
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case				:   List[Any]     =				logging.get_logger(__name__)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= DPTConfig()

						if "large" in checkpoint_url:
												_SCREAMING_SNAKE_CASE				= 1024
												_SCREAMING_SNAKE_CASE				= 4096
												_SCREAMING_SNAKE_CASE				= 24
												_SCREAMING_SNAKE_CASE				= 16
												_SCREAMING_SNAKE_CASE				= [5, 11, 17, 23]
												_SCREAMING_SNAKE_CASE				= [256, 512, 1024, 1024]
												_SCREAMING_SNAKE_CASE				= (1, 384, 384)

						if "ade" in checkpoint_url:
												_SCREAMING_SNAKE_CASE				= True

												_SCREAMING_SNAKE_CASE				= 150
												_SCREAMING_SNAKE_CASE				= "huggingface/label-files"
												_SCREAMING_SNAKE_CASE				= "ade20k-id2label.json"
												_SCREAMING_SNAKE_CASE				= json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			repo_type="dataset"  )  )	,			"r"  )  )
												_SCREAMING_SNAKE_CASE				= {int(_SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}
												_SCREAMING_SNAKE_CASE				= idalabel
												_SCREAMING_SNAKE_CASE				= {v: k for k, v in idalabel.items()}
												_SCREAMING_SNAKE_CASE				= [1, 150, 480, 480]

						return config, expected_shape



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple  ):
						_SCREAMING_SNAKE_CASE				= ["pretrained.model.head.weight", "pretrained.model.head.bias"]
						for k in ignore_keys:
												state_dict.pop(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict  ):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.model"	,			"dpt.encoder"  )
						if "pretrained.model" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.model"	,			"dpt.embeddings"  )
						if "patch_embed" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("patch_embed"	,			"patch_embeddings"  )
						if "pos_embed" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pos_embed"	,			"position_embeddings"  )
						if "attn.proj" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("attn.proj"	,			"attention.output.dense"  )
						if "proj" in name and "project" not in name:
												_SCREAMING_SNAKE_CASE				= name.replace("proj"	,			"projection"  )
						if "blocks" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("blocks"	,			"layer"  )
						if "mlp.fc1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("mlp.fc1"	,			"intermediate.dense"  )
						if "mlp.fc2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("mlp.fc2"	,			"output.dense"  )
						if "norm1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("norm1"	,			"layernorm_before"  )
						if "norm2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("norm2"	,			"layernorm_after"  )
						if "scratch.output_conv" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("scratch.output_conv"	,			"head"  )
						if "scratch" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("scratch"	,			"neck"  )
						if "layer1_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer1_rn"	,			"convs.0"  )
						if "layer2_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer2_rn"	,			"convs.1"  )
						if "layer3_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer3_rn"	,			"convs.2"  )
						if "layer4_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer4_rn"	,			"convs.3"  )
						if "refinenet" in name:
												_SCREAMING_SNAKE_CASE				= int(name[len("neck.refinenet"  ) : len("neck.refinenet"  ) + 1]  )
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												_SCREAMING_SNAKE_CASE				= name.replace(F'refinenet{layer_idx}'	,			F'fusion_stage.layers.{abs(layer_idx-4  )}'  )
						if "out_conv" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("out_conv"	,			"projection"  )
						if "resConfUnit1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("resConfUnit1"	,			"residual_layer1"  )
						if "resConfUnit2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("resConfUnit2"	,			"residual_layer2"  )
						if "conv1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("conv1"	,			"convolution1"  )
						if "conv2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("conv2"	,			"convolution2"  )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess1.0.project.0"	,			"neck.reassemble_stage.readout_projects.0.0"  )
						if "pretrained.act_postprocess2.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess2.0.project.0"	,			"neck.reassemble_stage.readout_projects.1.0"  )
						if "pretrained.act_postprocess3.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess3.0.project.0"	,			"neck.reassemble_stage.readout_projects.2.0"  )
						if "pretrained.act_postprocess4.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess4.0.project.0"	,			"neck.reassemble_stage.readout_projects.3.0"  )
						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess1.3"	,			"neck.reassemble_stage.layers.0.projection"  )
						if "pretrained.act_postprocess1.4" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess1.4"	,			"neck.reassemble_stage.layers.0.resize"  )
						if "pretrained.act_postprocess2.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess2.3"	,			"neck.reassemble_stage.layers.1.projection"  )
						if "pretrained.act_postprocess2.4" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess2.4"	,			"neck.reassemble_stage.layers.1.resize"  )
						if "pretrained.act_postprocess3.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess3.3"	,			"neck.reassemble_stage.layers.2.projection"  )
						if "pretrained.act_postprocess4.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess4.3"	,			"neck.reassemble_stage.layers.3.projection"  )
						if "pretrained.act_postprocess4.4" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess4.4"	,			"neck.reassemble_stage.layers.3.resize"  )
						if "pretrained" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained"	,			"dpt"  )
						if "bn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("bn"	,			"batch_norm"  )
						if "head" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("head"	,			"head.head"  )
						if "encoder.norm" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("encoder.norm"	,			"layernorm"  )
						if "auxlayer" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("auxlayer"	,			"auxiliary_head.head"  )

