





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()
UpperCAmelCase_  							=				logging.get_logger(__name__)




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

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

				UpperCamelCase__							:       Union[str, Any]      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
				UpperCamelCase__							:       Optional[Any]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}

				return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]   )   ->			Optional[Any]:
				UpperCamelCase__							:       Union[str, Any]      =   []
				# 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_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    Optional[Any]   )   ->			int:
				UpperCamelCase__							:       List[Any]      =   dct.pop(__UpperCAmelCase   )
				UpperCamelCase__							:       Tuple      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    str   )   ->			List[str]:
				UpperCamelCase__							:       Optional[Any]      =   [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
				for i in range(len(backbone_config.depths   )   ):
								UpperCamelCase__							:       List[str]      =   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)
												UpperCamelCase__							:       Any      =   state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"   )
												UpperCamelCase__							:       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
												UpperCamelCase__							:       Any      =   in_proj_weight[:dim, :]
												UpperCamelCase__							:       Tuple      =   in_proj_bias[: dim]
												UpperCamelCase__							:       List[str]      =   in_proj_weight[
												    dim : dim * 2, :
												]
												UpperCamelCase__							:       List[Any]      =   in_proj_bias[
												    dim : dim * 2
												]
												UpperCamelCase__							:       Dict      =   in_proj_weight[
												    -dim :, :
												]
												UpperCamelCase__							:       Optional[Any]      =   in_proj_bias[-dim :]
												# fmt: on




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]   )   ->			Any:
				# fmt: off
				UpperCamelCase__							:       List[Any]      =   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)
								UpperCamelCase__							:       Union[str, Any]      =   state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"   )
								UpperCamelCase__							:       str      =   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
								UpperCamelCase__							:       List[Any]      =   in_proj_weight[: hidden_size, :]
								UpperCamelCase__							:       Dict      =   in_proj_bias[:config.hidden_size]
								UpperCamelCase__							:       Optional[Any]      =   in_proj_weight[hidden_size : hidden_size * 2, :]
								UpperCamelCase__							:       int      =   in_proj_bias[hidden_size : hidden_size * 2]
								UpperCamelCase__							:       int      =   in_proj_weight[-hidden_size :, :]
								UpperCamelCase__							:       Union[str, Any]      =   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)
								UpperCamelCase__							:       Tuple      =   state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"   )
								UpperCamelCase__							:       Optional[Any]      =   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
								UpperCamelCase__							:       Optional[int]      =   in_proj_weight[: hidden_size, :]
								UpperCamelCase__							:       List[Any]      =   in_proj_bias[:config.hidden_size]
								UpperCamelCase__							:       Optional[int]      =   in_proj_weight[hidden_size : hidden_size * 2, :]
								UpperCamelCase__							:       Dict      =   in_proj_bias[hidden_size : hidden_size * 2]
								UpperCamelCase__							:       Any      =   in_proj_weight[-hidden_size :, :]
								UpperCamelCase__							:       int      =   in_proj_bias[-hidden_size :]
				# fmt: on




def 	lowerCAmelCase_					(						)   ->			torch.Tensor:
				UpperCamelCase__							:       int      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCamelCase__							:       Tuple      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
				return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    bool = False   )   ->			Dict:
				UpperCamelCase__							:       Tuple      =   get_maskformer_config(__UpperCAmelCase   )

				# load original state_dict
				with open(__UpperCAmelCase	,     '''rb'''   ) as f:
								UpperCamelCase__							:       Tuple      =   pickle.load(__UpperCAmelCase   )
				UpperCamelCase__							:       Optional[Any]      =   data['''model''']

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

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

				# update to torch tensors
				for key, value in state_dict.items():
								UpperCamelCase__							:       Union[str, Any]      =   torch.from_numpy(__UpperCAmelCase   )

				# load 🤗 model
				UpperCamelCase__							:       Any      =   MaskFormerForInstanceSegmentation(__UpperCAmelCase   )
				model.eval()

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

				UpperCamelCase__							:       Optional[Any]      =   model.load_state_dict(__UpperCAmelCase	,     strict=__UpperCAmelCase   )
				assert missing_keys == [
				    "model.pixel_level_module.encoder.model.layernorm.weight",
				    "model.pixel_level_module.encoder.model.layernorm.bias",
				]
				assert len(__UpperCAmelCase   ) == 0, f"Unexpected keys: {unexpected_keys}"

				# verify results
				UpperCamelCase__							:       Any      =   prepare_img()
				if "vistas" in model_name:
								UpperCamelCase__							:       List[str]      =   65
				elif "cityscapes" in model_name:
								UpperCamelCase__							:       List[Any]      =   6_5535
				else:
								UpperCamelCase__							:       List[str]      =   255
				UpperCamelCase__							:       Dict      =   True if '''ade''' in model_name else False
				UpperCamelCase__							:       Optional[Any]      =   MaskFormerImageProcessor(ignore_index=__UpperCAmelCase	,     reduce_labels=__UpperCAmelCase   )

				UpperCamelCase__							:       Tuple      =   image_processor(__UpperCAmelCase	,     return_tensors='''pt'''   )

				UpperCamelCase__							:       List[Any]      =   model(**__UpperCAmelCase   )

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

				if model_name == "maskformer-swin-tiny-ade":
								UpperCamelCase__							:       Union[str, Any]      =   torch.tensor(
								    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]   )
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]	,     __UpperCAmelCase	,     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(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
								model.save_pretrained(__UpperCAmelCase   )
								image_processor.save_pretrained(__UpperCAmelCase   )

				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__":
		UpperCAmelCase_  							=				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.'
		)

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








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase_  							=				False






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   pass






@nightly
@require_torch_gpu
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       int      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:       Union[str, Any]      =   VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   generator.manual_seed(0							)
       UpperCamelCase__							:       str      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion''',	torch_dtype=torch.floataa							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[int]      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       Optional[int]      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=50,	output_type='''numpy'''							).images

       UpperCamelCase__							:       int      =   image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       int      =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2

