

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()
A_			:					Union[str, Any]    			=     logging.get_logger(__name__)

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

	return new_state_dict

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					Any   )	-> List[Any]:
	# 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)
			A__				:       Optional[Any]	  =						state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""   )
			A__				:       Dict	  =						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__				:       Any	  =						kv_weight[
			    : config.hidden_sizes[i], :
			]
			A__				:       Any	  =						kv_bias[: config.hidden_sizes[i]]
			A__				:       Dict	  =						kv_weight[
			    config.hidden_sizes[i] :, :
			]
			A__				:       List[Any]	  =						kv_bias[
			    config.hidden_sizes[i] :
			]

def 		UpperCamelCase   ()	-> int:
	A__				:       List[str]	  =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__				:       Dict	  =						Image.open(requests.get(lowercase_       , stream=lowercase_   ).raw   )

	return image

@torch.no_grad()
def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Union[str, Any]       , lowercase_:					Dict   )	-> Tuple:
	A__				:       Optional[int]	  =						SegformerConfig()
	A__				:       Any	  =						False

	# set attributes based on model_name
	A__				:       str	  =						"""huggingface/label-files"""
	if "segformer" in model_name:
		A__				:       Dict	  =						model_name[len("""segformer."""   ) : len("""segformer."""   ) + 2]
		if "ade" in model_name:
			A__				:       Tuple	  =						150
			A__				:       List[str]	  =						"""ade20k-id2label.json"""
			A__				:       str	  =						(1, 150, 128, 128)
		elif "city" in model_name:
			A__				:       Optional[int]	  =						19
			A__				:       str	  =						"""cityscapes-id2label.json"""
			A__				:       Optional[Any]	  =						(1, 19, 128, 128)
		else:
			raise ValueError(f"""Model {model_name} not supported"""   )
	elif "mit" in model_name:
		A__				:       Dict	  =						True
		A__				:       List[str]	  =						model_name[4:6]
		A__				:       Tuple	  =						1000
		A__				:       Optional[int]	  =						"""imagenet-1k-id2label.json"""
		A__				:       List[Any]	  =						(1, 1000)
	else:
		raise ValueError(f"""Model {model_name} not supported"""   )

	# set config attributes
	A__				:       str	  =						json.load(open(hf_hub_download(lowercase_       , lowercase_       , repo_type="""dataset"""   )       , """r"""   )   )
	A__				:       Union[str, Any]	  =						{int(lowercase_   ): v for k, v in idalabel.items()}
	A__				:       Optional[Any]	  =						idalabel
	A__				:       Any	  =						{v: k for k, v in idalabel.items()}
	if size == "b0":
		pass
	elif size == "b1":
		A__				:       Dict	  =						[64, 128, 320, 512]
		A__				:       Optional[int]	  =						256
	elif size == "b2":
		A__				:       List[Any]	  =						[64, 128, 320, 512]
		A__				:       int	  =						768
		A__				:       int	  =						[3, 4, 6, 3]
	elif size == "b3":
		A__				:       Tuple	  =						[64, 128, 320, 512]
		A__				:       Dict	  =						768
		A__				:       Tuple	  =						[3, 4, 18, 3]
	elif size == "b4":
		A__				:       Optional[int]	  =						[64, 128, 320, 512]
		A__				:       List[Any]	  =						768
		A__				:       int	  =						[3, 8, 27, 3]
	elif size == "b5":
		A__				:       str	  =						[64, 128, 320, 512]
		A__				:       Any	  =						768
		A__				:       List[Any]	  =						[3, 6, 40, 3]
	else:
		raise ValueError(f"""Size {size} not supported"""   )

	# load image processor (only resize + normalize)
	A__				:       List[Any]	  =						SegformerImageProcessor(
	    image_scale=(512, 512)       , keep_ratio=lowercase_       , align=lowercase_       , do_random_crop=lowercase_   )

