(array.dtype     )
										SCREAMING_SNAKE_CASE           =      {'dtype': dtype, 'shape': list(array.shape     )}
					if array.ndim == 0:
										SCREAMING_SNAKE_CASE           =      array[None]
					SCREAMING_SNAKE_CASE           =      np.memmap(SCREAMING_SNAKE_CASE_,	dtype=array.dtype,	mode='w+',	shape=array.shape     )
					SCREAMING_SNAKE_CASE           =      array[:]
					file_array.flush()
					return index

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

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

					SCREAMING_SNAKE_CASE           =      np.memmap(SCREAMING_SNAKE_CASE_,	dtype=SCREAMING_SNAKE_CASE_,	shape=SCREAMING_SNAKE_CASE_,	mode='r'     )

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

					return weight

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

					SCREAMING_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:
															SCREAMING_SNAKE_CASE           =      json.load(SCREAMING_SNAKE_CASE_     )
					else:
										SCREAMING_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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					os.makedirs(SCREAMING_SNAKE_CASE_,	exist_ok=SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      {}
					for name, parameter in state_dict.items():
										SCREAMING_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 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      dataset
									SCREAMING_SNAKE_CASE           =      prefix


				def __getitem__(							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									return self.dataset[f'''{self.prefix}{key}''']


				def __iter__(							self     )       ->       List[str]:
									"""simple docstring"""







									return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )




				def __len__(							self     )       ->       Optional[Any]:
									"""simple docstring"""







									return len(self.dataset     )






class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__=None						,      )       ->       Optional[Any]:
									"""simple docstring"""







									if state_dict is None and save_folder is None:
														raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'     )

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


				def __getitem__(							self						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									if key in self.state_dict:
														return self.state_dict[key]
									SCREAMING_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

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

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

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


				def __iter__(							self     )       ->       Any:
									"""simple docstring"""







									return iter(self.all_keys     )




				def __len__(							self     )       ->       Optional[Any]:
									"""simple docstring"""







									return len(self.all_keys     )






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_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
