
"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_A            = logging.get_logger(__name__)


if is_vision_available():
					import PIL






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	['pixel_values']




	def __init__( self		, UpperCAmelCase_ = True		, UpperCAmelCase_ = None		, UpperCAmelCase_ = PILImageResampling.BILINEAR		, UpperCAmelCase_ = True		, UpperCAmelCase_ = False		, UpperCAmelCase_ = True		, UpperCAmelCase_ = True		, UpperCAmelCase_ = 1 / 255		, UpperCAmelCase_ = True		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, **UpperCAmelCase_		, )		->							None:
			super().__init__(**UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    size if size is not None else {'height': 2560, 'width': 1920}
			if isinstance(UpperCAmelCase_		, (tuple, list)					):
					# The previous feature extractor size parameter was in (width, height) format
					lowerCamelCase     :			str		=    size[::-1]
			lowerCamelCase     :			int		=    get_size_dict(UpperCAmelCase_					)

			lowerCamelCase     :			Union[str, Any]		=    do_resize
			lowerCamelCase     :			Union[str, Any]		=    size
			lowerCamelCase     :			Dict		=    resample
			lowerCamelCase     :			Optional[Any]		=    do_thumbnail
			lowerCamelCase     :			Any		=    do_align_long_axis
			lowerCamelCase     :			Optional[Any]		=    do_pad
			lowerCamelCase     :			Any		=    do_rescale
			lowerCamelCase     :			Any		=    rescale_factor
			lowerCamelCase     :			List[Any]		=    do_normalize
			lowerCamelCase     :			Optional[int]		=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCamelCase     :			str		=    image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = None					)		->							np.ndarray:
			lowerCamelCase	,    lowerCamelCase     :			Dict		=    get_image_size(UpperCAmelCase_					)
			lowerCamelCase	,    lowerCamelCase     :			Dict		=    size['height'], size['width']

			if (output_width < output_height and input_width > input_height) or (
			    output_width > output_height and input_width < input_height
			):
					lowerCamelCase     :			Union[str, Any]		=    np.rotaa(UpperCAmelCase_		, 3					)

			if data_format is not None:
					lowerCamelCase     :			Union[str, Any]		=    to_channel_dimension_format(UpperCAmelCase_		, UpperCAmelCase_					)

			return image




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = False		, UpperCAmelCase_ = None		, )		->							np.ndarray:
			lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    size['height'], size['width']
			lowerCamelCase	,    lowerCamelCase     :			List[str]		=    get_image_size(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    output_width - input_width
			lowerCamelCase     :			Any		=    output_height - input_height

			if random_padding:
					lowerCamelCase     :			Optional[Any]		=    np.random.randint(low=0		, high=delta_height + 1					)
					lowerCamelCase     :			Optional[int]		=    np.random.randint(low=0		, high=delta_width + 1					)
			else:
					lowerCamelCase     :			str		=    delta_height // 2
					lowerCamelCase     :			Optional[Any]		=    delta_width // 2

			lowerCamelCase     :			List[Any]		=    delta_height - pad_top
			lowerCamelCase     :			List[Any]		=    delta_width - pad_left

			lowerCamelCase     :			Optional[int]		=    ((pad_top, pad_bottom), (pad_left, pad_right))
			return pad(UpperCAmelCase_		, UpperCAmelCase_		, data_format=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, *UpperCAmelCase_		, **UpperCAmelCase_					)		->							Optional[Any]:
			logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'					)
			return self.pad_image(*UpperCAmelCase_		, **UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = PILImageResampling.BICUBIC		, UpperCAmelCase_ = None		, **UpperCAmelCase_		, )		->							np.ndarray:
			lowerCamelCase	,    lowerCamelCase     :			str		=    get_image_size(UpperCAmelCase_					)
			lowerCamelCase	,    lowerCamelCase     :			Any		=    size['height'], size['width']

			# We always resize to the smallest of either the input or output size.
			lowerCamelCase     :			str		=    min(UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    min(UpperCAmelCase_		, UpperCAmelCase_					)

			if height == input_height and width == input_width:
					return image

			if input_height > input_width:
					lowerCamelCase     :			str		=    int(input_width * height / input_height					)
			elif input_width > input_height:
					lowerCamelCase     :			Any		=    int(input_height * width / input_width					)

			return resize(
			    UpperCAmelCase_		, size=(height, width)		, resample=UpperCAmelCase_		, reducing_gap=2.0		, data_format=UpperCAmelCase_		, **UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = PILImageResampling.BICUBIC		, UpperCAmelCase_ = None		, **UpperCAmelCase_		, )		->							np.ndarray:
			lowerCamelCase     :			Union[str, Any]		=    get_size_dict(UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    min(size['height']		, size['width']					)
			lowerCamelCase     :			Optional[int]		=    get_resize_output_image_size(UpperCAmelCase_		, size=UpperCAmelCase_		, default_to_square=UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    resize(UpperCAmelCase_		, size=UpperCAmelCase_		, resample=UpperCAmelCase_		, data_format=UpperCAmelCase_		, **UpperCAmelCase_					)
			return resized_image




