


"""simple docstring"""







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a__    :							Union[str, Any]            =					{
    '''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__    :							Tuple            =					{
    '''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__    :							Any            =					{
    '''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__    :							Tuple            =					{
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}

a__    :							List[Any]            =					{
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}

a__    :							List[Any]            =					{
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_       ):
										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__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

					if has_skip:
										__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.skip_connection.weight"""]
										__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.skip_connection.bias"""]

					return new_checkpoint




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3  ,						dim=0       )
					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3  ,						dim=0       )

					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.norm.weight"""]
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.norm.bias"""]

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

					__SCREAMING_SNAKE_CASE						       =    (
					    checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1       ).squeeze(-1       )
					)
					__SCREAMING_SNAKE_CASE						       =    checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1       ).squeeze(-1       )

					return new_checkpoint




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    torch.load(lowerCAmelCase_  ,						map_location="cpu"       )
					__SCREAMING_SNAKE_CASE						       =    {}

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

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

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

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

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

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

										if i != len(lowerCAmelCase_       ) - 1:
															__SCREAMING_SNAKE_CASE						       =    f"""down_blocks.{i}.downsamplers.0"""
															__SCREAMING_SNAKE_CASE						       =    f"""input_blocks.{current_layer}.0"""
															__SCREAMING_SNAKE_CASE						       =    convert_resnet(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
															current_layer += 1

										__SCREAMING_SNAKE_CASE						       =    current_channels

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

					__SCREAMING_SNAKE_CASE						       =    0
					__SCREAMING_SNAKE_CASE						       =    unet_config["up_block_types"]

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

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

															if i != len(lowerCAmelCase_       ) - 1:
																				__SCREAMING_SNAKE_CASE						       =    f"""up_blocks.{i}.upsamplers.0"""
																				__SCREAMING_SNAKE_CASE						       =    f"""output_blocks.{current_layer-1}.2"""
																				__SCREAMING_SNAKE_CASE						       =    convert_resnet(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )

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

					return new_checkpoint


if __name__ == "__main__":
		a__    :							List[Any]            =					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__    :							Tuple            =					parser.parse_args()
		a__    :							str            =					strabool(args.class_cond)

		a__    :							Optional[int]            =					os.path.basename(args.unet_path)
		print(F"Checkpoint: {ckpt_name}")

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

		if not args.class_cond:
				a__    :							Any            =					None

		a__    :							List[str]            =					con_pt_to_diffuser(args.unet_path, unet_config)

		a__    :							Dict            =					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__    :							str            =					CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				a__    :							Optional[Any]            =					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__    :							Union[str, Any]            =					CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

		a__    :							List[str]            =					CMStochasticIterativeScheduler(**scheduler_config)

		a__    :							int            =					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)




"""simple docstring"""







from __future__ import annotations




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					print(f"""Vertex\tShortest Distance from vertex {src}"""       )
					for i, d in enumerate(lowerCAmelCase_       ):
										print(f"""{i}\t\t{d}"""       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					for j in range(lowerCAmelCase_       ):
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (graph[j][k] for k in ["src", "dst", "weight"])
										if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
															return True
					return False




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [float("inf"       )] * vertex_count
					__SCREAMING_SNAKE_CASE						       =    0.0

					for _ in range(vertex_count - 1       ):
										for j in range(lowerCAmelCase_       ):
															__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (graph[j][k] for k in ["src", "dst", "weight"])

															if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
																				__SCREAMING_SNAKE_CASE						       =    distance[u] + w

					__SCREAMING_SNAKE_CASE						       =    check_negative_cycle(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
					if negative_cycle_exists:
										raise Exception("Negative cycle found"       )

					return distance


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__    :							Union[str, Any]            =					int(input('''Enter number of vertices: ''').strip())
		a__    :							Any            =					int(input('''Enter number of edges: ''').strip())

		a__    :							list[dict[str, int]]            =					[{} for _ in range(E)]

		for i in range(E):
				print('''Edge ''', i + 1)
				a__      ,					a__      ,					a__    :							str            =					(
				    int(x)
				    for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
				)
				a__    :							str            =					{'''src''': src, '''dst''': dest, '''weight''': weight}

		a__    :							str            =					int(input('''\nEnter shortest path source:''').strip())
		a__    :							List[Any]            =					bellman_ford(graph, V, E, source)
		print_distance(shortest_distance, 0)
