


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCAmelCase_      : List[Any]  					=       {
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "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",
}

UpperCAmelCase_      : str  					=       {
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "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",
}

UpperCAmelCase_      : Union[str, Any]  					=       {
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "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",
}

UpperCAmelCase_      : str  					=       {
    "num_train_timesteps": 40,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

UpperCAmelCase_      : Union[str, Any]  					=       {
    "num_train_timesteps": 201,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

UpperCAmelCase_      : Any  					=       {
    "num_train_timesteps": 151,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict				)	->		int:

       """simple docstring"""

       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        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Optional[Any]  ,    __A :			List[Any]  ,    __A :			int  ,    __A :			List[str]=False				)	->		int:

       """simple docstring"""

       a_					:		Union[str, Any]							=  checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
       a_					:		Optional[Any]							=  checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
       a_					:		Optional[int]							=  checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
       a_					:		str							=  checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
       a_					:		List[str]							=  checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
       a_					:		Optional[Any]							=  checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
       a_					:		Union[str, Any]							=  checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
       a_					:		Tuple							=  checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
       a_					:		int							=  checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
       a_					:		List[Any]							=  checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

       if has_skip:
              a_					:		Optional[int]							=  checkpoint[F"""{old_prefix}.skip_connection.weight"""]
              a_					:		int							=  checkpoint[F"""{old_prefix}.skip_connection.bias"""]

       return new_checkpoint







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			Any  ,    __A :			Optional[Any]  ,    __A :			List[Any]  ,    __A :			Dict=None				)	->		List[Any]:

       """simple docstring"""

       a_							,		a_							,		a_					:		Any							=  checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3  ,    dim=0				)
       a_							,		a_							,		a_					:		Dict							=  checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3  ,    dim=0				)

       a_					:		Union[str, Any]							=  checkpoint[F"""{old_prefix}.norm.weight"""]
       a_					:		str							=  checkpoint[F"""{old_prefix}.norm.bias"""]

       a_					:		str							=  weight_q.squeeze(-1				).squeeze(-1				)
       a_					:		List[Any]							=  bias_q.squeeze(-1				).squeeze(-1				)
       a_					:		List[str]							=  weight_k.squeeze(-1				).squeeze(-1				)
       a_					:		Tuple							=  bias_k.squeeze(-1				).squeeze(-1				)
       a_					:		Tuple							=  weight_v.squeeze(-1				).squeeze(-1				)
       a_					:		int							=  bias_v.squeeze(-1				).squeeze(-1				)

       a_					:		Optional[int]							=  (
           checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1				).squeeze(-1				)
       )
       a_					:		str							=  checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1				).squeeze(-1				)

       return new_checkpoint







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			int				)	->		List[str]:

       """simple docstring"""

       a_					:		Any							=  torch.load(__A  ,    map_location='cpu'				)
       a_					:		Tuple							=  {}

       a_					:		int							=  checkpoint['time_embed.0.weight']
       a_					:		Tuple							=  checkpoint['time_embed.0.bias']
       a_					:		int							=  checkpoint['time_embed.2.weight']
       a_					:		Optional[Any]							=  checkpoint['time_embed.2.bias']

       if unet_config["num_class_embeds"] is not None:
              a_					:		Tuple							=  checkpoint['label_emb.weight']

       a_					:		Optional[Any]							=  checkpoint['input_blocks.0.0.weight']
       a_					:		int							=  checkpoint['input_blocks.0.0.bias']

       a_					:		Any							=  unet_config['down_block_types']
       a_					:		Optional[Any]							=  unet_config['layers_per_block']
       a_					:		Any							=  unet_config['attention_head_dim']
       a_					:		Any							=  unet_config['block_out_channels']
       a_					:		Optional[Any]							=  1
       a_					:		Optional[int]							=  channels_list[0]

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

              elif layer_type == "AttnDownBlock2D":
                     for j in range(__A				):
                            a_					:		List[Any]							=  F"""down_blocks.{i}.resnets.{j}"""
                            a_					:		Dict							=  F"""input_blocks.{current_layer}.0"""
                            a_					:		List[str]							=  True if j == 0 and downsample_block_has_skip else False
                            a_					:		Union[str, Any]							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A  ,    has_skip=__A				)
                            a_					:		Any							=  F"""down_blocks.{i}.attentions.{j}"""
                            a_					:		str							=  F"""input_blocks.{current_layer}.1"""
                            a_					:		Tuple							=  convert_attention(
                                __A  ,    __A  ,    __A  ,    __A  ,    __A				)
                            current_layer += 1

              if i != len(__A				) - 1:
                     a_					:		Optional[int]							=  F"""down_blocks.{i}.downsamplers.0"""
                     a_					:		Tuple							=  F"""input_blocks.{current_layer}.0"""
                     a_					:		Optional[Any]							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A				)
                     current_layer += 1