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = None		, **UpperCAmelCase_		, )		->							Optional[int]:
			return rescale(UpperCAmelCase_		, scale=UpperCAmelCase_		, data_format=UpperCAmelCase_		, **UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = None		, **UpperCAmelCase_		, )		->							np.ndarray:
			return normalize(UpperCAmelCase_		, mean=UpperCAmelCase_		, std=UpperCAmelCase_		, data_format=UpperCAmelCase_		, **UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = False		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = ChannelDimension.FIRST		, **UpperCAmelCase_		, )		->							PIL.Image.Image:
			lowerCamelCase     :			str		=    do_resize if do_resize is not None else self.do_resize
			lowerCamelCase     :			Optional[Any]		=    size if size is not None else self.size
			if isinstance(UpperCAmelCase_		, (tuple, list)					):
					# Previous feature extractor had size in (width, height) format
					lowerCamelCase     :			str		=    size[::-1]
			lowerCamelCase     :			Union[str, Any]		=    get_size_dict(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    resample if resample is not None else self.resample
			lowerCamelCase     :			Optional[int]		=    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
			lowerCamelCase     :			Dict		=    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
			lowerCamelCase     :			Union[str, Any]		=    do_pad if do_pad is not None else self.do_pad
			lowerCamelCase     :			Dict		=    do_rescale if do_rescale is not None else self.do_rescale
			lowerCamelCase     :			Dict		=    rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCamelCase     :			Union[str, Any]		=    do_normalize if do_normalize is not None else self.do_normalize
			lowerCamelCase     :			Union[str, Any]		=    image_mean if image_mean is not None else self.image_mean
			lowerCamelCase     :			Optional[Any]		=    image_std if image_std is not None else self.image_std

			lowerCamelCase     :			Dict		=    make_list_of_images(UpperCAmelCase_					)

			if not valid_images(UpperCAmelCase_					):
					raise ValueError(
					    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
					    'torch.Tensor, tf.Tensor or jax.ndarray.'					)

			if do_resize and size is None:
					raise ValueError('Size must be specified if do_resize is True.'					)

			if do_rescale and rescale_factor is None:
					raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

			if do_pad and size is None:
					raise ValueError('Size must be specified if do_pad is True.'					)

			if do_normalize and (image_mean is None or image_std is None):
					raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

			# All transformations expect numpy arrays.
			lowerCamelCase     :			List[str]		=    [to_numpy_array(UpperCAmelCase_					) for image in images]

			if do_align_long_axis:
					lowerCamelCase     :			int		=    [self.align_long_axis(UpperCAmelCase_		, size=UpperCAmelCase_					) for image in images]

			if do_resize:
					lowerCamelCase     :			List[Any]		=    [self.resize(image=UpperCAmelCase_		, size=UpperCAmelCase_		, resample=UpperCAmelCase_					) for image in images]

			if do_thumbnail:
					lowerCamelCase     :			List[str]		=    [self.thumbnail(image=UpperCAmelCase_		, size=UpperCAmelCase_					) for image in images]

			if do_pad:
					lowerCamelCase     :			Union[str, Any]		=    [self.pad_image(image=UpperCAmelCase_		, size=UpperCAmelCase_		, random_padding=UpperCAmelCase_					) for image in images]

			if do_rescale:
					lowerCamelCase     :			List[str]		=    [self.rescale(image=UpperCAmelCase_		, scale=UpperCAmelCase_					) for image in images]

			if do_normalize:
					lowerCamelCase     :			Optional[Any]		=    [self.normalize(image=UpperCAmelCase_		, mean=UpperCAmelCase_		, std=UpperCAmelCase_					) for image in images]

			lowerCamelCase     :			str		=    [to_channel_dimension_format(UpperCAmelCase_		, UpperCAmelCase_					) for image in images]

			lowerCamelCase     :			Optional[int]		=    {'pixel_values': images}
			return BatchFeature(data=UpperCAmelCase_		, tensor_type=UpperCAmelCase_					)







"""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)


