




import os

import numpy
import onnx







def      _a				(						SCREAMING_SNAKE_CASE_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					__lowerCAmelCase           =	a.name
					__lowerCAmelCase           =	b.name

					__lowerCAmelCase           =	""
					__lowerCAmelCase           =	""

					__lowerCAmelCase           =	a == b

					__lowerCAmelCase           =	name_a
					__lowerCAmelCase           =	name_b

					return res







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[Any]					):
					for i, input_name in enumerate(node_proto.input					):
										if input_name == name:
															node_proto.input.insert(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
															node_proto.input.pop(i + 1					)

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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[Any]					):
					for n in graph_proto.node:
										_node_replace_input_with(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    str					):
					__lowerCAmelCase           =	list(model.graph.initializer					)
					__lowerCAmelCase           =	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

										__lowerCAmelCase           =	inits[i].name
										__lowerCAmelCase           =	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				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					__lowerCAmelCase           =	os.path.dirname(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	os.path.basename(SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	onnx.load(os.path.join(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)					)

					__lowerCAmelCase           =	list(model.graph.initializer					)

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

					__lowerCAmelCase           =	0

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

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

																				__lowerCAmelCase           =	inits[j].data_type
																				__lowerCAmelCase           =	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: "				,				SCREAMING_SNAKE_CASE_					)
																				total_reduced_size += mem_size

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

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

					print("total reduced size: "				,				total_reduced_size / 10_24 / 10_24 / 10_24				,				"GB"					)

					__lowerCAmelCase           =	sorted(SCREAMING_SNAKE_CASE_					)
					_remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	"optimized_" + model_file_name
					__lowerCAmelCase           =	os.path.join(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					onnx.save(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					return new_model










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       a__   ( unittest.TestCase				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	UNetaDModel(
											    block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_uncond_unet
											__lowerCAmelCase           =	ScoreSdeVeScheduler()

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A		,      return_dict=_A      )[
											    0
											]

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]
											__lowerCAmelCase           =	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase           =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






@slow
@require_torch
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"google/ncsnpp-church-256"
											__lowerCAmelCase           =	UNetaDModel.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVeScheduler.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=1_0		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)

											__lowerCAmelCase           =	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