						return name



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Dict  ):
						for i in range(config.num_hidden_layers  ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												_SCREAMING_SNAKE_CASE				= state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'  )
												_SCREAMING_SNAKE_CASE				= state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'  )
												# next, add query, keys and values (in that order) to the state dict
												_SCREAMING_SNAKE_CASE				= in_proj_weight[: config.hidden_size, :]
												_SCREAMING_SNAKE_CASE				= in_proj_bias[: config.hidden_size]
												_SCREAMING_SNAKE_CASE				= in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_SCREAMING_SNAKE_CASE				= in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_SCREAMING_SNAKE_CASE				= in_proj_weight[
												    -config.hidden_size :, :
												]
												_SCREAMING_SNAKE_CASE				= in_proj_bias[-config.hidden_size :]



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= "http://images.cocodataset.org/val2017/000000039769.jpg"
						_SCREAMING_SNAKE_CASE				= Image.open(requests.get(_SCREAMING_SNAKE_CASE	,			stream=_SCREAMING_SNAKE_CASE  ).raw  )
						return im



@torch.no_grad()
def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   Any  ):
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= get_dpt_config(_SCREAMING_SNAKE_CASE  )
						# load original state_dict from URL
						_SCREAMING_SNAKE_CASE				= torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,			map_location="cpu"  )
						# remove certain keys
						remove_ignore_keys_(_SCREAMING_SNAKE_CASE  )
						# rename keys
						for key in state_dict.copy().keys():
												_SCREAMING_SNAKE_CASE				= state_dict.pop(_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= val
						# read in qkv matrices
						read_in_q_k_v(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						# load HuggingFace model
						_SCREAMING_SNAKE_CASE				= DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE  ) if "ade" in checkpoint_url else DPTForDepthEstimation(_SCREAMING_SNAKE_CASE  )
						model.load_state_dict(_SCREAMING_SNAKE_CASE  )
						model.eval()

						# Check outputs on an image
						_SCREAMING_SNAKE_CASE				= 480 if "ade" in checkpoint_url else 384
						_SCREAMING_SNAKE_CASE				= DPTImageProcessor(size=_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= prepare_img()
						_SCREAMING_SNAKE_CASE				= image_processor(_SCREAMING_SNAKE_CASE	,			return_tensors="pt"  )

						# forward pass
						_SCREAMING_SNAKE_CASE				= model(**_SCREAMING_SNAKE_CASE  ).logits if "ade" in checkpoint_url else model(**_SCREAMING_SNAKE_CASE  ).predicted_depth

						# Assert logits
						_SCREAMING_SNAKE_CASE				= torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]  )
						if "ade" in checkpoint_url:
												_SCREAMING_SNAKE_CASE				= torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]  )
						assert outputs.shape == torch.Size(_SCREAMING_SNAKE_CASE  )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]	,			_SCREAMING_SNAKE_CASE	,			atol=1e-4  )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]	,			_SCREAMING_SNAKE_CASE  )
						)