              a_					:		Union[str, Any]							=  current_channels

       # hardcoded the mid-block for now
       a_					:		Tuple							=  'mid_block.resnets.0'
       a_					:		Optional[int]							=  'middle_block.0'
       a_					:		int							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A				)
       a_					:		Any							=  'mid_block.attentions.0'
       a_					:		int							=  'middle_block.1'
       a_					:		str							=  convert_attention(__A  ,    __A  ,    __A  ,    __A  ,    __A				)
       a_					:		Optional[Any]							=  'mid_block.resnets.1'
       a_					:		Optional[Any]							=  'middle_block.2'
       a_					:		List[str]							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A				)

       a_					:		Optional[int]							=  0
       a_					:		List[Any]							=  unet_config['up_block_types']

       for i, layer_type in enumerate(__A				):
              if layer_type == "ResnetUpsampleBlock2D":
                     for j in range(layers_per_block + 1				):
                            a_					:		List[str]							=  F"""up_blocks.{i}.resnets.{j}"""
                            a_					:		List[str]							=  F"""output_blocks.{current_layer}.0"""
                            a_					:		Dict							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A  ,    has_skip=__A				)
                            current_layer += 1

                     if i != len(__A				) - 1:
                            a_					:		Tuple							=  F"""up_blocks.{i}.upsamplers.0"""
                            a_					:		str							=  F"""output_blocks.{current_layer-1}.1"""
                            a_					:		List[str]							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A				)
              elif layer_type == "AttnUpBlock2D":
                     for j in range(layers_per_block + 1				):
                            a_					:		int							=  F"""up_blocks.{i}.resnets.{j}"""
                            a_					:		Optional[int]							=  F"""output_blocks.{current_layer}.0"""
                            a_					:		Any							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A  ,    has_skip=__A				)
                            a_					:		Any							=  F"""up_blocks.{i}.attentions.{j}"""
                            a_					:		Union[str, Any]							=  F"""output_blocks.{current_layer}.1"""
                            a_					:		List[str]							=  convert_attention(
                                __A  ,    __A  ,    __A  ,    __A  ,    __A				)
                            current_layer += 1

                     if i != len(__A				) - 1:
                            a_					:		Optional[int]							=  F"""up_blocks.{i}.upsamplers.0"""
                            a_					:		Tuple							=  F"""output_blocks.{current_layer-1}.2"""
                            a_					:		Optional[Any]							=  convert_resnet(__A  ,    __A  ,    __A  ,    __A				)

       a_					:		List[str]							=  checkpoint['out.0.weight']
       a_					:		Dict							=  checkpoint['out.0.bias']
       a_					:		Any							=  checkpoint['out.2.weight']
       a_					:		List[str]							=  checkpoint['out.2.bias']

       return new_checkpoint


if __name__ == "__main__":
   UpperCAmelCase_      : str  					=       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.')

   UpperCAmelCase_      : List[str]  					=       parser.parse_args()
   UpperCAmelCase_      : Dict  					=       strabool(args.class_cond)

   UpperCAmelCase_      : Optional[Any]  					=       os.path.basename(args.unet_path)
   print(F'Checkpoint: {ckpt_name}')

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

   if not args.class_cond:
      UpperCAmelCase_      : List[Any]  					=       None

   UpperCAmelCase_      : Optional[Any]  					=       con_pt_to_diffuser(args.unet_path, unet_config)

   UpperCAmelCase_      : str  					=       UNetaDModel(**unet_config)
   image_unet.load_state_dict(converted_unet_ckpt)

   # Get scheduler config
   if "cd" in ckpt_name or "test" in ckpt_name:
      UpperCAmelCase_      : str  					=       CD_SCHEDULER_CONFIG
   elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
      UpperCAmelCase_      : 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)):
      UpperCAmelCase_      : Union[str, Any]  					=       CT_LSUN_256_SCHEDULER_CONFIG
   else:
      raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

   UpperCAmelCase_      : Optional[int]  					=       CMStochasticIterativeScheduler(**scheduler_config)

   UpperCAmelCase_      : Any  					=       ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
   consistency_model.save_pretrained(args.dump_path)








lowerCamelCase				:		Tuple     =   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       int	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [False] * len(lowercase     )
      lowerCamelCase_		     =    [s]
      lowerCamelCase_		     =    True

      while queue:
            lowerCamelCase_		     =    queue.pop(0     )
            for ind in range(len(graph[u]     )     ):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(lowercase     )
                        lowerCamelCase_		     =    True
                        lowerCamelCase_		     =    u

      return visited[t]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       Tuple	,							lowercase	:       Tuple     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [-1] * (len(lowercase     ))
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    []
      lowerCamelCase_		     =    [i[:] for i in graph]  # Record original cut, copy.
      while bfs(lowercase	,							lowercase	,							lowercase	,							lowercase     ):
            lowerCamelCase_		     =    float('Inf'     )
            lowerCamelCase_		     =    sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase_		     =    min(lowercase	,							graph[parent[s]][s]     )
                  lowerCamelCase_		     =    parent[s]

            max_flow += path_flow
            lowerCamelCase_		     =    sink

            while v != source:
                  lowerCamelCase_		     =    parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase_		     =    parent[v]

      for i in range(len(lowercase     )     ):
            for j in range(len(graph[0]     )     ):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)     )

      return res


if __name__ == "__main__":
 print(mincut(test_graph, source=0, sink=5))



