
import os

import numpy
import onnx




def  lowerCamelCase__ (  a__     :     str		,					a__     :     Optional[Any]   )						->		int:
       UpperCamelCase_											=				a.name
       UpperCamelCase_											=				b.name

       UpperCamelCase_											=				''''''
       UpperCamelCase_											=				''''''

       UpperCamelCase_											=				a == b

       UpperCamelCase_											=				name_a
       UpperCamelCase_											=				name_b

       return res




def  lowerCamelCase__ (  a__     :     str		,					a__     :     Any		,					a__     :     Tuple   )						->		Any:
       for i, input_name in enumerate(node_proto.input   ):
              if input_name == name:
                     node_proto.input.insert(_lowerCAmelCase		,					_lowerCAmelCase   )
                     node_proto.input.pop(i + 1   )

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g		,					_lowerCAmelCase		,					_lowerCAmelCase   )
              _graph_replace_input_with(node_proto.attribute[1].g		,					_lowerCAmelCase		,					_lowerCAmelCase   )
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g		,					_lowerCAmelCase		,					_lowerCAmelCase   )




def  lowerCamelCase__ (  a__     :     int		,					a__     :     Any		,					a__     :     Optional[Any]   )						->		Any:
       for n in graph_proto.node:
              _node_replace_input_with(_lowerCAmelCase		,					_lowerCAmelCase		,					_lowerCAmelCase   )




def  lowerCamelCase__ (  a__     :     str		,					a__     :     List[Any]		,					a__     :     List[str]   )						->		int:
       UpperCamelCase_											=				list(model.graph.initializer   )
       UpperCamelCase_											=				list(model_without_ext.graph.initializer   )
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              UpperCamelCase_											=				inits[i].name
              UpperCamelCase_											=				inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]   )

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph		,					_lowerCAmelCase		,					_lowerCAmelCase   )







def  lowerCamelCase__ (  a__     :     int   )						->		Tuple:
       UpperCamelCase_											=				os.path.dirname(_lowerCAmelCase   )
       UpperCamelCase_											=				os.path.basename(_lowerCAmelCase   )

       UpperCamelCase_											=				onnx.load(os.path.join(_lowerCAmelCase		,					_lowerCAmelCase   )   )

       UpperCamelCase_											=				list(model.graph.initializer   )

       UpperCamelCase_											=				set()
       UpperCamelCase_											=				{}
       UpperCamelCase_											=				[]

       UpperCamelCase_											=				0

       for i in range(len(_lowerCAmelCase   )   ):
              if i in dup_set:
                     continue

              for j in range(i + 1		,					len(_lowerCAmelCase   )   ):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]		,					inits[j]   ):
                            dup_set.add(_lowerCAmelCase   )
                            dup_set.add(_lowerCAmelCase   )

                            UpperCamelCase_											=				inits[j].data_type
                            UpperCamelCase_											=				numpy.prod(inits[j].dims   )
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print("""unexpected data type: """		,					_lowerCAmelCase   )
                            total_reduced_size += mem_size

                            UpperCamelCase_											=				inits[i].name
                            UpperCamelCase_											=				inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(_lowerCAmelCase   )
                            else:
                                   UpperCamelCase_											=				[name_j]
                            ind_to_replace.append((j, i)   )

       print("""total reduced size: """		,					total_reduced_size / 1024 / 1024 / 1024		,					"""GB"""   )

       UpperCamelCase_											=				sorted(_lowerCAmelCase   )
       _remove_dup_initializers_from_model(_lowerCAmelCase		,					_lowerCAmelCase		,					_lowerCAmelCase   )

       UpperCamelCase_											=				'''optimized_''' + model_file_name
       UpperCamelCase_											=				os.path.join(_lowerCAmelCase		,					_lowerCAmelCase   )
       onnx.save(_lowerCAmelCase		,					_lowerCAmelCase   )

       return new_model








'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





