
import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_						: Dict         =  logging.get_logger(__name__)







def        A__					(						lowerCamelCase     ,    lowerCamelCase=False						)  ->	List[str]:
				UpperCamelCase_:							Optional[Any]	=						OrderedDict()
				for key, value in state_dict.items():
								if encoder_only and not key.startswith("""head"""						):
												UpperCamelCase_:							int	=						"""segformer.encoder.""" + key
								if key.startswith("""backbone"""						):
												UpperCamelCase_:							str	=						key.replace("""backbone"""     ,    """segformer.encoder"""						)
								if "patch_embed" in key:
												# replace for example patch_embed1 by patch_embeddings.0
												UpperCamelCase_:							List[Any]	=						key[key.find("""patch_embed"""						) + len("""patch_embed"""						)]
												UpperCamelCase_:							Any	=						key.replace(F'''patch_embed{idx}'''     ,    F'''patch_embeddings.{int(lowerCamelCase						)-1}'''						)
								if "norm" in key:
												UpperCamelCase_:							List[Any]	=						key.replace("""norm"""     ,    """layer_norm"""						)
								if "segformer.encoder.layer_norm" in key:
												# replace for example layer_norm1 by layer_norm.0
												UpperCamelCase_:							Optional[Any]	=						key[key.find("""segformer.encoder.layer_norm"""						) + len("""segformer.encoder.layer_norm"""						)]
												UpperCamelCase_:							Dict	=						key.replace(F'''layer_norm{idx}'''     ,    F'''layer_norm.{int(lowerCamelCase						)-1}'''						)
								if "layer_norm1" in key:
												UpperCamelCase_:							Tuple	=						key.replace("""layer_norm1"""     ,    """layer_norm_1"""						)
								if "layer_norm2" in key:
												UpperCamelCase_:							List[Any]	=						key.replace("""layer_norm2"""     ,    """layer_norm_2"""						)
								if "block" in key:
												# replace for example block1 by block.0
												UpperCamelCase_:							List[str]	=						key[key.find("""block"""						) + len("""block"""						)]
												UpperCamelCase_:							Optional[Any]	=						key.replace(F'''block{idx}'''     ,    F'''block.{int(lowerCamelCase						)-1}'''						)
								if "attn.q" in key:
												UpperCamelCase_:							int	=						key.replace("""attn.q"""     ,    """attention.self.query"""						)
								if "attn.proj" in key:
												UpperCamelCase_:							int	=						key.replace("""attn.proj"""     ,    """attention.output.dense"""						)
								if "attn" in key:
												UpperCamelCase_:							List[Any]	=						key.replace("""attn"""     ,    """attention.self"""						)
								if "fc1" in key:
												UpperCamelCase_:							Dict	=						key.replace("""fc1"""     ,    """dense1"""						)
								if "fc2" in key:
												UpperCamelCase_:							Any	=						key.replace("""fc2"""     ,    """dense2"""						)
								if "linear_pred" in key:
												UpperCamelCase_:							Any	=						key.replace("""linear_pred"""     ,    """classifier"""						)
								if "linear_fuse" in key:
												UpperCamelCase_:							str	=						key.replace("""linear_fuse.conv"""     ,    """linear_fuse"""						)
												UpperCamelCase_:							Tuple	=						key.replace("""linear_fuse.bn"""     ,    """batch_norm"""						)
								if "linear_c" in key:
												# replace for example linear_c4 by linear_c.3
												UpperCamelCase_:							List[str]	=						key[key.find("""linear_c"""						) + len("""linear_c"""						)]
												UpperCamelCase_:							Any	=						key.replace(F'''linear_c{idx}'''     ,    F'''linear_c.{int(lowerCamelCase						)-1}'''						)
								if key.startswith("""head"""						):
												UpperCamelCase_:							Union[str, Any]	=						key.replace("""head"""     ,    """classifier"""						)
								UpperCamelCase_:							Optional[int]	=						value

