"""simple docstring"""






import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
						import bitsandbytes as bnb
						import torch
						import torch.nn as nn

						from ..pytorch_utils import ConvaD

if is_accelerate_available():
						from accelerate import init_empty_weights
						from accelerate.utils import find_tied_parameters

lowerCAmelCase__  :  Union[str, Any]						     =	logging.get_logger(__name__)



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=None       ):
				# Recurse if needed
				if "." in tensor_name:
								UpperCAmelCase__             =							tensor_name.split('.'       )
								for split in splits[:-1]:
												UpperCAmelCase__             =							getattr(lowerCamelCase					,			lowerCamelCase       )
												if new_module is None:
																raise ValueError(f'''{module} has no attribute {split}.'''       )
												UpperCAmelCase__             =							new_module
								UpperCAmelCase__             =							splits[-1]

				if tensor_name not in module._parameters and tensor_name not in module._buffers:
								raise ValueError(f'''{module} does not have a parameter or a buffer named {tensor_name}.'''       )
				UpperCAmelCase__             =							tensor_name in module._buffers
				UpperCAmelCase__             =							getattr(lowerCamelCase					,			lowerCamelCase       )

				if old_value.device == torch.device('meta'       ) and device not in ["meta", torch.device('meta'       )] and value is None:
								raise ValueError(f'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.'''       )

				UpperCAmelCase__             =							False
				UpperCAmelCase__             =							False
				if is_buffer or not is_bitsandbytes_available():
								UpperCAmelCase__             =							False
								UpperCAmelCase__             =							False
				else:
								UpperCAmelCase__             =							hasattr(bnb.nn					,			'Params4bit'       ) and isinstance(module._parameters[tensor_name]					,			bnb.nn.Paramsabit       )
								UpperCAmelCase__             =							isinstance(module._parameters[tensor_name]					,			bnb.nn.IntaParams       )

				if is_abit or is_abit:
								UpperCAmelCase__             =							module._parameters[tensor_name]
								if param.device.type != "cuda":
												if value is None:
																UpperCAmelCase__             =							old_value.to(lowerCamelCase       )
												elif isinstance(lowerCamelCase					,			torch.Tensor       ):
																UpperCAmelCase__             =							value.to('cpu'       )
																if value.dtype == torch.inta:
																				UpperCAmelCase__             =							version.parse(importlib.metadata.version('bitsandbytes'       )       ) > version.parse(
																				    '0.37.2'       )
																				if not is_abit_serializable:
																								raise ValueError(
																								    'Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '
																								    'Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'       )
												else:
																UpperCAmelCase__             =							torch.tensor(lowerCamelCase					,			device='cpu'       )

												# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
												# Since weights are saved in the correct "orientation", we skip transposing when loading.
												if issubclass(module.source_cls					,			lowerCamelCase       ) and fpaa_statistics is None:
																UpperCAmelCase__             =							new_value.T

												UpperCAmelCase__             =							old_value.__dict__
												if is_abit:
																UpperCAmelCase__             =							bnb.nn.IntaParams(lowerCamelCase					,			requires_grad=lowerCamelCase					,			**lowerCamelCase       ).to(lowerCamelCase       )
												elif is_abit:
																UpperCAmelCase__             =							bnb.nn.Paramsabit(lowerCamelCase					,			requires_grad=lowerCamelCase					,			**lowerCamelCase       ).to(lowerCamelCase       )

