"""simple docstring"""


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase=0				)							->					Optional[Any]:




						'''simple docstring'''



						if name is None:
												lowerCamelCase__					=None
						else:
												lowerCamelCase__					="." * max(0   ,    spaces - 2				) + "# {:" + str(50 - spaces				) + "s}"
												lowerCamelCase__					=fmt.format(__lowerCAmelCase				)

						# Print and recurse (if needed).
						if isinstance(__lowerCAmelCase   ,    __lowerCAmelCase				):
												if msg is not None:
																		print(__lowerCAmelCase				)
												for k in val.keys():
																		recursive_print(__lowerCAmelCase   ,    val[k]   ,    spaces + 2				)
						elif isinstance(__lowerCAmelCase   ,    torch.Tensor				):
												print(__lowerCAmelCase   ,    ":"   ,    val.size()				)
						else:
												print(__lowerCAmelCase   ,    ":"   ,    __lowerCAmelCase				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__					=param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						lowerCamelCase__					=(num_heads, num_splits, hidden_size) + input_shape[1:]
						lowerCamelCase__					=param.view(*__lowerCAmelCase				)
						lowerCamelCase__					=param.transpose(0   ,    1				).contiguous()
						lowerCamelCase__					=param.view(*__lowerCAmelCase				)
						return param





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__					=config.n_positions
						lowerCamelCase__					=config.n_layer
						lowerCamelCase__					=config.vocab_size
						lowerCamelCase__					=config.n_head
						lowerCamelCase__					=config.n_embd // config.n_head

						lowerCamelCase__					=sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
						lowerCamelCase__					={
						    "transformer.wte.weight": word_embeddings,
						    "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
						    "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
						    "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
						}

						lowerCamelCase__					="model.language_model.encoder.layers."
						for i in range(__lowerCAmelCase				):
												lowerCamelCase__					=torch.tril(torch.ones((n_positions, n_positions)   ,    dtype=torch.bool				)				)
												lowerCamelCase__					=causal_mask.view(1   ,    1   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)
												lowerCamelCase__					=causal_mask
												lowerCamelCase__					=torch.tensor(-1e4   ,    dtype=torch.bfloataa				)

												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
												lowerCamelCase__					=fix_query_key_value_ordering(__lowerCAmelCase   ,    3   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)
												lowerCamelCase__					=vala.transpose(0   ,    1				).contiguous()

												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
												lowerCamelCase__					=fix_query_key_value_ordering(__lowerCAmelCase   ,    3   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)
												lowerCamelCase__					=vala

												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
												    0   ,    1				)
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0   ,    1				)
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
												    0   ,    1				)
												lowerCamelCase__					=sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

						# For LM head, transformers' wants the matrix to weight embeddings.
						lowerCamelCase__					=word_embeddings

						return sd_hf





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=6_4000
						lowerCamelCase__					=config_megatron["encoder_seq_length"]
						lowerCamelCase__					=config_megatron["hidden_size"]
						lowerCamelCase__					=config_megatron["num_layers"]
						lowerCamelCase__					=config_megatron["num_attention_heads"]
						lowerCamelCase__					=config_megatron["ffn_hidden_size"]
						lowerCamelCase__					="gelu"
						lowerCamelCase__					=0.1
						lowerCamelCase__					=0.1
						lowerCamelCase__					=0.1
						lowerCamelCase__					=config_megatron["layernorm_epsilon"]  # 1e-5
						lowerCamelCase__					=config_megatron["init_method_std"]  # 0.02
						lowerCamelCase__					=config_megatron["apply_query_key_layer_scaling"]  # True
						lowerCamelCase__					=True
						lowerCamelCase__					=True

						# This identifies the 6.7B (7B) model which uses a different tokenizer
						if config_megatron["hidden_size"] == 4096:
												lowerCamelCase__					=1  # <|endoftext|>
												lowerCamelCase__					=1  # <|endoftext|>
												lowerCamelCase__					=0  # <unk>
						else:
												lowerCamelCase__					=2  # <s>
												lowerCamelCase__					=3  # <|endoftext|>
												lowerCamelCase__					=0  # <pad>

						return config_hf





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					List[Any]:




						'''simple docstring'''



						print(__lowerCAmelCase				)

						lowerCamelCase__					=args.checkpoint_path
						lowerCamelCase__					=args.save_path
						if isfile(__lowerCAmelCase				):
												raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''				)

						# Load the model.
						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location="cpu"				)

						# Load the config.
						lowerCamelCase__					=checkpoint["hyper_parameters"]["cfg"]
						lowerCamelCase__					=GPTaConfig()
						lowerCamelCase__					=copy_config(config_hf=__lowerCAmelCase   ,    config_megatron=__lowerCAmelCase				)
						lowerCamelCase__					=["GPT2LMHeadModel"]

						lowerCamelCase__					=checkpoint["state_dict"]

						# Convert.
						print("Converting"				)
						lowerCamelCase__					=convert_megatron_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase				)

						# Print the structure of converted state dict.
						if args.print_checkpoint_structure:
												recursive_print(__lowerCAmelCase   ,    __lowerCAmelCase				)

						lowerCamelCase__					="GPTSw3Tokenizer"

						# Store the config to file.
						print("Saving config"				)
						config_hf.save_pretrained(__lowerCAmelCase				)

						# Store the state_dict to file.
						lowerCamelCase__					=os.path.join(__lowerCAmelCase   ,    "pytorch_model.bin"				)
						print(F'''Saving checkpoint to "{output_checkpoint_file}"'''				)
						torch.save(__lowerCAmelCase   ,    __lowerCAmelCase				)


if __name__ == "__main__":
	a							=argparse.ArgumentParser()
	parser.add_argument(
	    '--checkpoint_path',
	    type=str,
	    required=True,
	    help='e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000',
	)
	parser.add_argument('--save_path', type=str, required=True, help='e.g. /home/user/gpt-sw3/hf')
	parser.add_argument('--print-checkpoint-structure', action='store_true')
	a							=parser.parse_args()
	main(_args)



"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						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				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					,      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				)							->					str:




						'''simple docstring'''



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




						'''simple docstring'''



						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(__lowerCAmelCase   ,    "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(
												        __lowerCAmelCase   ,    __lowerCAmelCase   ,    diffusers_resnet_prefix=__lowerCAmelCase   ,    resnet_prefix=__lowerCAmelCase				)				)

						if hasattr(__lowerCAmelCase   ,    "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(
																		        __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 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						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(__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 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						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(__lowerCAmelCase				)
												attna_types.append(__lowerCAmelCase				)

						lowerCamelCase__					=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 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int:




						'''simple docstring'''



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

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

						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location=__lowerCAmelCase				)["model_ema"]
						lowerCamelCase__					=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__":
	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)