				return new_state_dict







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Tuple:
				# for each of the encoder blocks:
				for i in range(config.num_encoder_blocks						):
								for j in range(config.depths[i]						):
												# read in weights + bias of keys and values (which is a single matrix in the original implementation)
												UpperCamelCase_:							Any	=						state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''						)
												UpperCamelCase_:							Optional[int]	=						state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias'''						)
												# next, add keys and values (in that order) to the state dict
												UpperCamelCase_:							Optional[Any]	=						kv_weight[
												    : config.hidden_sizes[i], :
												]
												UpperCamelCase_:							int	=						kv_bias[: config.hidden_sizes[i]]
												UpperCamelCase_:							str	=						kv_weight[
												    config.hidden_sizes[i] :, :
												]
												UpperCamelCase_:							Any	=						kv_bias[
												    config.hidden_sizes[i] :
												]







def        A__					(						)  ->	Tuple:
				UpperCamelCase_:							Dict	=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:							Tuple	=						Image.open(requests.get(lowerCamelCase     ,    stream=lowerCamelCase						).raw						)

				return image







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Dict:
				UpperCamelCase_:							Tuple	=						SegformerConfig()
				UpperCamelCase_:							Optional[int]	=						False

				# set attributes based on model_name
				UpperCamelCase_:							List[str]	=						"""huggingface/label-files"""
				if "segformer" in model_name:
								UpperCamelCase_:							int	=						model_name[len("""segformer."""						) : len("""segformer."""						) + 2]
								if "ade" in model_name:
												UpperCamelCase_:							Optional[Any]	=						1_50
												UpperCamelCase_:							str	=						"""ade20k-id2label.json"""
												UpperCamelCase_:							Any	=						(1, 1_50, 1_28, 1_28)
								elif "city" in model_name:
												UpperCamelCase_:							Union[str, Any]	=						19
												UpperCamelCase_:							int	=						"""cityscapes-id2label.json"""
												UpperCamelCase_:							int	=						(1, 19, 1_28, 1_28)
								else:
												raise ValueError(F'''Model {model_name} not supported'''						)
				elif "mit" in model_name:
								UpperCamelCase_:							int	=						True
								UpperCamelCase_:							Union[str, Any]	=						model_name[4:6]
								UpperCamelCase_:							Optional[int]	=						10_00
								UpperCamelCase_:							Tuple	=						"""imagenet-1k-id2label.json"""
								UpperCamelCase_:							List[str]	=						(1, 10_00)
				else:
								raise ValueError(F'''Model {model_name} not supported'''						)

				# set config attributes
				UpperCamelCase_:							List[Any]	=						json.load(open(hf_hub_download(lowerCamelCase     ,    lowerCamelCase     ,    repo_type="""dataset"""						)     ,    """r"""						)						)
				UpperCamelCase_:							List[str]	=						{int(lowerCamelCase						): v for k, v in idalabel.items()}
				UpperCamelCase_:							Union[str, Any]	=						idalabel
				UpperCamelCase_:							List[Any]	=						{v: k for k, v in idalabel.items()}
				if size == "b0":
								pass
				elif size == "b1":
								UpperCamelCase_:							Any	=						[64, 1_28, 3_20, 5_12]
								UpperCamelCase_:							List[str]	=						2_56
				elif size == "b2":
								UpperCamelCase_:							Union[str, Any]	=						[64, 1_28, 3_20, 5_12]
								UpperCamelCase_:							int	=						7_68
								UpperCamelCase_:							Any	=						[3, 4, 6, 3]
				elif size == "b3":
								UpperCamelCase_:							Tuple	=						[64, 1_28, 3_20, 5_12]
								UpperCamelCase_:							Union[str, Any]	=						7_68
								UpperCamelCase_:							Union[str, Any]	=						[3, 4, 18, 3]
				elif size == "b4":
								UpperCamelCase_:							Optional[Any]	=						[64, 1_28, 3_20, 5_12]
								UpperCamelCase_:							Union[str, Any]	=						7_68
								UpperCamelCase_:							Union[str, Any]	=						[3, 8, 27, 3]
				elif size == "b5":
								UpperCamelCase_:							str	=						[64, 1_28, 3_20, 5_12]
								UpperCamelCase_:							str	=						7_68
								UpperCamelCase_:							Union[str, Any]	=						[3, 6, 40, 3]
				else:
								raise ValueError(F'''Size {size} not supported'''						)