												UpperCAmelCase__             =							new_value
												if fpaa_statistics is not None:
																setattr(module.weight					,			'SCB'					,			fpaa_statistics.to(lowerCamelCase       )       )

				else:
								if value is None:
												UpperCAmelCase__             =							old_value.to(lowerCamelCase       )
								elif isinstance(lowerCamelCase					,			torch.Tensor       ):
												UpperCAmelCase__             =							value.to(lowerCamelCase       )
								else:
												UpperCAmelCase__             =							torch.tensor(lowerCamelCase					,			device=lowerCamelCase       )

								if is_buffer:
												UpperCAmelCase__             =							new_value
								else:
												UpperCAmelCase__             =							nn.Parameter(lowerCamelCase					,			requires_grad=old_value.requires_grad       )
												UpperCAmelCase__             =							new_value



def   a_						(					lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=None					,			lowerCamelCase=None					,			lowerCamelCase=False       ):
				for name, module in model.named_children():
								if current_key_name is None:
												UpperCAmelCase__             =							[]
								current_key_name.append(lowerCamelCase       )

								if (isinstance(lowerCamelCase					,			nn.Linear       ) or isinstance(lowerCamelCase					,			lowerCamelCase       )) and name not in modules_to_not_convert:
												# Check if the current key is not in the `modules_to_not_convert`
												if not any(key in '.'.join(lowerCamelCase       ) for key in modules_to_not_convert       ):
																with init_empty_weights():
																				if isinstance(lowerCamelCase					,			lowerCamelCase       ):
																								UpperCAmelCase__      ,	UpperCAmelCase__             =							module.weight.shape
																				else:
																								UpperCAmelCase__             =							module.in_features
																								UpperCAmelCase__             =							module.out_features

																				if quantization_config.quantization_method() == "llm_int8":
																								UpperCAmelCase__             =							bnb.nn.LinearabitLt(
																								    lowerCamelCase					,			lowerCamelCase					,			module.bias is not None					,			has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight					,			threshold=quantization_config.llm_inta_threshold					,			)
																								UpperCAmelCase__             =							True
																				else:
																								if (
																								    quantization_config.llm_inta_skip_modules is not None
																								    and name in quantization_config.llm_inta_skip_modules
																								):
																												pass
																								else:
																												UpperCAmelCase__             =							bnb.nn.Linearabit(
																												    lowerCamelCase					,			lowerCamelCase					,			module.bias is not None					,			quantization_config.bnb_abit_compute_dtype					,			compress_statistics=quantization_config.bnb_abit_use_double_quant					,			quant_type=quantization_config.bnb_abit_quant_type					,			)
																												UpperCAmelCase__             =							True
                    # Store the module class in case we need to transpose the weight later
																				UpperCAmelCase__             =							type(lowerCamelCase       )
																				# Force requires grad to False to avoid unexpected errors
																				model._modules[name].requires_grad_(lowerCamelCase       )
								if len(list(module.children()       )       ) > 0:
												UpperCAmelCase__      ,	UpperCAmelCase__             =							_replace_with_bnb_linear(
												    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			has_been_replaced=lowerCamelCase					,			)
								# Remove the last key for recursion
								current_key_name.pop(-1       )
				return model, has_been_replaced



def   a_						(					lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=None					,			lowerCamelCase=None       ):
				UpperCAmelCase__             =							['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
				UpperCAmelCase__      ,	UpperCAmelCase__             =							_replace_with_bnb_linear(
				    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				if not has_been_replaced:
								logger.warning(
								    'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
								    ' Please double check your model architecture, or submit an issue on github if you think this is'
								    ' a bug.'       )

				return model



def   a_						(					*lowerCamelCase					,			**lowerCamelCase       ):
				warnings.warn(
				    '`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'					,			lowerCamelCase					,			)
				return replace_with_bnb_linear(*lowerCamelCase					,			**lowerCamelCase       )



def   a_						(					*lowerCamelCase					,			**lowerCamelCase       ):
				warnings.warn(
				    '`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'					,			lowerCamelCase					,			)
				return set_module_quantized_tensor_to_device(*lowerCamelCase					,			**lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							deepcopy(lowerCamelCase       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
				tied_model.tie_weights()

				UpperCAmelCase__             =							find_tied_parameters(lowerCamelCase       )
				# For compatibility with Accelerate < 0.18
				if isinstance(lowerCamelCase					,			lowerCamelCase       ):
								UpperCAmelCase__             =							sum(list(tied_params.values()       )					,			[]       ) + list(tied_params.keys()       )
				else:
								UpperCAmelCase__             =							sum(lowerCamelCase					,			[]       )
				UpperCAmelCase__             =							len(lowerCamelCase       ) > 0