						Path(_SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE  )
						print(F'Saving model to {pytorch_dump_folder_path}'  )
						model.save_pretrained(_SCREAMING_SNAKE_CASE  )
						print(F'Saving image processor to {pytorch_dump_folder_path}'  )
						image_processor.save_pretrained(_SCREAMING_SNAKE_CASE  )

						if push_to_hub:
												print("Pushing model to hub..."  )
												model.push_to_hub(
												    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )	,			organization="nielsr"	,			commit_message="Add model"	,			use_temp_dir=_SCREAMING_SNAKE_CASE	,			)
												image_processor.push_to_hub(
												    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )	,			organization="nielsr"	,			commit_message="Add image processor"	,			use_temp_dir=_SCREAMING_SNAKE_CASE	,			)


if __name__ == "__main__":
						_snake_case				:   List[Any]     =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
						    type=str,
						    help="""URL of the original DPT checkpoint you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument(
						    """--push_to_hub""",
						    action="""store_true""",
						)
						parser.add_argument(
						    """--model_name""",
						    default="""dpt-large""",
						    type=str,
						    help="""Name of the model, in case you're pushing to the hub.""",
						)

						_snake_case				:   Union[str, Any]     =				parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

'''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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					KandinskyImgaImgPipeline
		a							:					Union[str, Any]       =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a							:					Union[str, Any]       =					[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a							:					int       =					[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a							:					Tuple       =					False




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return 32




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




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




		@property
		def     lowercase       (				self      ):
								return 100




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
								return tokenizer




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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					,						)

								_SCREAMING_SNAKE_CASE				= MultilingualCLIP(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= text_encoder.eval()

								return text_encoder




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

								_SCREAMING_SNAKE_CASE				= {
								    "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,
								}

								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(**UpperCamelCase      )
								return model




		@property
		def     lowercase       (				self      ):
								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     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= VQModel(**self.dummy_movq_kwargs      )
								return model




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.dummy_text_encoder
								_SCREAMING_SNAKE_CASE				= self.dummy_tokenizer
								_SCREAMING_SNAKE_CASE				= self.dummy_unet
								_SCREAMING_SNAKE_CASE				= self.dummy_movq

								_SCREAMING_SNAKE_CASE				= {
								    "num_train_timesteps": 1_000,
								    "beta_schedule": "linear",
								    "beta_start": 0.0_00_85,
								    "beta_end": 0.0_12,
								    "clip_sample": False,
								    "set_alpha_to_one": False,
								    "steps_offset": 0,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								}

								_SCREAMING_SNAKE_CASE				= DDIMScheduler(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}

								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(seed + 1      )      ).to(UpperCamelCase      )
								# create init_image
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image.cpu().permute(0					,						2					,						3					,						1      )[0]
								_SCREAMING_SNAKE_CASE				= Image.fromarray(np.uinta(UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )

								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "prompt": "horse",
								    "image": init_image,
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "generator": generator,
								    "height": 64,
								    "width": 64,
								    "num_inference_steps": 10,
								    "guidance_scale": 7.0,
								    "strength": 0.2,
								    "output_type": "np",
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cpu"

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()

								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe.to(UpperCamelCase      )

								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**self.get_dummy_inputs(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= output.images

								_SCREAMING_SNAKE_CASE				= pipe(
								    **self.get_dummy_inputs(UpperCamelCase      )					,						return_dict=UpperCamelCase					,						)[0]

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

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

								_SCREAMING_SNAKE_CASE				= np.array(
								    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]      )
								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 			lowerCAmelCase (				unittest.TestCase			):




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_img2img_frog.npy"      )

								_SCREAMING_SNAKE_CASE				= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
								_SCREAMING_SNAKE_CASE				= "A red cartoon frog, 4k"

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

								_SCREAMING_SNAKE_CASE				= KandinskyImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1"					,						torch_dtype=torch.floataa      )
								_SCREAMING_SNAKE_CASE				= pipeline.to(UpperCamelCase      )

								pipeline.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.Generator(device="cpu"      ).manual_seed(0      )
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe_prior(
								    UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=5					,						negative_prompt=""					,						).to_tuple()

								_SCREAMING_SNAKE_CASE				= pipeline(
								    UpperCamelCase					,						image=UpperCamelCase					,						image_embeds=UpperCamelCase					,						negative_image_embeds=UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						strength=0.2					,						output_type="np"					,						)

								_SCREAMING_SNAKE_CASE				= output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(UpperCamelCase					,						UpperCamelCase      )