
"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 					A__			(      A_):
		A_		:			Tuple							  =      (DPMSolverMultistepScheduler,)
		A_		:			List[str]							  =      (('''num_inference_steps''', 2_5),)

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Any		     = {
			    'num_train_timesteps': 10_00,
			    '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(**lowerCamelCase_					)
			return config

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE=0			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		str		     = dict(self.forward_default_kwargs					)
			__lowerCAmelCase     :		Tuple		     = kwargs.pop('num_inference_steps'			,						lowerCamelCase_					)
			__lowerCAmelCase     :		int		     = self.dummy_sample
			__lowerCAmelCase     :		int		     = 0.1 * sample
			__lowerCAmelCase     :		Tuple		     = [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase     :		Dict		     = self.get_scheduler_config(**lowerCamelCase_					)
				__lowerCAmelCase     :		List[Any]		     = scheduler_class(**lowerCamelCase_					)
				scheduler.set_timesteps(lowerCamelCase_					)
				# copy over dummy past residuals
				__lowerCAmelCase     :		List[Any]		     = dummy_past_residuals[: scheduler.config.solver_order]

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(lowerCamelCase_					)
					__lowerCAmelCase     :		Tuple		     = scheduler_class.from_pretrained(lowerCamelCase_					)
					new_scheduler.set_timesteps(lowerCamelCase_					)
					# copy over dummy past residuals
					__lowerCAmelCase     :		Union[str, Any]		     = dummy_past_residuals[: new_scheduler.config.solver_order]

				__lowerCAmelCase     ,  __lowerCAmelCase     :		str		     = sample, sample
				for t in range(lowerCamelCase_			,						time_step + scheduler.config.solver_order + 1					):
					__lowerCAmelCase     :		List[Any]		     = scheduler.step(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_			,						**lowerCamelCase_					).prev_sample
					__lowerCAmelCase     :		int		     = 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 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE=0			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Any		     = dict(self.forward_default_kwargs					)
			__lowerCAmelCase     :		Optional[Any]		     = kwargs.pop('num_inference_steps'			,						lowerCamelCase_					)
			__lowerCAmelCase     :		str		     = self.dummy_sample
			__lowerCAmelCase     :		int		     = 0.1 * sample
			__lowerCAmelCase     :		List[str]		     = [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase     :		int		     = self.get_scheduler_config()
				__lowerCAmelCase     :		List[Any]		     = scheduler_class(**lowerCamelCase_					)
				scheduler.set_timesteps(lowerCamelCase_					)

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

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(lowerCamelCase_					)
					__lowerCAmelCase     :		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)
					__lowerCAmelCase     :		Any		     = dummy_past_residuals[: new_scheduler.config.solver_order]

				__lowerCAmelCase     :		List[Any]		     = scheduler.step(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_			,						**lowerCamelCase_					).prev_sample
				__lowerCAmelCase     :		Union[str, 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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE=None			,						**_SCREAMING_SNAKE_CASE					):
			if scheduler is None:
				__lowerCAmelCase     :		str		     = self.scheduler_classes[0]
				__lowerCAmelCase     :		Tuple		     = self.get_scheduler_config(**lowerCamelCase_					)
				__lowerCAmelCase     :		Union[str, Any]		     = scheduler_class(**lowerCamelCase_					)

			__lowerCAmelCase     :		Dict		     = 10
			__lowerCAmelCase     :		int		     = self.dummy_model()
			__lowerCAmelCase     :		Optional[Any]		     = self.dummy_sample_deter
			scheduler.set_timesteps(lowerCamelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
				__lowerCAmelCase     :		int		     = model(lowerCamelCase_			,						lowerCamelCase_					)
				__lowerCAmelCase     :		Any		     = scheduler.step(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_					).prev_sample

			return sample

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[Any]		     = dict(self.forward_default_kwargs					)

			__lowerCAmelCase     :		Any		     = kwargs.pop('num_inference_steps'			,						lowerCamelCase_					)

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase     :		Optional[Any]		     = self.get_scheduler_config()
				__lowerCAmelCase     :		Dict		     = scheduler_class(**lowerCamelCase_					)

				__lowerCAmelCase     :		Dict		     = self.dummy_sample
				__lowerCAmelCase     :		List[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'					):
					__lowerCAmelCase     :		Union[str, Any]		     = num_inference_steps

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

				__lowerCAmelCase     :		Optional[Any]		     = scheduler.timesteps[5]
				__lowerCAmelCase     :		str		     = scheduler.timesteps[6]

				__lowerCAmelCase     :		int		     = scheduler.step(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_			,						**lowerCamelCase_					).prev_sample
				__lowerCAmelCase     :		Tuple		     = scheduler.step(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_			,						**lowerCamelCase_					).prev_sample

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

		def 				__lowerCamelCase				( self					):
			for timesteps in [25, 50, 1_00, 9_99, 10_00]:
				self.check_over_configs(num_train_timesteps=lowerCamelCase_					)