				# Check if it is a base model
				UpperCAmelCase__             =							not hasattr(lowerCamelCase					,			model.base_model_prefix       )

				# Ignore this for base models (BertModel, GPT2Model, etc.)
				if (not has_tied_params) and is_base_model:
								return []

				# otherwise they have an attached head
				UpperCAmelCase__             =							list(model.named_children()       )
				UpperCAmelCase__             =							[list_modules[-1][0]]

				# add last module together with tied weights
				UpperCAmelCase__             =							set(lowerCamelCase       ) - set(lowerCamelCase       )
				UpperCAmelCase__             =							list(set(lowerCamelCase       )       ) + list(lowerCamelCase       )

				# remove ".weight" from the keys
				UpperCAmelCase__             =							['.weight', '.bias']
				UpperCAmelCase__             =							[]
				for name in list_untouched:
								for name_to_remove in names_to_remove:
												if name_to_remove in name:
																UpperCAmelCase__             =							name.replace(lowerCamelCase					,			''       )
								filtered_module_names.append(lowerCamelCase       )

				return filtered_module_names




"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = (PNDMScheduler,)
					snake_case__				    = (("num_inference_steps", 50),)







					def 		__lowerCAmelCase						(				self   :	List[str]    ,**lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							{
									    'num_train_timesteps': 1_000,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									}

									config.update(**lowerCamelCase__			)
									return config







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	Optional[Any]=0    ,**lowerCamelCase__   :	List[str]			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config(**lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
													scheduler.set_timesteps(lowerCamelCase__			)
													# copy over dummy past residuals
													UpperCAmelCase__             =							dummy_past_residuals[:]

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

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							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"

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							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 		__lowerCAmelCase						(				self   :	Tuple			):
									pass







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	List[str]=0    ,**lowerCamelCase__   :	Tuple			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
													scheduler.set_timesteps(lowerCamelCase__			)

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(lowerCamelCase__			)
																	UpperCAmelCase__             =							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__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							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"

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							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 		__lowerCAmelCase						(				self   :	List[Any]    ,**lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(**lowerCamelCase__			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

									UpperCAmelCase__             =							10
									UpperCAmelCase__             =							self.dummy_model()
									UpperCAmelCase__             =							self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__			)

									for i, t in enumerate(scheduler.prk_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									return sample







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)

									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													UpperCAmelCase__             =							self.dummy_sample
													UpperCAmelCase__             =							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'			):
																	UpperCAmelCase__             =							num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,1    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							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 		__lowerCAmelCase						(				self   :	List[Any]			):
									for timesteps in [100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCamelCase__			)

									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(steps_offset=1			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
									scheduler.set_timesteps(10			)
									assert torch.equal(
									    scheduler.timesteps    ,torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]			)    ,)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,[0.0_0_2, 0.0_2]			):
													self.check_over_configs(beta_start=lowerCamelCase__    ,beta_end=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCamelCase__			)







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







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									for t in [1, 5, 10]:
													self.check_over_forward(time_step=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for t, num_inference_steps in zip([1, 5, 10]    ,[10, 50, 100]			):
													self.check_over_forward(num_inference_steps=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int			):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									UpperCAmelCase__             =							27

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.dummy_sample
													UpperCAmelCase__             =							0.1 * sample

													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							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]			):
																	UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample







					def 		__lowerCAmelCase						(				self   :	int			):
									with self.assertRaises(lowerCamelCase__			):
													UpperCAmelCase__             =							self.scheduler_classes[0]
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

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







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop()
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_9_8.1_3_1_8			) < 1e-2
									assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop(prediction_type='v_prediction'			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 6_7.3_9_8_6			) < 1e-2
									assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 2_3_0.0_3_9_9			) < 1e-2
									assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_8_6.9_4_8_2			) < 1e-2
									assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3



