


'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__lowercase:		Any							=     '''pcuenq/k-upscaler'''



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Dict    ,		*, _UpperCamelCase      :				str    ,		_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      :				str    ,		*, _UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Dict   )		->					Optional[Any]:





   '''simple docstring'''







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





   '''simple docstring'''







   UpperCamelCase__ 						=						checkpoint[F'{attention_prefix}.kv_proj.weight'].chunk(2    ,		dim=0   )
   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[str]    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				Any   )		->					Optional[int]:





   '''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      :				Optional[Any]    ,		_UpperCamelCase      :				Any   )		->					Tuple:





   '''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      :				Union[str, Any]   )		->					Tuple:





   '''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      :				List[str]   )		->					int:





   '''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:		Any							=     argparse.ArgumentParser()

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

       main(args)



'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class  UpperCAmelCase (       unittest.TestCase):

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						tempfile.mkdtemp()

    UpperCamelCase__ 						=						CLIPImageProcessor()
    UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
        "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)

    UpperCamelCase__ 						=						GitProcessor(a_,					a_					)

    processor.save_pretrained(self.tmpdirname					)

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    shutil.rmtree(self.tmpdirname					)

 def 		lowercase_						( self :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]

    UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]

    return image_inputs

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
    processor.save_pretrained(self.tmpdirname					)

    UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
    UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)

    UpperCamelCase__ 						=						GitProcessor.from_pretrained(
        self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)

    self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
    self.assertIsInstance(processor.tokenizer,					a_					)

    self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
    self.assertIsInstance(processor.image_processor,					a_					)

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
    UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)

    for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"

    UpperCamelCase__ 						=						processor(text=a_					)

    UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)

    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"
    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						processor(text=a_,					images=a_					)

    self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)

    # test if it raises when no input is passed
    with pytest.raises(a_					):
       processor()

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    UpperCamelCase__ 						=						processor.batch_decode(a_					)
    UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)

    self.assertListEqual(a_,					a_					)

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"
    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						processor(text=a_,					images=a_					)

    # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
    self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)