


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a       	=  {
    'sample_size': 3_2,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [3_2, 6_4],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a       	=  {
    'sample_size': 6_4,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    'attention_head_dim': 6_4,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a       	=  {
    'sample_size': 2_5_6,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    'attention_head_dim': 6_4,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

a       	=  {
    'num_train_timesteps': 4_0,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

a       	=  {
    'num_train_timesteps': 2_0_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

a       	=  {
    'num_train_timesteps': 1_5_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					if isinstance(__A    ,				__A	):
										return v
					if v.lower() in ("yes", "true", "t", "y", "1"):
										return True
					elif v.lower() in ("no", "false", "f", "n", "0"):
										return False
					else:
										raise argparse.ArgumentTypeError("""boolean value expected"""	)


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__=False	):
					lowercase_					=      checkpoint[F'''{old_prefix}.in_layers.0.weight''']
					lowercase_					=      checkpoint[F'''{old_prefix}.in_layers.0.bias''']
					lowercase_					=      checkpoint[F'''{old_prefix}.in_layers.2.weight''']
					lowercase_					=      checkpoint[F'''{old_prefix}.in_layers.2.bias''']
					lowercase_					=      checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
					lowercase_					=      checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
					lowercase_					=      checkpoint[F'''{old_prefix}.out_layers.0.weight''']
					lowercase_					=      checkpoint[F'''{old_prefix}.out_layers.0.bias''']
					lowercase_					=      checkpoint[F'''{old_prefix}.out_layers.3.weight''']
					lowercase_					=      checkpoint[F'''{old_prefix}.out_layers.3.bias''']

					if has_skip:
										lowercase_					=      checkpoint[F'''{old_prefix}.skip_connection.weight''']
										lowercase_					=      checkpoint[F'''{old_prefix}.skip_connection.bias''']

					return new_checkpoint


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__=None	):
					lowercase_					=      checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3    ,				dim=0	)
					lowercase_					=      checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3    ,				dim=0	)

					lowercase_					=      checkpoint[F'''{old_prefix}.norm.weight''']
					lowercase_					=      checkpoint[F'''{old_prefix}.norm.bias''']

					lowercase_					=      weight_q.squeeze(-1	).squeeze(-1	)
					lowercase_					=      bias_q.squeeze(-1	).squeeze(-1	)
					lowercase_					=      weight_k.squeeze(-1	).squeeze(-1	)
					lowercase_					=      bias_k.squeeze(-1	).squeeze(-1	)
					lowercase_					=      weight_v.squeeze(-1	).squeeze(-1	)
					lowercase_					=      bias_v.squeeze(-1	).squeeze(-1	)

					lowercase_					=      (
					    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1	).squeeze(-1	)
					)
					lowercase_					=      checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1	).squeeze(-1	)

					return new_checkpoint


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__	):
					lowercase_					=      torch.load(__A    ,				map_location="""cpu"""	)
					lowercase_					=      {}

					lowercase_					=      checkpoint["time_embed.0.weight"]
					lowercase_					=      checkpoint["time_embed.0.bias"]
					lowercase_					=      checkpoint["time_embed.2.weight"]
					lowercase_					=      checkpoint["time_embed.2.bias"]

					if unet_config["num_class_embeds"] is not None:
										lowercase_					=      checkpoint["label_emb.weight"]

					lowercase_					=      checkpoint["input_blocks.0.0.weight"]
					lowercase_					=      checkpoint["input_blocks.0.0.bias"]

					lowercase_					=      unet_config["down_block_types"]
					lowercase_					=      unet_config["layers_per_block"]
					lowercase_					=      unet_config["attention_head_dim"]
					lowercase_					=      unet_config["block_out_channels"]
					lowercase_					=      1
					lowercase_					=      channels_list[0]

					for i, layer_type in enumerate(__A	):
										lowercase_					=      channels_list[i]
										lowercase_					=      current_channels != prev_channels
										if layer_type == "ResnetDownsampleBlock2D":
															for j in range(__A	):
																				lowercase_					=      F'''down_blocks.{i}.resnets.{j}'''
																				lowercase_					=      F'''input_blocks.{current_layer}.0'''
																				lowercase_					=      True if j == 0 and downsample_block_has_skip else False
																				lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A    ,				has_skip=__A	)
																				current_layer += 1

