




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DeiTImageProcessor





class 		A__				(   unittest.TestCase ):






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase=7				,     lowerCamelCase=3				,     lowerCamelCase=18				,     lowerCamelCase=30				,     lowerCamelCase=400				,     lowerCamelCase=True				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=[0.5, 0.5, 0.5]				,     lowerCamelCase=[0.5, 0.5, 0.5]				,     )      ->   List[Any]:




						"""simple docstring"""






						__magic_name__					:							Any		   =							size if size is not None else {'''height''': 20, '''width''': 20}
						__magic_name__					:							List[str]		   =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

						__magic_name__					:							Union[str, Any]		   =							parent
						__magic_name__					:							int		   =							batch_size
						__magic_name__					:							Optional[Any]		   =							num_channels
						__magic_name__					:							Any		   =							image_size
						__magic_name__					:							Optional[Any]		   =							min_resolution
						__magic_name__					:							Any		   =							max_resolution
						__magic_name__					:							int		   =							do_resize
						__magic_name__					:							Optional[Any]		   =							size
						__magic_name__					:							Optional[Any]		   =							do_center_crop
						__magic_name__					:							Dict		   =							crop_size
						__magic_name__					:							int		   =							do_normalize
						__magic_name__					:							Tuple		   =							image_mean
						__magic_name__					:							Tuple		   =							image_std






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}





@require_torch
@require_vision
class 		A__				(   __SCREAMING_SNAKE_CASE		,     unittest.TestCase ):
	lowerCamelCase__		: Optional[int]				    =DeiTImageProcessor if is_vision_available() else None
	lowerCamelCase__		: List[Any]				    =True






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							DeiTImageProcessingTester(self						)






	@property
	def  lowercase      (	self						)      ->   List[Any]:




						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()






	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_resize'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''size'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''center_crop'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''do_normalize'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''image_mean'''						)						)
						self.assertTrue(hasattr(lowerCamelCase				,     '''image_std'''						)						)






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size				,     {'''height''': 20, '''width''': 20}						)
						self.assertEqual(image_processor.crop_size				,     {'''height''': 18, '''width''': 18}						)

						__magic_name__					:							str		   =							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84						)
						self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}						)
						self.assertEqual(image_processor.crop_size				,     {'''height''': 84, '''width''': 84}						)






	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						pass






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




						"""simple docstring"""






						__magic_name__					:							Dict		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						__magic_name__					:							Any		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowerCamelCase						)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase				,     Image.Image						)

						# Test not batched input
						__magic_name__					:							List[Any]		   =							image_processing(image_inputs[0]				,     return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape				,     (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )

						# Test batched
						__magic_name__					:							Optional[int]		   =							image_processing(lowerCamelCase				,     return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape				,     (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__magic_name__					:							Tuple		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowerCamelCase				,     numpify=lowerCamelCase						)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase				,     np.ndarray						)

						# Test not batched input
						__magic_name__					:							List[str]		   =							image_processing(image_inputs[0]				,     return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape				,     (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )

						# Test batched
						__magic_name__					:							Any		   =							image_processing(lowerCamelCase				,     return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape				,     (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )






	def  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__magic_name__					:							int		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowerCamelCase				,     torchify=lowerCamelCase						)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase				,     torch.Tensor						)

						# Test not batched input
						__magic_name__					:							Optional[Any]		   =							image_processing(image_inputs[0]				,     return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape				,     (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )

						# Test batched
						__magic_name__					:							Union[str, Any]		   =							image_processing(lowerCamelCase				,     return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape				,     (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )				,     )




"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					={
						    # norm1
						    F'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[F'''{resnet_prefix}.main.0.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[F'''{resnet_prefix}.main.0.mapper.bias'''],
						    # conv1
						    F'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[F'''{resnet_prefix}.main.2.weight'''],
						    F'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[F'''{resnet_prefix}.main.2.bias'''],
						    # norm2
						    F'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[F'''{resnet_prefix}.main.4.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[F'''{resnet_prefix}.main.4.mapper.bias'''],
						    # conv2
						    F'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[F'''{resnet_prefix}.main.6.weight'''],
						    F'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[F'''{resnet_prefix}.main.6.bias'''],
						}

						if resnet.conv_shortcut is not None:
												rv.update(
												    {
												        F'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[F'''{resnet_prefix}.skip.weight'''],
												    }				)

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					={
						    # norm
						    F'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[F'''{attention_prefix}.norm_in.mapper.weight'''],
						    F'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[F'''{attention_prefix}.norm_in.mapper.bias'''],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
						    # to_k
						    F'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[F'''{attention_prefix}.out_proj.weight''']
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[F'''{attention_prefix}.out_proj.bias'''],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2   ,    dim=0				)

						lowerCamelCase__					={
						    # norm2 (ada groupnorm)
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.bias'''
						    ],
						    # layernorm on encoder_hidden_state
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
						        F'''{attention_prefix}.norm_enc.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
						        F'''{attention_prefix}.norm_enc.bias'''
						    ],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
						        F'''{attention_prefix}.q_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
						        F'''{attention_prefix}.q_proj.bias'''
						    ],
						    # to_k
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
						        F'''{attention_prefix}.out_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
						        F'''{attention_prefix}.out_proj.bias'''
						    ],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					="inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
						lowerCamelCase__					=F'''{block_prefix}.{block_idx}'''