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

		def 				__lowerCamelCase				( self					):
			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=lowerCamelCase_			,						solver_type=lowerCamelCase_			,						prediction_type=lowerCamelCase_			,						algorithm_type=lowerCamelCase_			,						)
							__lowerCAmelCase     :		Optional[int]		     = 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 				__lowerCamelCase				( self					):
			self.check_over_configs(lower_order_final=lowerCamelCase_					)
			self.check_over_configs(lower_order_final=lowerCamelCase_					)

		def 				__lowerCamelCase				( self					):
			self.check_over_configs(lambda_min_clipped=-float('inf'					)					)
			self.check_over_configs(lambda_min_clipped=-5.1					)

		def 				__lowerCamelCase				( self					):
			self.check_over_configs(variance_type=lowerCamelCase_					)
			self.check_over_configs(variance_type='learned_range'					)

		def 				__lowerCamelCase				( self					):
			for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
				self.check_over_forward(num_inference_steps=lowerCamelCase_			,						time_step=0					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		int		     = self.full_loop()
			__lowerCAmelCase     :		List[str]		     = torch.mean(torch.abs(lowerCamelCase_					)					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Union[str, Any]		     = self.full_loop(thresholding=lowerCamelCase_			,						dynamic_thresholding_ratio=0.87			,						sample_max_value=0.5					)
			__lowerCAmelCase     :		Optional[Any]		     = torch.mean(torch.abs(lowerCamelCase_					)					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = self.full_loop(prediction_type='v_prediction'					)
			__lowerCAmelCase     :		List[str]		     = torch.mean(torch.abs(lowerCamelCase_					)					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.full_loop(prediction_type='v_prediction'			,						use_karras_sigmas=lowerCamelCase_					)
			__lowerCAmelCase     :		str		     = torch.mean(torch.abs(lowerCamelCase_					)					)

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

		def 				__lowerCamelCase				( self					):
			# make sure that iterating over schedulers with same config names gives same results
			# for defaults
			__lowerCAmelCase     :		int		     = DPMSolverMultistepScheduler(**self.get_scheduler_config()					)
			__lowerCAmelCase     :		Dict		     = self.full_loop(scheduler=lowerCamelCase_					)
			__lowerCAmelCase     :		Any		     = torch.mean(torch.abs(lowerCamelCase_					)					)

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

			__lowerCAmelCase     :		List[Any]		     = DPMSolverSinglestepScheduler.from_config(scheduler.config					)
			__lowerCAmelCase     :		Tuple		     = UniPCMultistepScheduler.from_config(scheduler.config					)
			__lowerCAmelCase     :		Tuple		     = DEISMultistepScheduler.from_config(scheduler.config					)
			__lowerCAmelCase     :		str		     = DPMSolverMultistepScheduler.from_config(scheduler.config					)

			__lowerCAmelCase     :		Optional[Any]		     = self.full_loop(scheduler=lowerCamelCase_					)
			__lowerCAmelCase     :		int		     = torch.mean(torch.abs(lowerCamelCase_					)					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Dict		     = self.scheduler_classes[0]
			__lowerCAmelCase     :		List[str]		     = self.get_scheduler_config(thresholding=lowerCamelCase_			,						dynamic_thresholding_ratio=0					)
			__lowerCAmelCase     :		List[Any]		     = scheduler_class(**lowerCamelCase_					)

			__lowerCAmelCase     :		Optional[Any]		     = 10
			__lowerCAmelCase     :		Tuple		     = self.dummy_model()
			__lowerCAmelCase     :		Union[str, Any]		     = self.dummy_sample_deter.half()
			scheduler.set_timesteps(lowerCamelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
				__lowerCAmelCase     :		Optional[Any]		     = model(lowerCamelCase_			,						lowerCamelCase_					)
				__lowerCAmelCase     :		Optional[int]		     = scheduler.step(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_					).prev_sample

			assert sample.dtype == torch.floataa







		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase     :		int		     = self.get_scheduler_config(**lowerCamelCase_					)
				__lowerCAmelCase     :		int		     = scheduler_class(**lowerCamelCase_					)

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


import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__A       			=      [nn.LayerNorm]

__A       			=      logging.get_logger(__name__)

__A       			=      version.parse(version.parse(torch.__version__).base_version)

__A       			=      parsed_torch_version_base >= version.parse("""2.0""")
__A       			=      parsed_torch_version_base >= version.parse("""1.12""")
__A       			=      parsed_torch_version_base >= version.parse("""1.11""")
__A       			=      parsed_torch_version_base < version.parse("""1.11""")

def 						UpperCamelCase				(	_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							int				, self   :							Optional[Any]						):
    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(_lowerCAmelCase				, _lowerCAmelCase				, parent.dim				, self						)
    else:
        return _softmax_backward_data(_lowerCAmelCase				, _lowerCAmelCase				, parent.dim				, self.dtype						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							nn.Linear				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							int = 0						):
    __a			      =						index.to(layer.weight.device						)
    __a			      =						layer.weight.index_select(_lowerCAmelCase				, _lowerCAmelCase						).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            __a			      =						layer.bias.clone().detach()
        else:
            __a			      =						layer.bias[index].clone().detach()
    __a			      =						list(layer.weight.size()						)
    __a			      =						len(_lowerCAmelCase						)
    __a			      =						nn.Linear(new_size[1]				, new_size[0]				, bias=layer.bias is not None						).to(layer.weight.device						)
    __a			      =						False
    new_layer.weight.copy_(W.contiguous()						)
    __a			      =						True
    if layer.bias is not None:
        __a			      =						False
        new_layer.bias.copy_(b.contiguous()						)
        __a			      =						True
    return new_layer






class 					a					(				nn.Module							):