										elif layer_type == "AttnDownBlock2D":
															for j in range(__A	):
																				lowercase_					=      F'''down_blocks.{i}.resnets.{j}'''
																				lowercase_					=      F'''input_blocks.{current_layer}.0'''
																				lowercase_					=      True if j == 0 and downsample_block_has_skip else False
																				lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A    ,				has_skip=__A	)
																				lowercase_					=      F'''down_blocks.{i}.attentions.{j}'''
																				lowercase_					=      F'''input_blocks.{current_layer}.1'''
																				lowercase_					=      convert_attention(
																				    __A    ,				__A    ,				__A    ,				__A    ,				__A	)
																				current_layer += 1

										if i != len(__A	) - 1:
															lowercase_					=      F'''down_blocks.{i}.downsamplers.0'''
															lowercase_					=      F'''input_blocks.{current_layer}.0'''
															lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A	)
															current_layer += 1

										lowercase_					=      current_channels

					# hardcoded the mid-block for now
					lowercase_					=      "mid_block.resnets.0"
					lowercase_					=      "middle_block.0"
					lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A	)
					lowercase_					=      "mid_block.attentions.0"
					lowercase_					=      "middle_block.1"
					lowercase_					=      convert_attention(__A    ,				__A    ,				__A    ,				__A    ,				__A	)
					lowercase_					=      "mid_block.resnets.1"
					lowercase_					=      "middle_block.2"
					lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A	)

					lowercase_					=      0
					lowercase_					=      unet_config["up_block_types"]

					for i, layer_type in enumerate(__A	):
										if layer_type == "ResnetUpsampleBlock2D":
															for j in range(layers_per_block + 1	):
																				lowercase_					=      F'''up_blocks.{i}.resnets.{j}'''
																				lowercase_					=      F'''output_blocks.{current_layer}.0'''
																				lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A    ,				has_skip=__A	)
																				current_layer += 1

															if i != len(__A	) - 1:
																				lowercase_					=      F'''up_blocks.{i}.upsamplers.0'''
																				lowercase_					=      F'''output_blocks.{current_layer-1}.1'''
																				lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A	)
										elif layer_type == "AttnUpBlock2D":
															for j in range(layers_per_block + 1	):
																				lowercase_					=      F'''up_blocks.{i}.resnets.{j}'''
																				lowercase_					=      F'''output_blocks.{current_layer}.0'''
																				lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A    ,				has_skip=__A	)
																				lowercase_					=      F'''up_blocks.{i}.attentions.{j}'''
																				lowercase_					=      F'''output_blocks.{current_layer}.1'''
																				lowercase_					=      convert_attention(
																				    __A    ,				__A    ,				__A    ,				__A    ,				__A	)
																				current_layer += 1

															if i != len(__A	) - 1:
																				lowercase_					=      F'''up_blocks.{i}.upsamplers.0'''
																				lowercase_					=      F'''output_blocks.{current_layer-1}.2'''
																				lowercase_					=      convert_resnet(__A    ,				__A    ,				__A    ,				__A	)

					lowercase_					=      checkpoint["out.0.weight"]
					lowercase_					=      checkpoint["out.0.bias"]
					lowercase_					=      checkpoint["out.2.weight"]
					lowercase_					=      checkpoint["out.2.bias"]

					return new_checkpoint


if __name__ == "__main__":
							a       	=  argparse.ArgumentParser()

							parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
							parser.add_argument(
							    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
							)
							parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

							a       	=  parser.parse_args()
							a       	=  strabool(args.class_cond)

							a       	=  os.path.basename(args.unet_path)
							print(F'''Checkpoint: {ckpt_name}''')

							# Get U-Net config
							if "imagenet64" in ckpt_name:
														a       	=  IMAGENET_64_UNET_CONFIG
							elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														a       	=  LSUN_256_UNET_CONFIG
							elif "test" in ckpt_name:
														a       	=  TEST_UNET_CONFIG
							else:
														raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

							if not args.class_cond:
														a       	=  None

							a       	=  con_pt_to_diffuser(args.unet_path, unet_config)

							a       	=  UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

							# Get scheduler config
							if "cd" in ckpt_name or "test" in ckpt_name:
														a       	=  CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														a       	=  CT_IMAGENET_64_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														a       	=  CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

							a       	=  CMStochasticIterativeScheduler(**scheduler_config)

							a       	=  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)










from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





