





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase_												=	{
    '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',
}

UpperCamelCase_												=	{
    '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',
}

UpperCamelCase_												=	{
    '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',
}

UpperCamelCase_												=	{
    'num_train_timesteps': 40,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}

UpperCamelCase_												=	{
    'num_train_timesteps': 2_01,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}

UpperCamelCase_												=	{
    'num_train_timesteps': 1_51,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}






def 				_UpperCAmelCase			(    A     ):




   '''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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=False     ):




   '''simple docstring'''

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

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

   return new_checkpoint






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=None     ):




   '''simple docstring'''

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

   UpperCAmelCase__						=checkpoint[F"""{old_prefix}.norm.weight"""]
   UpperCAmelCase__						=checkpoint[F"""{old_prefix}.norm.bias"""]

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

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

   return new_checkpoint






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=torch.load(A				,							map_location="cpu"     )
   UpperCAmelCase__						={}

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

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

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

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

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

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

      if i != len(A     ) - 1:
         UpperCAmelCase__						=F"""down_blocks.{i}.downsamplers.0"""
         UpperCAmelCase__						=F"""input_blocks.{current_layer}.0"""
         UpperCAmelCase__						=convert_resnet(A				,							A				,							A				,							A     )
         current_layer += 1

      UpperCAmelCase__						=current_channels

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

   UpperCAmelCase__						=0
   UpperCAmelCase__						=unet_config["up_block_types"]

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

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

         if i != len(A     ) - 1:
            UpperCAmelCase__						=F"""up_blocks.{i}.upsamplers.0"""
            UpperCAmelCase__						=F"""output_blocks.{current_layer-1}.2"""
            UpperCAmelCase__						=convert_resnet(A				,							A				,							A				,							A     )

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

   return new_checkpoint


if __name__ == "__main__":
    UpperCamelCase_												=	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_												=	parser.parse_args()
    UpperCamelCase_												=	strabool(args.class_cond)

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

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

    if not args.class_cond:
        UpperCamelCase_												=	None

    UpperCamelCase_												=	con_pt_to_diffuser(args.unet_path, unet_config)

    UpperCamelCase_												=	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_												=	CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        UpperCamelCase_												=	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_												=	CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

    UpperCamelCase_												=	CMStochasticIterativeScheduler(**scheduler_config)

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










from math import factorial






def 				_UpperCAmelCase			(    A = 20     ):




   '''simple docstring'''

   UpperCAmelCase__						=2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
   # 2, 3,...
   UpperCAmelCase__						=n // 2

   return int(factorial(A     ) / (factorial(A     ) * factorial(n - k     ))     )


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution(20))
    else:
        try:
            UpperCamelCase_												=	int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print('Invalid entry - please enter a number.')



