



"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      List[Any] 						=     SwinConfig.from_pretrained(
		    "microsoft/swin-tiny-patch4-window7-224", out_features=["stage1", "stage2", "stage3", "stage4"]	)
		a_     :      List[str] 						=     MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE__	)

		a_     :      Dict 						=     "huggingface/label-files"
		if "ade20k-full" in model_name:
				# this should be ok
				a_     :      List[str] 						=     847
				a_     :      Optional[Any] 						=     "maskformer-ade20k-full-id2label.json"
		elif "ade" in model_name:
				# this should be ok
				a_     :      List[str] 						=     150
				a_     :      Tuple 						=     "ade20k-id2label.json"
		elif "coco-stuff" in model_name:
				# this should be ok
				a_     :      Union[str, Any] 						=     171
				a_     :      Union[str, Any] 						=     "maskformer-coco-stuff-id2label.json"
		elif "coco" in model_name:
				# TODO
				a_     :      Optional[Any] 						=     133
				a_     :      List[str] 						=     "coco-panoptic-id2label.json"
		elif "cityscapes" in model_name:
				# this should be ok
				a_     :      Union[str, Any] 						=     19
				a_     :      Any 						=     "cityscapes-id2label.json"
		elif "vistas" in model_name:
				# this should be ok
				a_     :      int 						=     65
				a_     :      Any 						=     "mapillary-vistas-id2label.json"

		a_     :      List[str] 						=     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, repo_type="dataset"	), "r"	)	)
		a_     :      Optional[Any] 						=     {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}

		return config



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		a_     :      str 						=     []
		# stem
		# fmt: off
		rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")	)
		rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")	)
		rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")	)
		rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")	)
		# stages
		for i in range(len(config.backbone_config.depths	)	):
				for j in range(config.backbone_config.depths[i]	):
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")	)

				if i < 3:
						rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")	)
						rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")	)
				rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")	)
				rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")	)

		# FPN
		rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")	)
		rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")	)
		rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")	)
		for source_index, target_index in zip(range(3, 0, -1	), range(0, 3	)	):
				rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")	)
				rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")	)
				rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")	)
				rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")	)
				rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")	)
				rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")	)
		rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")	)
		rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")	)

		# Transformer decoder
		for idx in range(config.decoder_config.decoder_layers	):
				# self-attention out projection
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")	)
				# cross-attention out projection
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")	)
				# MLP 1
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")	)
				# MLP 2
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")	)
				# layernorm 1 (self-attention layernorm)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")	)
				# layernorm 2 (cross-attention layernorm)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")	)
				# layernorm 3 (final layernorm)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")	)

		rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")	)
		rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")	)

		# heads on top
		rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")	)

		rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")	)
		rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")	)

		rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")	)
		rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")	)

		for i in range(3	):
				rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")	)
				rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")	)
		# fmt: on

		return rename_keys



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Optional[int] 						=     dct.pop(SCREAMING_SNAKE_CASE__	)
		a_     :      List[Any] 						=     val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		a_     :      int 						=     [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
		for i in range(len(backbone_config.depths	)	):
				a_     :      Optional[int] 						=     num_features[i]
				for j in range(backbone_config.depths[i]	):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						a_     :      List[str] 						=     state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
						a_     :      str 						=     state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""	)
						# next, add query, keys and values (in that order) to the state dict
						a_     :      Tuple 						=     in_proj_weight[:dim, :]
						a_     :      Union[str, Any] 						=     in_proj_bias[: dim]
						a_     :      Dict 						=     in_proj_weight[
						    dim : dim * 2, :
						]
						a_     :      Tuple 						=     in_proj_bias[
						    dim : dim * 2
						]
						a_     :      Optional[int] 						=     in_proj_weight[
						    -dim :, :
						]
						a_     :      str 						=     in_proj_bias[-dim :]
						# fmt: on