  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]     )      -> List[str]:
      super().__init__()
      __a			      =						nf
      __a			      =						nn.Parameter(torch.empty(lowerCamelCase_						,  lowerCamelCase_     )     )
      __a			      =						nn.Parameter(torch.zeros(lowerCamelCase_     )     )
      nn.init.normal_(self.weight						,  std=0.02     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Optional[Any]     )      -> Union[str, Any]:
      __a			      =						x.size()[:-1] + (self.nf,)
      __a			      =						torch.addmm(self.bias						,  x.view(-1						,  x.size(-1     )     )						,  self.weight     )
      __a			      =						x.view(lowerCamelCase_     )
      return x

def 						UpperCamelCase				(	_lowerCAmelCase   :							ConvaD				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							int = 1						):
    __a			      =						index.to(layer.weight.device						)
    __a			      =						layer.weight.index_select(_lowerCAmelCase				, _lowerCAmelCase						).clone().detach()
    if dim == 0:
        __a			      =						layer.bias.clone().detach()
    else:
        __a			      =						layer.bias[index].clone().detach()
    __a			      =						list(layer.weight.size()						)
    __a			      =						len(_lowerCAmelCase						)
    __a			      =						ConvaD(new_size[1]				, new_size[0]						).to(layer.weight.device						)
    __a			      =						False
    new_layer.weight.copy_(W.contiguous()						)
    __a			      =						True
    __a			      =						False
    new_layer.bias.copy_(b.contiguous()						)
    __a			      =						True
    return new_layer

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[nn.Linear, ConvaD]				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							Optional[int] = None						):
    if isinstance(_lowerCAmelCase				, nn.Linear						):
        return prune_linear_layer(_lowerCAmelCase				, _lowerCAmelCase				, dim=0 if dim is None else dim						)
    elif isinstance(_lowerCAmelCase				, _lowerCAmelCase						):
        return prune_convad_layer(_lowerCAmelCase				, _lowerCAmelCase				, dim=1 if dim is None else dim						)
    else:
        raise ValueError(f"""Can't prune layer of class {layer.__class__}"""						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							Callable[..., torch.Tensor]				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							int				, *_lowerCAmelCase   :							List[Any]						):
    assert len(_lowerCAmelCase						) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    __a			      =						len(inspect.signature(_lowerCAmelCase						).parameters						)
    if num_args_in_forward_chunk_fn != len(_lowerCAmelCase						):
        raise ValueError(
            f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowerCAmelCase						)} input """
            """tensors are given"""						)

    if chunk_size > 0:
        __a			      =						input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f"""All input tenors have to be of the same shape: {tensor_shape}, """
                    f"""found shape {input_tensor.shape[chunk_dim]}"""						)

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
                f"""size {chunk_size}"""						)

        __a			      =						input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        __a			      =						tuple(input_tensor.chunk(_lowerCAmelCase				, dim=_lowerCAmelCase						) for input_tensor in input_tensors						)
        # apply forward fn to every tuple
        __a			      =						tuple(forward_fn(*_lowerCAmelCase						) for input_tensors_chunk in zip(*_lowerCAmelCase						)						)
        # concatenate output at same dimension
        return torch.cat(_lowerCAmelCase				, dim=_lowerCAmelCase						)

    return forward_fn(*_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[int]				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							Set[int]						):
    __a			      =						torch.ones(_lowerCAmelCase				, _lowerCAmelCase						)
    __a			      =						set(_lowerCAmelCase						) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        __a			      =						head - sum(1 if h < head else 0 for h in already_pruned_heads						)
        __a			      =						0
    __a			      =						mask.view(-1						).contiguous().eq(1						)
    __a			      =						torch.arange(len(_lowerCAmelCase						)						)[mask].long()
    return heads, index

def 						UpperCamelCase				(	*_lowerCAmelCase   :							Union[torch.Tensor, List[torch.Tensor]]				, _lowerCAmelCase   :							Optional[str] = None						):
    return torch.meshgrid(*_lowerCAmelCase				, indexing=_lowerCAmelCase						)






def 						UpperCamelCase				(	_lowerCAmelCase   :							torch.Tensor						):
    return tensor.device, storage_ptr(_lowerCAmelCase						), storage_size(_lowerCAmelCase						)
