






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


snake_case_   :	Union[str, Any]							      ='''pcuenq/k-upscaler'''



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__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						( lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A ,   __A ,   __A    =							checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3					,     dim=0	)
		__A ,   __A ,   __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						( lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A ,   __A    =							checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2					,     dim=0	)
		__A ,   __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						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__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(lowerCAmelCase__					,     "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(
				        lowerCAmelCase__					,     lowerCAmelCase__					,     diffusers_resnet_prefix=lowerCAmelCase__					,     resnet_prefix=lowerCAmelCase__	)	)

		if hasattr(lowerCAmelCase__					,     "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(
						        lowerCAmelCase__					,     diffusers_attention_prefix=lowerCAmelCase__					,     diffusers_attention_index=2					,     attention_prefix=lowerCAmelCase__					,     )	)

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

		return diffusers_checkpoint



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__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(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     "down"	)	)

		# up_blocks
		for up_block_idx, up_block in enumerate(model.up_blocks	):
				diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     "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						( lowerCAmelCase__	):


		'''simple docstring'''

		__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(lowerCAmelCase__	)
				attna_types.append(lowerCAmelCase__	)

		__A    =							UNetaDConditionModel(
		    in_channels=lowerCAmelCase__					,     out_channels=lowerCAmelCase__					,     down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")					,     mid_block_type=lowerCAmelCase__					,     up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")					,     block_out_channels=lowerCAmelCase__					,     layers_per_block=lowerCAmelCase__					,     act_fn="gelu"					,     norm_num_groups=lowerCAmelCase__					,     cross_attention_dim=lowerCAmelCase__					,     attention_head_dim=64					,     time_cond_proj_dim=lowerCAmelCase__					,     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						( lowerCAmelCase__	):


		'''simple docstring'''

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

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

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

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


if __name__ == "__main__":
					snake_case_   :	Optional[Any]							      =argparse.ArgumentParser()

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

					main(args)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_   :	Union[str, Any]							      ={'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	int							      =[
					    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''GPTJForCausalLM''',
					    '''GPTJForQuestionAnswering''',
					    '''GPTJForSequenceClassification''',
					    '''GPTJModel''',
					    '''GPTJPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''TFGPTJForCausalLM''',
					    '''TFGPTJForQuestionAnswering''',
					    '''TFGPTJForSequenceClassification''',
					    '''TFGPTJModel''',
					    '''TFGPTJPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	List[Any]							      =[
					    '''FlaxGPTJForCausalLM''',
					    '''FlaxGPTJModel''',
					    '''FlaxGPTJPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					snake_case_   :	List[str]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

