"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = KandinskyVaaPipeline
					snake_case__				    = [
					    "image_embeds",
					    "negative_image_embeds",
					]
					snake_case__				    = ["image_embeds", "negative_image_embeds"]
					snake_case__				    = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									return self.time_input_dim







					@property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									return 100







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'in_channels': 4,
									    # Out channels is double in channels because predicts mean and variance
									    'out_channels': 8,
									    'addition_embed_type': 'image',
									    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
									    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
									    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
									    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
									    'layers_per_block': 1,
									    'encoder_hid_dim': self.text_embedder_hidden_size,
									    'encoder_hid_dim_type': 'image_proj',
									    'cross_attention_dim': self.cross_attention_dim,
									    'attention_head_dim': 4,
									    'resnet_time_scale_shift': 'scale_shift',
									    'class_embed_type': None,
									}

									UpperCAmelCase__             =							UNetaDConditionModel(**lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Any			):
									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}







					@property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							VQModel(**self.dummy_movq_kwargs			)
									return model







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							self.dummy_unet
									UpperCAmelCase__             =							self.dummy_movq

									UpperCAmelCase__             =							DDIMScheduler(
									    num_train_timesteps=1_000    ,beta_schedule='linear'    ,beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,clip_sample=lowerCamelCase__    ,set_alpha_to_one=lowerCamelCase__    ,steps_offset=1    ,prediction_type='epsilon'    ,thresholding=lowerCamelCase__    ,)

									UpperCAmelCase__             =							{
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}
									return components







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Dict=0			):
									UpperCAmelCase__             =							floats_tensor((1, self.text_embedder_hidden_size)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							floats_tensor((1, self.text_embedder_hidden_size)    ,rng=random.Random(seed + 1			)			).to(
									    lowerCamelCase__			)
									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'generator': generator,
									    'height': 64,
									    'width': 64,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()

									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)

									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							output.images

									UpperCAmelCase__             =							pipe(
									    **self.get_dummy_inputs(lowerCamelCase__			)    ,return_dict=lowerCamelCase__    ,)[0]

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

									assert image.shape == (1, 64, 64, 3)

									UpperCAmelCase__             =							np.array(
									    [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]			)

									assert (
									    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
									), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
									), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	Dict			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'			)

									UpperCAmelCase__             =							KandinskyVaaPriorPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-2-prior'    ,torch_dtype=torch.floataa			)
									pipe_prior.to(lowerCamelCase__			)

									UpperCAmelCase__             =							KandinskyVaaPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-2-decoder'    ,torch_dtype=torch.floataa			)
									UpperCAmelCase__             =							pipeline.to(lowerCamelCase__			)
									pipeline.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							'red cat, 4k photo'

									UpperCAmelCase__             =							torch.Generator(device='cuda'			).manual_seed(0			)
									UpperCAmelCase__      ,	UpperCAmelCase__             =							pipe_prior(
									    lowerCamelCase__    ,generator=lowerCamelCase__    ,num_inference_steps=5    ,negative_prompt=''    ,).to_tuple()

									UpperCAmelCase__             =							torch.Generator(device='cuda'			).manual_seed(0			)
									UpperCAmelCase__             =							pipeline(
									    image_embeds=lowerCamelCase__    ,negative_image_embeds=lowerCamelCase__    ,generator=lowerCamelCase__    ,num_inference_steps=100    ,output_type='np'    ,)

									UpperCAmelCase__             =							output.images[0]

									assert image.shape == (512, 512, 3)

									assert_mean_pixel_difference(lowerCamelCase__    ,lowerCamelCase__			)




"""simple docstring"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				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   a_						(					lowerCamelCase       ):
				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 == 1_1:
																				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 / 1_0_2_4 / 1_0_2_4 / 1_0_2_4					,			'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