	# prepare image
	A__				:       str	  =						prepare_img()
	A__				:       Union[str, Any]	  =						image_processor(images=lowercase_       , return_tensors="""pt"""   ).pixel_values

	logger.info(f"""Converting model {model_name}..."""   )

	# load original state dict
	if encoder_only:
		A__				:       Optional[Any]	  =						torch.load(lowercase_       , map_location=torch.device("""cpu"""   )   )
	else:
		A__				:       List[str]	  =						torch.load(lowercase_       , map_location=torch.device("""cpu"""   )   )["""state_dict"""]

	# rename keys
	A__				:       Union[str, Any]	  =						rename_keys(lowercase_       , encoder_only=lowercase_   )
	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(lowercase_       , lowercase_   )

	# create HuggingFace model and load state dict
	if encoder_only:
		A__				:       int	  =						False
		A__				:       Dict	  =						SegformerForImageClassification(lowercase_   )
	else:
		A__				:       Tuple	  =						SegformerForSemanticSegmentation(lowercase_   )
	model.load_state_dict(lowercase_   )
	model.eval()

	# forward pass
	A__				:       str	  =						model(lowercase_   )
	A__				:       Union[str, Any]	  =						outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		A__				:       Union[str, Any]	  =						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__				:       Dict	  =						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__				:       Dict	  =						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__				:       int	  =						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__				:       int	  =						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__				:       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":
		A__				:       Dict	  =						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__				:       str	  =						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__				:       Optional[int]	  =						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__				:       List[str]	  =						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__				:       List[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":
		A__				:       Optional[int]	  =						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__				:       Dict	  =						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__				:       Optional[int]	  =						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__				:       List[str]	  =						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__				:       Dict	  =						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]       , lowercase_       , atol=1E-2   )

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


if __name__ == "__main__":
	A_			:					str    			=     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.'
	)
	A_			:					int    			=     parser.parse_args()
	convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp
	from flax.jax_utils import replicate
	from flax.training.common_utils import shard

	from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline



@require_flax
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						with tempfile.TemporaryDirectory() as tmpdirname:
							# pipeline has Flax weights
							A__				:       Optional[int]	  =						FlaxDiffusionPipeline.from_pretrained(
							    """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,			safety_checker=A__       ,			cache_dir=A__     )

							A__				:       int	  =						[t[-1] for t in os.walk(os.path.join(A__       ,			os.listdir(A__     )[0]       ,			"""snapshots"""     )     )]
							A__				:       str	  =						[item for sublist in all_root_files for item in sublist]

							# None of the downloaded files should be a PyTorch file even if we have some here:
							# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
							assert not any(f.endswith(""".bin"""     ) for f in files     )



@slow
@require_flax
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__  ,  A__				:       Any	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,			safety_checker=A__     )

						A__				:       Optional[int]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Tuple	  =						jax.random.PRNGKey(0     )
						A__				:       int	  =						4

						A__				:       Optional[Any]	  =						jax.device_count()
						A__				:       Union[str, Any]	  =						num_samples * [prompt]
						A__				:       str	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       str	  =						replicate(A__     )
						A__				:       Any	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[Any]	  =						shard(A__     )

						A__				:       int	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 64, 64, 3)
						if jax.device_count() == 8:
							assert np.abs(np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 4.1_5_1_4_7_4_5     ) < 1e-3
							assert np.abs(np.abs(A__       ,			dtype=np.floataa     ).sum() - 4_9_9_4_7.8_7_5     ) < 5e-1

						A__				:       str	  =						pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]     )     )     )
						assert len(A__     ) == num_samples






					def     __A     (					self     ):
						A__  ,  A__				:       Any	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""flax"""       ,			safety_checker=A__     )

						A__				:       Tuple	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Tuple	  =						jax.random.PRNGKey(0     )
						A__				:       Optional[Any]	  =						50

						A__				:       List[str]	  =						jax.device_count()
						A__				:       Dict	  =						num_samples * [prompt]
						A__				:       Union[str, Any]	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       int	  =						replicate(A__     )
						A__				:       List[str]	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[Any]	  =						shard(A__     )