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
		# fmt: off
		a_     :      List[str] 						=     config.decoder_config.hidden_size
		for idx in range(config.decoder_config.decoder_layers	):
				# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
				a_     :      Optional[int] 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""	)
				a_     :      int 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""	)
				# next, add query, keys and values (in that order) to the state dict
				a_     :      int 						=     in_proj_weight[: hidden_size, :]
				a_     :      Tuple 						=     in_proj_bias[:config.hidden_size]
				a_     :      List[Any] 						=     in_proj_weight[hidden_size : hidden_size * 2, :]
				a_     :      Dict 						=     in_proj_bias[hidden_size : hidden_size * 2]
				a_     :      Optional[int] 						=     in_proj_weight[-hidden_size :, :]
				a_     :      List[str] 						=     in_proj_bias[-hidden_size :]
				# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
				a_     :      Union[str, Any] 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""	)
				a_     :      str 						=     state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""	)
				# next, add query, keys and values (in that order) to the state dict
				a_     :      Dict 						=     in_proj_weight[: hidden_size, :]
				a_     :      Optional[Any] 						=     in_proj_bias[:config.hidden_size]
				a_     :      List[Any] 						=     in_proj_weight[hidden_size : hidden_size * 2, :]
				a_     :      str 						=     in_proj_bias[hidden_size : hidden_size * 2]
				a_     :      List[Any] 						=     in_proj_weight[-hidden_size :, :]
				a_     :      Dict 						=     in_proj_bias[-hidden_size :]
		# fmt: on



def        lowerCAmelCase_						(							)			->      torch.Tensor:
		a_     :      Any 						=     "http://images.cocodataset.org/val2017/000000039769.jpg"
		a_     :      Optional[Any] 						=     Image.open(requests.get(SCREAMING_SNAKE_CASE__, stream=SCREAMING_SNAKE_CASE__	).raw	)
		return im



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False	)			->      Optional[int]:
		a_     :      List[Any] 						=     get_maskformer_config(SCREAMING_SNAKE_CASE__	)

		# load original state_dict
		with open(SCREAMING_SNAKE_CASE__, "rb"	) as f:
				a_     :      int 						=     pickle.load(SCREAMING_SNAKE_CASE__	)
		a_     :      Optional[int] 						=     data["model"]

		# for name, param in state_dict.items():
		#     print(name, param.shape)

		# rename keys
		a_     :      List[Any] 						=     create_rename_keys(SCREAMING_SNAKE_CASE__	)
		for src, dest in rename_keys:
				rename_key(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__, config.backbone_config	)
		read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# update to torch tensors
		for key, value in state_dict.items():
				a_     :      Tuple 						=     torch.from_numpy(SCREAMING_SNAKE_CASE__	)

		# load 🤗 model
		a_     :      Tuple 						=     MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__	)
		model.eval()

		for name, param in model.named_parameters():
				print(SCREAMING_SNAKE_CASE__, param.shape	)

		a_						,				a_     :      int 						=     model.load_state_dict(SCREAMING_SNAKE_CASE__, strict=SCREAMING_SNAKE_CASE__	)
		assert missing_keys == [
		    "model.pixel_level_module.encoder.model.layernorm.weight",
		    "model.pixel_level_module.encoder.model.layernorm.bias",
		]
		assert len(SCREAMING_SNAKE_CASE__	) == 0, F"""Unexpected keys: {unexpected_keys}"""

		# verify results
		a_     :      List[Any] 						=     prepare_img()
		if "vistas" in model_name:
				a_     :      int 						=     65
		elif "cityscapes" in model_name:
				a_     :      str 						=     65_535
		else:
				a_     :      int 						=     255
		a_     :      List[str] 						=     True if "ade" in model_name else False
		a_     :      Any 						=     MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE__, reduce_labels=SCREAMING_SNAKE_CASE__	)

		a_     :      int 						=     image_processor(SCREAMING_SNAKE_CASE__, return_tensors="pt"	)

		a_     :      Optional[int] 						=     model(**SCREAMING_SNAKE_CASE__	)

		print("Logits:", outputs.class_queries_logits[0, :3, :3]	)

		if model_name == "maskformer-swin-tiny-ade":
				a_     :      Union[str, Any] 						=     torch.tensor(
				    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]	)
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3], SCREAMING_SNAKE_CASE__, atol=1e-4	)
		print("Looks ok!"	)

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""	)
				Path(SCREAMING_SNAKE_CASE__	).mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
				model.save_pretrained(SCREAMING_SNAKE_CASE__	)
				image_processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

		if push_to_hub:
				print("Pushing model and image processor to the hub..."	)
				model.push_to_hub(F"""nielsr/{model_name}"""	)
				image_processor.push_to_hub(F"""nielsr/{model_name}"""	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""maskformer-swin-tiny-ade""",
					    type=str,
					    help=("""Name of the MaskFormer model you'd like to convert""",),
					)
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
					    type=str,
					    help="""Path to the original state dict (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)




