




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Tuple										=[]
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
				        F"stage{idx}.patch_embed.proj.weight",
				    ) )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
				        F"stage{idx}.patch_embed.proj.bias",
				    ) )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
				        F"stage{idx}.patch_embed.norm.weight",
				    ) )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
				        F"stage{idx}.patch_embed.norm.bias",
				    ) )
				return embed




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						List[Any]										=[]
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj.weight",
				    ) )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj.bias",
				    ) )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight") )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias") )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight") )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias") )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight") )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias") )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight") )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias") )
				return attention_weights




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=[]
				token.append((F"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""") )
				return token




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Any										=[]
				head.append(("""layernorm.weight""", """norm.weight""") )
				head.append(("""layernorm.bias""", """norm.bias""") )
				head.append(("""classifier.weight""", """head.weight""") )
				head.append(("""classifier.bias""", """head.bias""") )
				return head




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Dict										="""imagenet-1k-id2label.json"""
				__magic_name__						:						Union[str, Any]										=1000

				__magic_name__						:						int										="""huggingface/label-files"""
				__magic_name__						:						Optional[Any]										=num_labels
				__magic_name__						:						str										=json.load(open(cached_download(hf_hub_url(lowerCamelCase						,  lowerCamelCase						,  repo_type="""dataset""" ) )						,  """r""" ) )
				__magic_name__						:						Optional[Any]										={int(lowerCamelCase ): v for k, v in idalabel.items()}

				__magic_name__						:						List[str]										=idalabel
				__magic_name__						:						Optional[Any]										={v: k for k, v in idalabel.items()}

				__magic_name__						:						Dict										=CvtConfig(num_labels=lowerCamelCase						,  idalabel=lowerCamelCase						,  labelaid=lowerCamelCase )

				# For depth size 13 (13 = 1+2+10)
				if cvt_model.rsplit("""/"""						,  1 )[-1][4:6] == "13":
								__magic_name__						:						Tuple										=[1, 2, 10]

				# For depth size 21 (21 = 1+4+16)
				elif cvt_model.rsplit("""/"""						,  1 )[-1][4:6] == "21":
								__magic_name__						:						Optional[Any]										=[1, 4, 16]

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								__magic_name__						:						int										=[2, 2, 20]
								__magic_name__						:						Tuple										=[3, 12, 16]
								__magic_name__						:						Optional[Any]										=[192, 768, 1024]

				__magic_name__						:						str										=CvtForImageClassification(lowerCamelCase )
				__magic_name__						:						Dict										=AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
				__magic_name__						:						str										=image_size
				__magic_name__						:						List[str]										=torch.load(lowerCamelCase						,  map_location=torch.device("""cpu""" ) )

				__magic_name__						:						int										=OrderedDict()
				__magic_name__						:						Optional[Any]										=[]

				for idx in range(len(config.depth ) ):
								if config.cls_token[idx]:
												__magic_name__						:						str										=list_of_state_dict + cls_token(lowerCamelCase )
								__magic_name__						:						List[str]										=list_of_state_dict + embeddings(lowerCamelCase )
								for cnt in range(config.depth[idx] ):
												__magic_name__						:						Optional[Any]										=list_of_state_dict + attention(lowerCamelCase						,  lowerCamelCase )

				__magic_name__						:						List[Any]										=list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(lowerCamelCase )
				for i in range(len(lowerCamelCase ) ):
								__magic_name__						:						Optional[int]										=original_weights[list_of_state_dict[i][1]]

				model.load_state_dict(lowerCamelCase )
				model.save_pretrained(lowerCamelCase )
				image_processor.save_pretrained(lowerCamelCase )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
							UpperCAmelCase_		:					Union[str, Any]          =  argparse.ArgumentParser()
							parser.add_argument(
							    "--cvt_model",
							    default="cvt-w24",
							    type=str,
							    help="Name of the cvt model you'd like to convert.",
							)
							parser.add_argument(
							    "--image_size",
							    default=384,
							    type=int,
							    help="Input Image Size",
							)
							parser.add_argument(
							    "--cvt_file_name",
							    default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
							    type=str,
							    help="Input Image Size",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)

							UpperCAmelCase_		:					Optional[int]          =  parser.parse_args()
							convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)










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 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       KandinskyInpaintPipeline
						UpperCamelCase      =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						UpperCamelCase      =       [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						UpperCamelCase      =       [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase      =       False

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=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=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """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,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										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      A__    (		self :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[Any]										={
										    """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      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

										__magic_name__						:						int										=image[0, -3:, -3:, -1]
										__magic_name__						:						str										=image_from_tuple[0, -3:, -3:, -1]

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

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

										__magic_name__						:						Optional[Any]										=np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]			)

										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      A__    (		self :Dict			):
										'''simple docstring'''

										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""			)

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

										__magic_name__						:						Dict										=KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""     ,			torch_dtype=torch.floataa			)
										__magic_name__						:						int										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)