						A__				:       Optional[Any]	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_5_6_5_2_4_0_1)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_8_3_8_0_8.2)     ) < 5e-1






					def     __A     (					self     ):
						A__  ,  A__				:       int	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			safety_checker=A__     )

						A__				:       Any	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Dict	  =						jax.random.PRNGKey(0     )
						A__				:       str	  =						50

						A__				:       Any	  =						jax.device_count()
						A__				:       List[Any]	  =						num_samples * [prompt]
						A__				:       Any	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       Any	  =						replicate(A__     )
						A__				:       str	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[Any]	  =						shard(A__     )

						A__				:       Dict	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_4_0_0_3_9_0_6)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_7_3_5_1_6.7_5)     ) < 5e-1






					def     __A     (					self     ):
						A__  ,  A__				:       Tuple	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa     )

						A__				:       List[str]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       int	  =						jax.random.PRNGKey(0     )
						A__				:       Optional[int]	  =						50

						A__				:       Optional[Any]	  =						jax.device_count()
						A__				:       Optional[int]	  =						num_samples * [prompt]
						A__				:       Optional[int]	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       Optional[int]	  =						replicate(A__     )
						A__				:       Tuple	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[int]	  =						shard(A__     )

						A__				:       Optional[int]	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_4_0_0_3_9_0_6)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_7_3_5_1_6.7_5)     ) < 5e-1






					def     __A     (					self     ):
						A__				:       int	  =						FlaxDDIMScheduler(
						    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule="""scaled_linear"""       ,			set_alpha_to_one=A__       ,			steps_offset=1       ,			)

						A__  ,  A__				:       Tuple	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			scheduler=A__       ,			safety_checker=A__       ,			)
						A__				:       Union[str, Any]	  =						scheduler.create_state()

						A__				:       Optional[Any]	  =						scheduler_state

						A__				:       Union[str, Any]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Tuple	  =						jax.random.PRNGKey(0     )
						A__				:       List[Any]	  =						50

						A__				:       Optional[int]	  =						jax.device_count()
						A__				:       Dict	  =						num_samples * [prompt]
						A__				:       List[Any]	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       Tuple	  =						replicate(A__     )
						A__				:       Dict	  =						jax.random.split(A__       ,			A__     )
						A__				:       Dict	  =						shard(A__     )

						A__				:       int	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_4_5_0_4_3_9_4_5)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_4_7_6_9_3.5)     ) < 5e-1






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Optional[int]	  =						jax.device_count()
						A__				:       str	  =						num_samples * [prompt]
						A__				:       Any	  =						jax.random.split(jax.random.PRNGKey(0     )       ,			A__     )

						A__  ,  A__				:       Optional[Any]	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			safety_checker=A__       ,			)

						A__				:       Tuple	  =						replicate(A__     )
						A__				:       Dict	  =						pipeline.prepare_inputs(A__     )
						A__				:       str	  =						shard(A__     )
						A__				:       Tuple	  =						pipeline(A__       ,			A__       ,			A__       ,			jit=A__     ).images
						assert images.shape == (num_samples, 1, 512, 512, 3)
						A__				:       Optional[Any]	  =						images[2, 0, 256, 10:17, 1]

						# With memory efficient attention
						A__  ,  A__				:       Any	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			safety_checker=A__       ,			use_memory_efficient_attention=A__       ,			)

						A__				:       Any	  =						replicate(A__     )
						A__				:       Optional[int]	  =						pipeline.prepare_inputs(A__     )
						A__				:       Dict	  =						shard(A__     )
						A__				:       Tuple	  =						pipeline(A__       ,			A__       ,			A__       ,			jit=A__     ).images
						assert images_eff.shape == (num_samples, 1, 512, 512, 3)
						A__				:       Union[str, Any]	  =						images[2, 0, 256, 10:17, 1]

						# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
						# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
						assert abs(slice_eff - slice     ).max() < 1e-2