"""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, KandinskyImgaImgPipeline, 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_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					KandinskyImgaImgPipeline
							__lowerCAmelCase    					=					["prompt", "image_embeds", "negative_image_embeds", "image"]
							__lowerCAmelCase    					=					[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							__lowerCAmelCase    					=					[
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							__lowerCAmelCase    					=					False






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim






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






							@property
							def snake_case_  (   self      ):
									return 1_0_0






							@property
							def snake_case_  (   self      ):
									a_     :      List[str] 						=     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
									return tokenizer






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      List[Any] 						=     MCLIPConfig(
									    numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=3_7			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1_0_0_5			,     )

									a_     :      str 						=     MultilingualCLIP(a_      )
									a_     :      Any 						=     text_encoder.eval()

									return text_encoder






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )

									a_     :      Union[str, Any] 						=     {
									    "in_channels": 4,
									    # 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,
									}

									a_     :      Dict 						=     UNetaDConditionModel(**a_      )
									return model






							@property
							def snake_case_  (   self      ):
									return {
									    "block_out_channels": [3_2, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      int 						=     VQModel(**self.dummy_movq_kwargs      )
									return model






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.dummy_text_encoder
									a_     :      Dict 						=     self.dummy_tokenizer
									a_     :      Optional[int] 						=     self.dummy_unet
									a_     :      Dict 						=     self.dummy_movq

									a_     :      List[str] 						=     {
									    "num_train_timesteps": 1_0_0_0,
									    "beta_schedule": "linear",
									    "beta_start": 0.00_085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									a_     :      List[Any] 						=     DDIMScheduler(**a_      )

									a_     :      Union[str, Any] 						=     {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
									a_     :      Optional[int] 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Tuple 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(a_      )
									# create init_image
									a_     :      Optional[int] 						=     floats_tensor((1, 3, 6_4, 6_4)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Any 						=     image.cpu().permute(0			,     2			,     3			,     1      )[0]
									a_     :      int 						=     Image.fromarray(np.uinta(a_      )      ).convert("RGB"      ).resize((2_5_6, 2_5_6)      )

									if str(a_      ).startswith("mps"      ):
											a_     :      Any 						=     torch.manual_seed(a_      )
									else:
											a_     :      Any 						=     torch.Generator(device=a_      ).manual_seed(a_      )
									a_     :      List[Any] 						=     {
									    "prompt": "horse",
									    "image": init_image,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 6_4,
									    "width": 6_4,
									    "num_inference_steps": 1_0,
									    "guidance_scale": 7.0,
									    "strength": 0.2,
									    "output_type": "np",
									}
									return inputs






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "cpu"

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

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

									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Union[str, Any] 						=     pipe(**self.get_dummy_inputs(a_      )      )
									a_     :      Any 						=     output.images

									a_     :      str 						=     pipe(
									    **self.get_dummy_inputs(a_      )			,     return_dict=a_			,     )[0]

									a_     :      List[str] 						=     image[0, -3:, -3:, -1]
									a_     :      Optional[Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									a_     :      Optional[int] 						=     np.array(
									    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]      )
									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				):






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






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_img2img_frog.npy"      )

									a_     :      Union[str, Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
									a_     :      Optional[Any] 						=     "A red cartoon frog, 4k"

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

									a_     :      int 						=     KandinskyImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1"			,     torch_dtype=torch.floataa      )
									a_     :      List[Any] 						=     pipeline.to(a_      )

									pipeline.set_progress_bar_config(disable=a_      )

									a_     :      int 						=     torch.Generator(device="cpu"      ).manual_seed(0      )
									a_						,				a_     :      Optional[int] 						=     pipe_prior(
									    a_			,     generator=a_			,     num_inference_steps=5			,     negative_prompt=""			,     ).to_tuple()

									a_     :      List[Any] 						=     pipeline(
									    a_			,     image=a_			,     image_embeds=a_			,     negative_image_embeds=a_			,     generator=a_			,     num_inference_steps=1_0_0			,     height=7_6_8			,     width=7_6_8			,     strength=0.2			,     output_type="np"			,     )

									a_     :      int 						=     output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(a_			,     a_      )