
'''simple docstring'''
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()
lowercase__											=logging.get_logger(__name__)






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

	return new_state_dict






def      UpperCamelCase_				(      A__  ,      A__						):





	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)
			a_      				=						state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''						)
			a_      				=						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
			a_      				=						kv_weight[
			    : config.hidden_sizes[i], :
			]
			a_      				=						kv_bias[: config.hidden_sizes[i]]
			a_      				=						kv_weight[
			    config.hidden_sizes[i] :, :
			]
			a_      				=						kv_bias[
			    config.hidden_sizes[i] :
			]






def      UpperCamelCase_				(      ):
	a_      				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	a_      				=						Image.open(requests.get(UpperCamelCase__  ,      stream=UpperCamelCase__						).raw						)

	return image






@torch.no_grad()
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						SegformerConfig()
	a_      				=						False

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

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

	# load image processor (only resize + normalize)
	a_      				=						SegformerImageProcessor(
	    image_scale=(5_12, 5_12)  ,      keep_ratio=UpperCamelCase__  ,      align=UpperCamelCase__  ,      do_random_crop=UpperCamelCase__						)

	# prepare image
	a_      				=						prepare_img()
	a_      				=						image_processor(images=UpperCamelCase__  ,      return_tensors="""pt"""						).pixel_values

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

	# load original state dict
	if encoder_only:
		a_      				=						torch.load(UpperCamelCase__  ,      map_location=torch.device("""cpu"""						)						)
	else:
		a_      				=						torch.load(UpperCamelCase__  ,      map_location=torch.device("""cpu"""						)						)['''state_dict''']

	# rename keys
	a_      				=						rename_keys(UpperCamelCase__  ,      encoder_only=UpperCamelCase__						)
	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(UpperCamelCase__  ,      UpperCamelCase__						)

	# create HuggingFace model and load state dict
	if encoder_only:
		a_      				=						False
		a_      				=						SegformerForImageClassification(UpperCamelCase__						)
	else:
		a_      				=						SegformerForSemanticSegmentation(UpperCamelCase__						)
	model.load_state_dict(UpperCamelCase__						)
	model.eval()

	# forward pass
	a_      				=						model(UpperCamelCase__						)
	a_      				=						outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						torch.tensor(
		    [
		        [
		            [-1.13_72e01, -1.27_87e01, -1.34_77e01],
		            [-1.25_36e01, -1.41_94e01, -1.44_09e01],
		            [-1.32_17e01, -1.48_88e01, -1.53_27e01],
		        ],
		        [
		            [-1.47_91e01, -1.71_22e01, -1.82_77e01],
		            [-1.71_63e01, -1.91_92e01, -1.95_33e01],
		            [-1.78_97e01, -1.99_91e01, -2.03_15e01],
		        ],
		        [
		            [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
		            [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
		            [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
		        ],
		    ]						)
	elif model_name == "segformer.b0.768x768.city.160k":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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":
		a_      				=						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:
		a_      				=						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]  ,      UpperCamelCase__  ,      atol=1e-2						)

	# finally, save model and image processor
	logger.info(F'''Saving PyTorch 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 __name__ == "__main__":
		lowercase__											=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.'
		)
		lowercase__											=parser.parse_args()
		convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 				a			(      __lowercase   ,__lowercase   ,__lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     StableUnCLIPPipeline
				SCREAMING_SNAKE_CASE__     :   str		      =     TEXT_TO_IMAGE_PARAMS
				SCREAMING_SNAKE_CASE__     :   int		      =     TEXT_TO_IMAGE_BATCH_PARAMS
				SCREAMING_SNAKE_CASE__     :   Dict		      =     TEXT_TO_IMAGE_IMAGE_PARAMS
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				SCREAMING_SNAKE_CASE__     :   int		      =     False






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   32
										__SCREAMING_SNAKE_CASE:				List[Any]           =   embedder_hidden_size

										# prior components

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Dict           =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				int           =   CLIPTextModelWithProjection(
										    CLIPTextConfig(
										        bos_token_id=0			, eos_token_id=2			, hidden_size=_lowerCAmelCase			, projection_dim=_lowerCAmelCase			, intermediate_size=37			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1000			, )					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   PriorTransformer(
										    num_attention_heads=2			, attention_head_dim=12			, embedding_dim=_lowerCAmelCase			, num_layers=1			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   DDPMScheduler(
										    variance_type='''fixed_small_log'''			, prediction_type='''sample'''			, num_train_timesteps=1000			, clip_sample=_lowerCAmelCase			, clip_sample_range=5.0			, beta_schedule='''squaredcos_cap_v2'''			, )

										# regular denoising components

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   CLIPTextModel(
										    CLIPTextConfig(
										        bos_token_id=0			, eos_token_id=2			, hidden_size=_lowerCAmelCase			, projection_dim=32			, intermediate_size=37			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1000			, )					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   UNetaDConditionModel(
										    sample_size=32			, in_channels=4			, out_channels=4			, down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')			, up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')			, block_out_channels=(32, 64)			, attention_head_dim=(2, 4)			, class_embed_type='''projection'''			, projection_class_embeddings_input_dim=embedder_projection_dim * 2			, cross_attention_dim=_lowerCAmelCase			, layers_per_block=1			, upcast_attention=_lowerCAmelCase			, use_linear_projection=_lowerCAmelCase			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   DDIMScheduler(
										    beta_schedule='''scaled_linear'''			, beta_start=0.00085			, beta_end=0.012			, prediction_type='''v_prediction'''			, set_alpha_to_one=_lowerCAmelCase			, steps_offset=1			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				int           =   AutoencoderKL()

										__SCREAMING_SNAKE_CASE:				Dict           =   {
										    # prior components
										    '''prior_tokenizer''': prior_tokenizer,
										    '''prior_text_encoder''': prior_text_encoder,
										    '''prior''': prior,
										    '''prior_scheduler''': prior_scheduler,
										    # image noising components
										    '''image_normalizer''': image_normalizer,
										    '''image_noising_scheduler''': image_noising_scheduler,
										    # regular denoising components
										    '''tokenizer''': tokenizer,
										    '''text_encoder''': text_encoder,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										}

										return components






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=0					):

										"""simple docstring"""




										if str(_lowerCAmelCase					).startswith('''mps'''					):
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.manual_seed(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''prior_num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}
										return inputs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   torch_device == '''cpu'''

										self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   torch_device in ['''cpu''', '''mps''']

										self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase					)





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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''			, torch_dtype=torch.floataa					)
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
										# stable unclip will oom when integration tests are run on a V100,
										# so turn on memory savings
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										__SCREAMING_SNAKE_CASE:				Tuple           =   torch.Generator(device='''cpu'''					).manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe('''anime turle'''			, generator=_lowerCAmelCase			, output_type='''np'''					)

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

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

										assert_mean_pixel_difference(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										__SCREAMING_SNAKE_CASE:				int           =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''			, torch_dtype=torch.floataa					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										__SCREAMING_SNAKE_CASE:				Dict           =   pipe(
										    '''anime turtle'''			, prior_num_inference_steps=2			, num_inference_steps=2			, output_type='''np'''			, )

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.cuda.max_memory_allocated()
										# make sure that less than 7 GB is allocated
										assert mem_bytes < 7 * 10**9



