





'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCAmelCase__      :   str      	=			"pcuenq/k-upscaler"


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       *, _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							str     =     {
		    # 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						(			_UpperCAmelCase,       *, _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[Any]     =     checkpoint[F"{attention_prefix}.qkv_proj.weight"].chunk(3,       dim=0  )
		__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							Any     =     checkpoint[F"{attention_prefix}.qkv_proj.bias"].chunk(3,       dim=0  )
		__UpperCAmelCase	:							int     =     {
		    # 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						(			_UpperCAmelCase,       *, _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     checkpoint[F"{attention_prefix}.kv_proj.weight"].chunk(2,       dim=0  )
		__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     checkpoint[F"{attention_prefix}.kv_proj.bias"].chunk(2,       dim=0  )

		__UpperCAmelCase	:							Optional[int]     =     {
		    # 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						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							str     =     "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
		__UpperCAmelCase	:							Optional[int]     =     F"{block_prefix}.{block_idx}"

		__UpperCAmelCase	:							List[Any]     =     {}

		if not hasattr(_UpperCAmelCase,       "attentions"  ):
				__UpperCAmelCase	:							str     =     1  # resnet only
		elif not block.attentions[0].add_self_attention:
				__UpperCAmelCase	:							Tuple     =     2  # resnet -> cross-attention
		else:
				__UpperCAmelCase	:							Any     =     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	:							List[str]     =     F"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
				__UpperCAmelCase	:							Any     =     n * resnet_idx if block_type == "up" else n * resnet_idx + 1
				__UpperCAmelCase	:							int     =     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	:							Tuple     =     F"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
						__UpperCAmelCase	:							Optional[Any]     =     n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
						__UpperCAmelCase	:							List[Any]     =     F"{block_prefix}.{idx}"
						__UpperCAmelCase	:							List[Any]     =     F"{block_prefix}.{idx }"
						__UpperCAmelCase	:							List[str]     =     1 if not attention.add_self_attention else 2
						__UpperCAmelCase	:							Optional[Any]     =     (
						    n * attention_idx + cross_attention_index
						    if block_type == "up"
						    else n * attention_idx + cross_attention_index + 1
						)
						__UpperCAmelCase	:							Any     =     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						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							List[str]     =     {}

		# 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						(			_UpperCAmelCase  ):
		__UpperCAmelCase	:							Optional[Any]     =     original_config["input_channels"] + original_config["unet_cond_dim"]
		__UpperCAmelCase	:							Optional[Any]     =     original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

		__UpperCAmelCase	:							Union[str, Any]     =     original_config["channels"]

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

		__UpperCAmelCase	:							Any     =     original_config["mapping_cond_dim"]
		__UpperCAmelCase	:							List[str]     =     original_config["cross_cond_dim"]

		__UpperCAmelCase	:							Dict     =     []
		__UpperCAmelCase	:							Optional[Any]     =     []
		for s, c in zip(original_config["self_attn_depths"],       original_config["cross_attn_depths"]  ):
				if s:
						__UpperCAmelCase	:							Tuple     =     "self"
						__UpperCAmelCase	:							Any     =     "cross" if c else None
				elif c:
						__UpperCAmelCase	:							Union[str, Any]     =     "cross"
						__UpperCAmelCase	:							Tuple     =     None
				else:
						__UpperCAmelCase	:							str     =     None
						__UpperCAmelCase	:							str     =     None
				attna_types.append(_UpperCAmelCase  )
				attna_types.append(_UpperCAmelCase  )

		__UpperCAmelCase	:							Union[str, Any]     =     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						(			_UpperCAmelCase  ):
		__UpperCAmelCase	:							Tuple     =     torch.device("cuda" if torch.cuda.is_available() else "cpu"  )

		__UpperCAmelCase	:							Optional[Any]     =     huggingface_hub.hf_hub_download(_UpperCAmelCase,       "config_laion_text_cond_latent_upscaler_2.json"  )
		__UpperCAmelCase	:							Dict     =     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	:							List[Any]     =     K.config.load_config(open(_UpperCAmelCase  )  )["model"]
		__UpperCAmelCase	:							Optional[Any]     =     unet_model_from_original_config(_UpperCAmelCase  )

		__UpperCAmelCase	:							Tuple     =     torch.load(_UpperCAmelCase,       map_location=_UpperCAmelCase  )["model_ema"]
		__UpperCAmelCase	:							str     =     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__":
			lowerCAmelCase__      :   Tuple      	=			argparse.ArgumentParser()

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

			main(args)











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




