"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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__				    = KandinskyInpaintPipeline
					snake_case__				    = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
					snake_case__				    = [
					    "prompt",
					    "negative_prompt",
					    "image_embeds",
					    "negative_image_embeds",
					    "image",
					    "mask_image",
					]
					snake_case__				    = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "negative_prompt",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







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







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									return 32







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







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







					@property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									return 100







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'			)
									return tokenizer







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							MCLIPConfig(
									    numDims=self.cross_attention_dim    ,transformerDimensions=self.text_embedder_hidden_size    ,hidden_size=self.text_embedder_hidden_size    ,intermediate_size=37    ,num_attention_heads=4    ,num_hidden_layers=5    ,vocab_size=1_005    ,)

									UpperCAmelCase__             =							MultilingualCLIP(lowerCamelCase__			)
									UpperCAmelCase__             =							text_encoder.eval()

									return text_encoder







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'in_channels': 9,
									    # Out channels is double in channels because predicts mean and variance
									    'out_channels': 8,
									    'addition_embed_type': 'text_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': 'text_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   :	str			):
									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   :	str			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							VQModel(**self.dummy_movq_kwargs			)
									return model







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							self.dummy_text_encoder
									UpperCAmelCase__             =							self.dummy_tokenizer
									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__             =							{
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	Union[str, Any]    ,lowerCamelCase__   :	Dict=0			):
									UpperCAmelCase__             =							floats_tensor((1, self.cross_attention_dim)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							floats_tensor((1, self.cross_attention_dim)    ,rng=random.Random(seed + 1			)			).to(lowerCamelCase__			)
									# create init_image
									UpperCAmelCase__             =							floats_tensor((1, 3, 64, 64)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							image.cpu().permute(0    ,2    ,3    ,1			)[0]
									UpperCAmelCase__             =							Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('RGB'			).resize((256, 256)			)
									# create mask
									UpperCAmelCase__             =							np.ones((64, 64)    ,dtype=np.floataa			)
									UpperCAmelCase__             =							0

									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'prompt': 'horse',
									    'image': init_image,
									    'mask_image': mask,
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'generator': generator,
									    'height': 64,
									    'width': 64,
									    'num_inference_steps': 2,
									    'guidance_scale': 4.0,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									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]

									print(f'''image.shape {image.shape}'''			)

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

									UpperCAmelCase__             =							np.array(
									    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_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()}'''







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)






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




					"""simple docstring"""

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







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'			)

									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'			)
									UpperCAmelCase__             =							np.ones((768, 768)    ,dtype=np.floataa			)
									UpperCAmelCase__             =							0

									UpperCAmelCase__             =							'a hat'

									UpperCAmelCase__             =							KandinskyPriorPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-1-prior'    ,torch_dtype=torch.floataa			)
									pipe_prior.to(lowerCamelCase__			)

									UpperCAmelCase__             =							KandinskyInpaintPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-1-inpaint'    ,torch_dtype=torch.floataa			)
									UpperCAmelCase__             =							pipeline.to(lowerCamelCase__			)
									pipeline.set_progress_bar_config(disable=lowerCamelCase__			)

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

									UpperCAmelCase__             =							pipeline(
									    lowerCamelCase__    ,image=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,image_embeds=lowerCamelCase__    ,negative_image_embeds=lowerCamelCase__    ,generator=lowerCamelCase__    ,num_inference_steps=100    ,height=768    ,width=768    ,output_type='np'    ,)

									UpperCAmelCase__             =							output.images[0]

									assert image.shape == (768, 768, 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



