



'''simple docstring'''







import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_UpperCamelCase  						=			"""pcuenq/k-upscaler"""
def 			_lowerCAmelCase(      UpperCAmelCase_      :	int					, UpperCAmelCase_      :	Optional[int]					, *, UpperCAmelCase_      :	List[Any]					, UpperCAmelCase_      :	int						) ->							Optional[int]:
							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(      UpperCAmelCase_      :	Union[str, Any]					, *, UpperCAmelCase_      :	Optional[int]					, UpperCAmelCase_      :	str						) ->							str:
							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(      UpperCAmelCase_      :	List[Any]					, *, UpperCAmelCase_      :	List[Any]					, UpperCAmelCase_      :	List[str]					, UpperCAmelCase_      :	List[Any]						) ->							Tuple:
							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(      UpperCAmelCase_      :	List[Any]					, UpperCAmelCase_      :	Any					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	Union[str, Any]						) ->							Union[str, Any]:
							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(UpperCAmelCase_					, """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(
														        UpperCAmelCase_					, UpperCAmelCase_					, diffusers_resnet_prefix=UpperCAmelCase_					, resnet_prefix=UpperCAmelCase_						)						)

							if hasattr(UpperCAmelCase_					, """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(
																					        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 			_lowerCAmelCase(      UpperCAmelCase_      :	Dict					, UpperCAmelCase_      :	List[Any]						) ->							Any:
							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(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 			_lowerCAmelCase(      UpperCAmelCase_      :	List[Any]						) ->							Union[str, Any]:
							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(UpperCAmelCase_						)
														attna_types.append(UpperCAmelCase_						)

							lowerCAmelCase__  =   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 			_lowerCAmelCase(      UpperCAmelCase_      :	Tuple						) ->							Dict:
							lowerCAmelCase__  =   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)

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

							lowerCAmelCase__  =   torch.load(UpperCAmelCase_					, map_location=UpperCAmelCase_						)["""model_ema"""]
							lowerCAmelCase__  =   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__":
		_UpperCamelCase  						=			argparse.ArgumentParser()

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

		main(args)










'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	str					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int					, ) ->							tuple[list[list[int]], list[list[str]]]:
							lowerCAmelCase__  =   list(UpperCAmelCase_						)
							lowerCAmelCase__  =   list(UpperCAmelCase_						)
							lowerCAmelCase__  =   len(UpperCAmelCase_						)
							lowerCAmelCase__  =   len(UpperCAmelCase_						)

							lowerCAmelCase__  =   [
							    [0 for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
							]
							lowerCAmelCase__  =   [
							    ["""0""" for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
							]

							for i in range(1					, len_source_seq + 1						):
														lowerCAmelCase__  =   i * delete_cost
														lowerCAmelCase__  =   F'''D{source_seq[i - 1]:c}'''

							for i in range(1					, len_destination_seq + 1						):
														lowerCAmelCase__  =   i * insert_cost
														lowerCAmelCase__  =   F'''I{destination_seq[i - 1]:c}'''

							for i in range(1					, len_source_seq + 1						):
														for j in range(1					, len_destination_seq + 1						):
																					if source_seq[i - 1] == destination_seq[j - 1]:
																												lowerCAmelCase__  =   costs[i - 1][j - 1] + copy_cost
																												lowerCAmelCase__  =   F'''C{source_seq[i - 1]:c}'''
																					else:
																												lowerCAmelCase__  =   costs[i - 1][j - 1] + replace_cost
																												lowerCAmelCase__  =   F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]						)

																					if costs[i - 1][j] + delete_cost < costs[i][j]:
																												lowerCAmelCase__  =   costs[i - 1][j] + delete_cost
																												lowerCAmelCase__  =   F'''D{source_seq[i - 1]:c}'''

																					if costs[i][j - 1] + insert_cost < costs[i][j]:
																												lowerCAmelCase__  =   costs[i][j - 1] + insert_cost
																												lowerCAmelCase__  =   F'''I{destination_seq[j - 1]:c}'''

							return costs, ops
def 			_lowerCAmelCase(      UpperCAmelCase_      :	list[list[str]]					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int						) ->							list[str]:
							if i == 0 and j == 0:
														return []
							else:
														if ops[i][j][0] == "C" or ops[i][j][0] == "R":
																					lowerCAmelCase__  =   assemble_transformation(UpperCAmelCase_					, i - 1					, j - 1						)
																					seq.append(ops[i][j]						)
																					return seq
														elif ops[i][j][0] == "D":
																					lowerCAmelCase__  =   assemble_transformation(UpperCAmelCase_					, i - 1					, UpperCAmelCase_						)
																					seq.append(ops[i][j]						)
																					return seq
														else:
																					lowerCAmelCase__  =   assemble_transformation(UpperCAmelCase_					, UpperCAmelCase_					, j - 1						)
																					seq.append(ops[i][j]						)
																					return seq


if __name__ == "__main__":
		_UpperCamelCase						, _UpperCamelCase  						=			compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

		_UpperCamelCase  						=			len(operations)
		_UpperCamelCase  						=			len(operations[0])
		_UpperCamelCase  						=			assemble_transformation(operations, m - 1, n - 1)

		_UpperCamelCase  						=			list("""Python""")
		_UpperCamelCase  						=			0
		_UpperCamelCase  						=			0

		with open("""min_cost.txt""", """w""") as file:
				for op in sequence:
						print("""""".join(string))

						if op[0] == "C":
								file.write("""%-16s""" % """Copy %c""" % op[1])
								file.write("""\t\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost -= 1
						elif op[0] == "R":
								_UpperCamelCase  						=			op[2]

								file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
								file.write("""\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost += 1
						elif op[0] == "D":
								string.pop(i)

								file.write("""%-16s""" % """Delete %c""" % op[1])
								file.write("""\t\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost += 2
						else:
								string.insert(i, op[1])

								file.write("""%-16s""" % """Insert %c""" % op[1])
								file.write("""\t\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost += 2

						i += 1

				print("""""".join(string))
				print("""Cost: """, cost)

				file.write("""\r\nMinimum cost: """ + str(cost))