				# load image processor (only resize + normalize)
				UpperCamelCase_:							str	=						SegformerImageProcessor(
				    image_scale=(5_12, 5_12)     ,    keep_ratio=lowerCamelCase     ,    align=lowerCamelCase     ,    do_random_crop=lowerCamelCase						)

				# prepare image
				UpperCamelCase_:							int	=						prepare_img()
				UpperCamelCase_:							Optional[Any]	=						image_processor(images=lowerCamelCase     ,    return_tensors="""pt"""						).pixel_values

				logger.info(F'''Converting model {model_name}...'''						)

				# load original state dict
				if encoder_only:
								UpperCamelCase_:							Tuple	=						torch.load(lowerCamelCase     ,    map_location=torch.device("""cpu"""						)						)
				else:
								UpperCamelCase_:							str	=						torch.load(lowerCamelCase     ,    map_location=torch.device("""cpu"""						)						)["""state_dict"""]

				# rename keys
				UpperCamelCase_:							List[Any]	=						rename_keys(lowerCamelCase     ,    encoder_only=lowerCamelCase						)
				if not encoder_only:
								del state_dict["decode_head.conv_seg.weight"]
								del state_dict["decode_head.conv_seg.bias"]

				# key and value matrices need special treatment
				read_in_k_v(lowerCamelCase     ,    lowerCamelCase						)

				# create HuggingFace model and load state dict
				if encoder_only:
								UpperCamelCase_:							Optional[int]	=						False
								UpperCamelCase_:							Optional[Any]	=						SegformerForImageClassification(lowerCamelCase						)
				else:
								UpperCamelCase_:							Tuple	=						SegformerForSemanticSegmentation(lowerCamelCase						)
				model.load_state_dict(lowerCamelCase						)
				model.eval()

				# forward pass
				UpperCamelCase_:							Any	=						model(lowerCamelCase						)
				UpperCamelCase_:							Any	=						outputs.logits

				# set expected_slice based on model name
				# ADE20k checkpoints
				if model_name == "segformer.b0.512x512.ade.160k":
								UpperCamelCase_:							Dict	=						torch.tensor(
								    [
								        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
								        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
								        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
								    ]						)
				elif model_name == "segformer.b1.512x512.ade.160k":
								UpperCamelCase_:							Union[str, Any]	=						torch.tensor(
								    [
								        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
								        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
								        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
								    ]						)
				elif model_name == "segformer.b2.512x512.ade.160k":
								UpperCamelCase_:							str	=						torch.tensor(
								    [
								        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
								        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
								        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
								    ]						)
				elif model_name == "segformer.b3.512x512.ade.160k":
								UpperCamelCase_:							Optional[Any]	=						torch.tensor(
								    [
								        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
								        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
								        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
								    ]						)
				elif model_name == "segformer.b4.512x512.ade.160k":
								UpperCamelCase_:							List[str]	=						torch.tensor(
								    [
								        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
								        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
								        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
								    ]						)
				elif model_name == "segformer.b5.640x640.ade.160k":
								UpperCamelCase_:							Optional[int]	=						torch.tensor(
								    [
								        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
								        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
								        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
								    ]						)
				# Cityscapes checkpoints
				elif model_name == "segformer.b0.1024x1024.city.160k":
								UpperCamelCase_:							Optional[Any]	=						torch.tensor(
								    [
								        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
								        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
								        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
								    ]						)
				elif model_name == "segformer.b0.512x1024.city.160k":
								UpperCamelCase_:							int	=						torch.tensor(
								    [
								        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
								        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
								        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
								    ]						)
				elif model_name == "segformer.b0.640x1280.city.160k":
								UpperCamelCase_:							Dict	=						torch.tensor(
								    [
								        [
								            [-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
								            [-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
								            [-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
								        ],
								        [
								            [-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
								            [-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
								            [-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
								        ],
								        [
								            [7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
								            [4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
								            [3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
								        ],
								    ]						)
				elif model_name == "segformer.b0.768x768.city.160k":
								UpperCamelCase_:							List[Any]	=						torch.tensor(
								    [
								        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
								        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
								        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
								    ]						)
				elif model_name == "segformer.b1.1024x1024.city.160k":
								UpperCamelCase_:							Any	=						torch.tensor(
								    [
								        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
								        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
								        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
								    ]						)
				elif model_name == "segformer.b2.1024x1024.city.160k":
								UpperCamelCase_:							Any	=						torch.tensor(
								    [
								        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
								        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
								        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
								    ]						)
				elif model_name == "segformer.b3.1024x1024.city.160k":
								UpperCamelCase_:							Optional[Any]	=						torch.tensor(
								    [
								        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
								        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
								        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
								    ]						)
				elif model_name == "segformer.b4.1024x1024.city.160k":
								UpperCamelCase_:							Optional[Any]	=						torch.tensor(
								    [
								        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
								        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
								        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
								    ]						)
				elif model_name == "segformer.b5.1024x1024.city.160k":
								UpperCamelCase_:							Any	=						torch.tensor(
								    [
								        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
								        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
								        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
								    ]						)
				else:
								UpperCamelCase_:							Optional[Any]	=						logits.argmax(-1						).item()
								print("""Predicted class:"""     ,    model.config.idalabel[predicted_class_idx]						)

