


'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__lowercase:		str    						=  "pcuenq/k-upscaler"



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Any     ,		*, _UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Tuple )		->       Optional[int]:





   '''simple docstring'''







   UpperCamelCase__			    =    {
       # 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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		*, _UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					int )		->       Any:





   '''simple docstring'''







   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    checkpoint[F'{attention_prefix}.qkv_proj.weight'].chunk(3     ,		dim=0 )
   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    checkpoint[F'{attention_prefix}.qkv_proj.bias'].chunk(3     ,		dim=0 )
   UpperCamelCase__			    =    {
       # 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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[str]     ,		*, _UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Tuple )		->       Any:





   '''simple docstring'''







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

   UpperCamelCase__			    =    {
       # 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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					int )		->       List[Any]:





   '''simple docstring'''







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

   UpperCamelCase__			    =    {}

   if not hasattr(_UpperCamelCase     ,		"attentions" ):
      UpperCamelCase__			    =    1  # resnet only
   elif not block.attentions[0].add_self_attention:
      UpperCamelCase__			    =    2  # resnet -> cross-attention
   else:
      UpperCamelCase__			    =    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}"
      UpperCamelCase__			    =    F'{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'
      UpperCamelCase__			    =    n * resnet_idx if block_type == "up" else n * resnet_idx + 1
      UpperCamelCase__			    =    F'{block_prefix}.{idx}' if block_type == "up" else F'{block_prefix}.{idx}'

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

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

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

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

   return diffusers_checkpoint



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Optional[int] )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    {}

   # 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(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		"down" ) )

   # up_blocks
   for up_block_idx, up_block in enumerate(model.up_blocks ):
      diffusers_checkpoint.update(block_to_diffusers_checkpoint(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		"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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[str] )		->       Union[str, Any]:





   '''simple docstring'''







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

   UpperCamelCase__			    =    original_config["channels"]

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

   UpperCamelCase__			    =    original_config["mapping_cond_dim"]
   UpperCamelCase__			    =    original_config["cross_cond_dim"]

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

   UpperCamelCase__			    =    UNetaDConditionModel(
       in_channels=_UpperCamelCase     ,		out_channels=_UpperCamelCase     ,		down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")     ,		mid_block_type=_UpperCamelCase     ,		up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")     ,		block_out_channels=_UpperCamelCase     ,		layers_per_block=_UpperCamelCase     ,		act_fn="gelu"     ,		norm_num_groups=_UpperCamelCase     ,		cross_attention_dim=_UpperCamelCase     ,		attention_head_dim=64     ,		time_cond_proj_dim=_UpperCamelCase     ,		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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any] )		->       Any:





   '''simple docstring'''







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

   UpperCamelCase__			    =    huggingface_hub.hf_hub_download(_UpperCamelCase     ,		"config_laion_text_cond_latent_upscaler_2.json" )
   UpperCamelCase__			    =    huggingface_hub.hf_hub_download(
       _UpperCamelCase     ,		"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" )
   UpperCamelCase__			    =    K.config.load_config(open(_UpperCamelCase ) )["model"]
   UpperCamelCase__			    =    unet_model_from_original_config(_UpperCamelCase )

   UpperCamelCase__			    =    torch.load(_UpperCamelCase     ,		map_location=_UpperCamelCase )["model_ema"]
   UpperCamelCase__			    =    unet_to_diffusers_checkpoint(_UpperCamelCase     ,		_UpperCamelCase )

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


if __name__ == "__main__":
       __lowercase:		str    						=  argparse.ArgumentParser()

       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       __lowercase:		Optional[Any]    						=  parser.parse_args()

       main(args)



'''simple docstring'''

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 OwlViTImageProcessor







class 			UpperCAmelCase			(							unittest.TestCase):

 def __init__(			self			:						Optional[Any],       a_			:						Optional[int],       a_			:						Dict=7,       a_			:						List[str]=3,       a_			:						int=18,       a_			:						Tuple=30,       a_			:						Any=400,       a_			:						Optional[int]=True,       a_			:						Optional[int]=None,       a_			:						Any=True,       a_			:						Optional[int]=None,       a_			:						int=True,       a_			:						Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073],       a_			:						Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711],       a_			:						Optional[int]=True,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    min_resolution
    UpperCamelCase__			    =    max_resolution
    UpperCamelCase__			    =    do_resize
    UpperCamelCase__			    =    size if size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_center_crop
    UpperCamelCase__			    =    crop_size if crop_size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_normalize
    UpperCamelCase__			    =    image_mean
    UpperCamelCase__			    =    image_std
    UpperCamelCase__			    =    do_convert_rgb

 def 		lowercase_    (			self			:						List[str]       ):





    """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,
        "do_convert_rgb": self.do_convert_rgb,
    }







@require_torch
@require_vision
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       Tuple            =       OwlViTImageProcessor if is_vision_available() else None

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    OwlViTImageProcessingTester(self       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(a_,       "do_resize"       )       )
    self.assertTrue(hasattr(a_,       "size"       )       )
    self.assertTrue(hasattr(a_,       "do_center_crop"       )       )
    self.assertTrue(hasattr(a_,       "center_crop"       )       )
    self.assertTrue(hasattr(a_,       "do_normalize"       )       )
    self.assertTrue(hasattr(a_,       "image_mean"       )       )
    self.assertTrue(hasattr(a_,       "image_std"       )       )
    self.assertTrue(hasattr(a_,       "do_convert_rgb"       )       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class.from_dict(self.image_processor_dict       )
    self.assertEqual(image_processor.size,       {"height": 18, "width": 18}       )
    self.assertEqual(image_processor.crop_size,       {"height": 18, "width": 18}       )

    UpperCamelCase__			    =    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			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_       )

    for image in image_inputs:
       self.assertIsInstance(a_,       Image.Image       )

    # Test not batched input
    UpperCamelCase__			    =    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
    UpperCamelCase__			    =    image_processing(a_,       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			:						Any       ):





    """simple docstring"""



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

    # Test not batched input
    UpperCamelCase__			    =    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
    UpperCamelCase__			    =    image_processing(a_,       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			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       torchify=a_       )
    for image in image_inputs:
       self.assertIsInstance(a_,       torch.Tensor       )

    # Test not batched input
    UpperCamelCase__			    =    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
    UpperCamelCase__			    =    image_processing(a_,       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"],
        ),       )