
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__a							  =						"pcuenq/k-upscaler"

def    UpperCamelCase_ (	a_  ,				a_  ,				*, a_  ,				a_		)     ->int:
		A				={
		    # 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    UpperCamelCase_ (	a_  ,				*, a_  ,				a_		)     ->Optional[int]:
		A				=checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3  ,				dim=0		)
		A				=checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3  ,				dim=0		)
		A				={
		    # 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    UpperCamelCase_ (	a_  ,				*, a_  ,				a_  ,				a_		)     ->Optional[int]:
		A				=checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2  ,				dim=0		)
		A				=checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2  ,				dim=0		)

		A				={
		    # 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    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->List[Any]:
		A				="inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
		A				=f'''{block_prefix}.{block_idx}'''

		A				={}

		if not hasattr(_UpperCAmelCase  ,				"attentions"		):
				A				=1  # resnet only
		elif not block.attentions[0].add_self_attention:
				A				=2  # resnet -> cross-attention
		else:
				A				=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}"
				A				=f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
				A				=n * resnet_idx if block_type == "up" else n * resnet_idx + 1
				A				=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		):
						A				=f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
						A				=n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
						A				=f'''{block_prefix}.{idx}'''
						A				=f'''{block_prefix}.{idx }'''
						A				=1 if not attention.add_self_attention else 2
						A				=(
						    n * attention_idx + cross_attention_index
						    if block_type == "up"
						    else n * attention_idx + cross_attention_index + 1
						)
						A				=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    UpperCamelCase_ (	a_  ,				a_		)     ->Dict:
		A				={}

		# 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    UpperCamelCase_ (	a_		)     ->Any:
		A				=original_config["input_channels"] + original_config["unet_cond_dim"]
		A				=original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

		A				=original_config["channels"]

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

		A				=original_config["mapping_cond_dim"]
		A				=original_config["cross_cond_dim"]

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

		A				=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    UpperCamelCase_ (	a_		)     ->str:
		A				=torch.device("cuda" if torch.cuda.is_available() else "cpu"		)

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

		A				=torch.load(_UpperCAmelCase  ,				map_location=_UpperCAmelCase		)["model_ema"]
		A				=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__":
	__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)











'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xglm import XGLMTokenizer
else:
			lowerCAmelCase__      :   List[str]      	=			None


lowerCAmelCase__      :   Union[str, Any]      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   Tuple      	=			{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__      :   List[str]      	=			{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase__      :   int      	=			{
    "facebook/xglm-564M": 20_48,
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE							=	PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE							=	['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE							=	XGLMTokenizer
					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      str="<s>"		,	UpperCAmelCase_       :      List[Any]="</s>"		,	UpperCAmelCase_       :      Dict="</s>"		,	UpperCAmelCase_       :      Optional[Any]="<s>"		,	UpperCAmelCase_       :      List[str]="<unk>"		,	UpperCAmelCase_       :      Union[str, Any]="<pad>"		,	**UpperCAmelCase_       :      List[Any]		,	):







							"""simple docstring"""
							# Compatibility with the original tokenizer
							__UpperCAmelCase	:							Union[str, Any]     =     7
							__UpperCAmelCase	:							Tuple     =     [f"<madeupword{i}>" for i in range(self.num_madeup_words		)]

							__UpperCAmelCase	:							List[Any]     =     kwargs.get("additional_special_tokens"		,	[]		)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    UpperCAmelCase_		,	tokenizer_file=UpperCAmelCase_		,	bos_token=UpperCAmelCase_		,	eos_token=UpperCAmelCase_		,	sep_token=UpperCAmelCase_		,	cls_token=UpperCAmelCase_		,	unk_token=UpperCAmelCase_		,	pad_token=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Dict     =     vocab_file
							__UpperCAmelCase	:							Optional[int]     =     False if not self.vocab_file else True
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""

							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							__UpperCAmelCase	:							Any     =     [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     [self.sep_token_id]

							if token_ids_a is None:
									return len(sep + token_ids_a		) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      Optional[str] = None		):







							"""simple docstring"""
							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."		)

							if not os.path.isdir(UpperCAmelCase_		):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
									return
							__UpperCAmelCase	:							str     =     os.path.join(
							    UpperCAmelCase_		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase_		):
									copyfile(self.vocab_file		,	UpperCAmelCase_		)

							return (out_vocab_file,)