				# verify logits
				if not encoder_only:
								assert logits.shape == expected_shape
								assert torch.allclose(logits[0, :3, :3, :3]     ,    lowerCamelCase     ,    atol=1E-2						)

				# finally, save model and image processor
				logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''						)
				Path(lowerCamelCase						).mkdir(exist_ok=lowerCamelCase						)
				model.save_pretrained(lowerCamelCase						)
				image_processor.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Dict         =  argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    default="""segformer.b0.512x512.ade.160k""",
							    type=str,
							    help="""Name of the model you'd like to convert.""",
							)
							parser.add_argument(
							    """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							lowerCamelCase_						: str         =  parser.parse_args()
							convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			@property
			def       lowerCAmelCase__	(       self :       int      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Any	=						UNetaDModel(
							    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=3						,	out_channels=3						,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Union[str, Any]	=						VQModel(
							    block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,	latent_channels=3						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Any      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							List[str]	=						CLIPTextConfig(
							    bos_token_id=0						,	eos_token_id=2						,	hidden_size=32						,	intermediate_size=37						,	layer_norm_eps=1e-05						,	num_attention_heads=4						,	num_hidden_layers=5						,	pad_token_id=1						,	vocab_size=1000						,	)
							return CLIPTextModel(snake_case_      )




			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Union[str, Any]	=						self.dummy_uncond_unet
							UpperCamelCase_:							Optional[Any]	=						DDIMScheduler()
							UpperCamelCase_:							List[str]	=						self.dummy_vq_model

							UpperCamelCase_:							List[Any]	=						LDMPipeline(unet=snake_case_						,	vqvae=snake_case_						,	scheduler=snake_case_      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							str	=						torch.manual_seed(0      )
							UpperCamelCase_:							int	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							Dict	=						torch.manual_seed(0      )
							UpperCamelCase_:							str	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""						,	return_dict=snake_case_      )[0]

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Optional[int]	=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase_:							str	=						np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]      )
							UpperCamelCase_:							Optional[Any]	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < tolerance




@slow
@require_torch
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Dict	=						LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							List[str]	=						torch.manual_seed(0      )
							UpperCamelCase_:							Optional[int]	=						ldm(generator=snake_case_						,	num_inference_steps=5						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							UpperCamelCase_:							List[str]	=						np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]      )
							UpperCamelCase_:							Dict	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance






