

"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCamelCase										=		{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 10_00,
    "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",
}

__lowerCamelCase										=		{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 10_00,
    "block_out_channels": [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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",
}

__lowerCamelCase										=		{
    "sample_size": 2_56,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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",
}

__lowerCamelCase										=		{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCamelCase										=		{
    "num_train_timesteps": 2_01,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCamelCase										=		{
    "num_train_timesteps": 1_51,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 	lowercase      (   __UpperCamelCase						) ->      Dict:
 if isinstance(__snake_case      ,   __snake_case						):
  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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=False						) ->      str:
 __magic_name__     =      checkpoint[f'''{old_prefix}.in_layers.0.weight''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.in_layers.0.bias''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.in_layers.2.weight''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.in_layers.2.bias''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.out_layers.0.weight''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.out_layers.0.bias''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.out_layers.3.weight''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.out_layers.3.bias''']

 if has_skip:
  __magic_name__     =      checkpoint[f'''{old_prefix}.skip_connection.weight''']
  __magic_name__     =      checkpoint[f'''{old_prefix}.skip_connection.bias''']

 return new_checkpoint





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=None						) ->      str:
 __magic_name__   ,						__magic_name__   ,						__magic_name__     =      checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3      ,   dim=0						)
 __magic_name__   ,						__magic_name__   ,						__magic_name__     =      checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3      ,   dim=0						)

 __magic_name__     =      checkpoint[f'''{old_prefix}.norm.weight''']
 __magic_name__     =      checkpoint[f'''{old_prefix}.norm.bias''']

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

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

 return new_checkpoint





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Any:
 __magic_name__     =      torch.load(__snake_case      ,   map_location='''cpu'''						)
 __magic_name__     =      {}

 __magic_name__     =      checkpoint['''time_embed.0.weight''']
 __magic_name__     =      checkpoint['''time_embed.0.bias''']
 __magic_name__     =      checkpoint['''time_embed.2.weight''']
 __magic_name__     =      checkpoint['''time_embed.2.bias''']

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

 __magic_name__     =      checkpoint['''input_blocks.0.0.weight''']
 __magic_name__     =      checkpoint['''input_blocks.0.0.bias''']

 __magic_name__     =      unet_config['''down_block_types''']
 __magic_name__     =      unet_config['''layers_per_block''']
 __magic_name__     =      unet_config['''attention_head_dim''']
 __magic_name__     =      unet_config['''block_out_channels''']
 __magic_name__     =      1
 __magic_name__     =      channels_list[0]

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

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

  if i != len(__snake_case						) - 1:
   __magic_name__     =      f'''down_blocks.{i}.downsamplers.0'''
   __magic_name__     =      f'''input_blocks.{current_layer}.0'''
   __magic_name__     =      convert_resnet(__snake_case      ,   __snake_case      ,   __snake_case      ,   __snake_case						)
   current_layer += 1

  __magic_name__     =      current_channels

 # hardcoded the mid-block for now
 __magic_name__     =      '''mid_block.resnets.0'''
 __magic_name__     =      '''middle_block.0'''
 __magic_name__     =      convert_resnet(__snake_case      ,   __snake_case      ,   __snake_case      ,   __snake_case						)
 __magic_name__     =      '''mid_block.attentions.0'''
 __magic_name__     =      '''middle_block.1'''
 __magic_name__     =      convert_attention(__snake_case      ,   __snake_case      ,   __snake_case      ,   __snake_case      ,   __snake_case						)
 __magic_name__     =      '''mid_block.resnets.1'''
 __magic_name__     =      '''middle_block.2'''
 __magic_name__     =      convert_resnet(__snake_case      ,   __snake_case      ,   __snake_case      ,   __snake_case						)

 __magic_name__     =      0
 __magic_name__     =      unet_config['''up_block_types''']

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

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

   if i != len(__snake_case						) - 1:
    __magic_name__     =      f'''up_blocks.{i}.upsamplers.0'''
    __magic_name__     =      f'''output_blocks.{current_layer-1}.2'''
    __magic_name__     =      convert_resnet(__snake_case      ,   __snake_case      ,   __snake_case      ,   __snake_case						)

 __magic_name__     =      checkpoint['''out.0.weight''']
 __magic_name__     =      checkpoint['''out.0.bias''']
 __magic_name__     =      checkpoint['''out.2.weight''']
 __magic_name__     =      checkpoint['''out.2.bias''']

 return new_checkpoint


if __name__ == "__main__":
  __lowerCamelCase										=		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.")

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

  __lowerCamelCase										=		os.path.basename(args.unet_path)
  print(f"""Checkpoint: {ckpt_name}""")

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

  if not args.class_cond:
    __lowerCamelCase										=		None

  __lowerCamelCase										=		con_pt_to_diffuser(args.unet_path, unet_config)

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

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    __lowerCamelCase										=		CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __lowerCamelCase										=		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)):
    __lowerCamelCase										=		CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  __lowerCamelCase										=		CMStochasticIterativeScheduler(**scheduler_config)

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




"""simple docstring"""






from collections.abc import Sequence
from queue import Queue
class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None ,						UpperCamelCase_=None       ):
      __magic_name__     =      start
      __magic_name__     =      end
      __magic_name__     =      val
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      left
      __magic_name__     =      right







     def __repr__(      self       ):
      return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''


class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      collection
      __magic_name__     =      function
      if self.collection:
       __magic_name__     =      self._build_tree(0 ,						len(UpperCamelCase_       ) - 1       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      self._update_tree(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      return self._query_range(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if start == end:
       return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.collection[start]       )
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      self._build_tree(UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self._build_tree(mid + 1 ,						UpperCamelCase_       )
      return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.fn(left.val ,						right.val       ) ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if node.start == i and node.end == i:
       __magic_name__     =      val
       return
      if i <= node.mid:
       self._update_tree(node.left ,						UpperCamelCase_ ,						UpperCamelCase_       )
      else:
       self._update_tree(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self.fn(node.left.val ,						node.right.val       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if node.start == i and node.end == j:
       return node.val

      if i <= node.mid:
       if j <= node.mid:
        # range in left child tree
        return self._query_range(node.left ,						UpperCamelCase_ ,						UpperCamelCase_       )
       else:
        # range in left child tree and right child tree
        return self.fn(
            self._query_range(node.left ,						UpperCamelCase_ ,						node.mid       ) ,						self._query_range(node.right ,						node.mid + 1 ,						UpperCamelCase_       ) ,						)
      else:
       # range in right child tree
       return self._query_range(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      if self.root is not None:
       __magic_name__     =      Queue()
       queue.put(self.root       )
       while not queue.empty():
        __magic_name__     =      queue.get()
        yield node

        if node.left is not None:
         queue.put(node.left       )

        if node.right is not None:
         queue.put(node.right       )


if __name__ == "__main__":
 import operator

 for fn in [operator.add, max, min]:
  print("*" * 50)
  __lowerCamelCase										=		SegmentTree([2, 1, 5, 3, 4], fn)
  for node in arr.traverse():
   print(node)
  print()

  arr.update(1, 5)
  for node in arr.traverse():
   print(node)
  print()

  print(arr.query_range(3, 4))  # 7
  print(arr.query_range(2, 2))  # 5
  print(arr.query_range(1, 3))  # 13
  print()