						lowerCamelCase__					={}

						if not hasattr(__lowerCAmelCase   ,    "attentions"				):
												lowerCamelCase__					=1  # resnet only
						elif not block.attentions[0].add_self_attention:
												lowerCamelCase__					=2  # resnet -> cross-attention
						else:
												lowerCamelCase__					=3  # resnet -> self-attention -> cross-attention)

						for resnet_idx, resnet in enumerate(block.resnets				):
												# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
												lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
												lowerCamelCase__					=n * resnet_idx if block_type == "up" else n * resnet_idx + 1
												lowerCamelCase__					=F'''{block_prefix}.{idx}''' if block_type == "up" else F'''{block_prefix}.{idx}'''

												diffusers_checkpoint.update(
												    resnet_to_diffusers_checkpoint(
												        __lowerCAmelCase   ,    __lowerCAmelCase   ,    diffusers_resnet_prefix=__lowerCAmelCase   ,    resnet_prefix=__lowerCAmelCase				)				)

						if hasattr(__lowerCAmelCase   ,    "attentions"				):
												for attention_idx, attention in enumerate(block.attentions				):
																		lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
																		lowerCamelCase__					=n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
																		lowerCamelCase__					=F'''{block_prefix}.{idx}'''
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''
																		lowerCamelCase__					=1 if not attention.add_self_attention else 2
																		lowerCamelCase__					=(
																		    n * attention_idx + cross_attention_index
																		    if block_type == "up"
																		    else n * attention_idx + cross_attention_index + 1
																		)
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''

																		diffusers_checkpoint.update(
																		    cross_attn_to_diffusers_checkpoint(
																		        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    diffusers_attention_index=2   ,    attention_prefix=__lowerCAmelCase   ,    )				)

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    attention_prefix=__lowerCAmelCase   ,    )				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					={}

						# pre-processing
						diffusers_checkpoint.update(
						    {
						        "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
						        "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
						    }				)

						# timestep and class embedding
						diffusers_checkpoint.update(
						    {
						        "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1				),
						        "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
						        "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
						        "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
						        "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
						        "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
						    }				)

						# down_blocks
						for down_block_idx, down_block in enumerate(model.down_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "down"				)				)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "up"				)				)

						# post-processing
						diffusers_checkpoint.update(
						    {
						        "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
						        "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
						    }				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					=original_config["input_channels"] + original_config["unet_cond_dim"]
						lowerCamelCase__					=original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

						lowerCamelCase__					=original_config["channels"]

						assert (
						    len(set(original_config["depths"]				)				) == 1
						), "UNet2DConditionModel currently do not support blocks with different number of layers"
						lowerCamelCase__					=original_config["depths"][0]

						lowerCamelCase__					=original_config["mapping_cond_dim"]
						lowerCamelCase__					=original_config["cross_cond_dim"]

						lowerCamelCase__					=[]
						lowerCamelCase__					=[]
						for s, c in zip(original_config["self_attn_depths"]   ,    original_config["cross_attn_depths"]				):
												if s:
																		lowerCamelCase__					="self"
																		lowerCamelCase__					="cross" if c else None
												elif c:
																		lowerCamelCase__					="cross"
																		lowerCamelCase__					=None
												else:
																		lowerCamelCase__					=None
																		lowerCamelCase__					=None
												attna_types.append(__lowerCAmelCase				)
												attna_types.append(__lowerCAmelCase				)

						lowerCamelCase__					=UNetaDConditionModel(
						    in_channels=__lowerCAmelCase   ,    out_channels=__lowerCAmelCase   ,    down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")   ,    mid_block_type=__lowerCAmelCase   ,    up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")   ,    block_out_channels=__lowerCAmelCase   ,    layers_per_block=__lowerCAmelCase   ,    act_fn="gelu"   ,    norm_num_groups=__lowerCAmelCase   ,    cross_attention_dim=__lowerCAmelCase   ,    attention_head_dim=64   ,    time_cond_proj_dim=__lowerCAmelCase   ,    resnet_time_scale_shift="scale_shift"   ,    time_embedding_type="fourier"   ,    timestep_post_act="gelu"   ,    conv_in_kernel=1   ,    conv_out_kernel=1   ,    )

						return unet





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					=torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

						lowerCamelCase__					=huggingface_hub.hf_hub_download(__lowerCAmelCase   ,    "config_laion_text_cond_latent_upscaler_2.json"				)
						lowerCamelCase__					=huggingface_hub.hf_hub_download(
						    __lowerCAmelCase   ,    "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"				)
						print(F'''loading original model configuration from {orig_config_path}'''				)
						print(F'''loading original model checkpoint from {orig_weights_path}'''				)

						print("converting to diffusers unet"				)
						lowerCamelCase__					=K.config.load_config(open(__lowerCAmelCase				)				)["model"]
						lowerCamelCase__					=unet_model_from_original_config(__lowerCAmelCase				)

						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location=__lowerCAmelCase				)["model_ema"]
						lowerCamelCase__					=unet_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase				)

						model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase				)
						model.save_pretrained(args.dump_path				)
						print(F'''saving converted unet model in {args.dump_path}'''				)


if __name__ == "__main__":
	a							=argparse.ArgumentParser()

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	a							=parser.parse_args()

	main(args)


