import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCAmelCase__      		=					'pcuenq/k-upscaler'






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    *, lowerCAmelCase__      ,    lowerCAmelCase__      ):
       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__      ):
       lowerCAmelCase__	      =   checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3      ,    dim=0      )
       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__      ):
       lowerCAmelCase__	      =   checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2      ,    dim=0      )
       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__      ):
       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(_SCREAMING_SNAKE_CASE      ,    '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(
                      _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    diffusers_resnet_prefix=_SCREAMING_SNAKE_CASE      ,    resnet_prefix=_SCREAMING_SNAKE_CASE      )      )

       if hasattr(_SCREAMING_SNAKE_CASE      ,    '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(
                             _SCREAMING_SNAKE_CASE      ,    diffusers_attention_prefix=_SCREAMING_SNAKE_CASE      ,    diffusers_attention_index=2      ,    attention_prefix=_SCREAMING_SNAKE_CASE      ,    )      )

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

       return diffusers_checkpoint






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       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(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    'down'      )      )

       # up_blocks
       for up_block_idx, up_block in enumerate(model.up_blocks      ):
              diffusers_checkpoint.update(block_to_diffusers_checkpoint(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    '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__      ):
       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(_SCREAMING_SNAKE_CASE      )
              attna_types.append(_SCREAMING_SNAKE_CASE      )

       lowerCAmelCase__	      =   UNetaDConditionModel(
           in_channels=_SCREAMING_SNAKE_CASE      ,    out_channels=_SCREAMING_SNAKE_CASE      ,    down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')      ,    mid_block_type=_SCREAMING_SNAKE_CASE      ,    up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')      ,    block_out_channels=_SCREAMING_SNAKE_CASE      ,    layers_per_block=_SCREAMING_SNAKE_CASE      ,    act_fn='gelu'      ,    norm_num_groups=_SCREAMING_SNAKE_CASE      ,    cross_attention_dim=_SCREAMING_SNAKE_CASE      ,    attention_head_dim=6_4      ,    time_cond_proj_dim=_SCREAMING_SNAKE_CASE      ,    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__      ):
       lowerCAmelCase__	      =   torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

       lowerCAmelCase__	      =   huggingface_hub.hf_hub_download(_SCREAMING_SNAKE_CASE      ,    'config_laion_text_cond_latent_upscaler_2.json'      )
       lowerCAmelCase__	      =   huggingface_hub.hf_hub_download(
           _SCREAMING_SNAKE_CASE      ,    '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(_SCREAMING_SNAKE_CASE      )      )["model"]
       lowerCAmelCase__	      =   unet_model_from_original_config(_SCREAMING_SNAKE_CASE      )

       lowerCAmelCase__	      =   torch.load(_SCREAMING_SNAKE_CASE      ,    map_location=_SCREAMING_SNAKE_CASE      )["model_ema"]
       lowerCAmelCase__	      =   unet_to_diffusers_checkpoint(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      )

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


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

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

    main(args)


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




