'''simple docstring'''




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': 0.002,
    'sigma_max': 80.0,
}

_A								=   {
    'num_train_timesteps': 2_0_1,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_A								=   {
    'num_train_timesteps': 1_5_1,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False							):
     lowercase_					:       List[str]     				=					checkpoint[f'''{old_prefix}.in_layers.0.weight''']
     lowercase_					:       Dict     				=					checkpoint[f'''{old_prefix}.in_layers.0.bias''']
     lowercase_					:       Tuple     				=					checkpoint[f'''{old_prefix}.in_layers.2.weight''']
     lowercase_					:       int     				=					checkpoint[f'''{old_prefix}.in_layers.2.bias''']
     lowercase_					:       List[str]     				=					checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
     lowercase_					:       str     				=					checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
     lowercase_					:       Optional[int]     				=					checkpoint[f'''{old_prefix}.out_layers.0.weight''']
     lowercase_					:       int     				=					checkpoint[f'''{old_prefix}.out_layers.0.bias''']
     lowercase_					:       Union[str, Any]     				=					checkpoint[f'''{old_prefix}.out_layers.3.weight''']
     lowercase_					:       Dict     				=					checkpoint[f'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          lowercase_					:       Optional[int]     				=					checkpoint[f'''{old_prefix}.skip_connection.weight''']
          lowercase_					:       Any     				=					checkpoint[f'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=None							):
     lowercase_				,lowercase_				,lowercase_					:       List[Any]     				=					checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3  ,       dim=0							)
     lowercase_				,lowercase_				,lowercase_					:       Optional[int]     				=					checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3  ,       dim=0							)

     lowercase_					:       int     				=					checkpoint[f'''{old_prefix}.norm.weight''']
     lowercase_					:       Optional[Any]     				=					checkpoint[f'''{old_prefix}.norm.bias''']

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

     lowercase_					:       Union[str, Any]     				=					(
         checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
     )
     lowercase_					:       Optional[int]     				=					checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

     return new_checkpoint

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Union[str, Any]     				=					torch.load(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)
     lowercase_					:       Optional[Any]     				=					{}

     lowercase_					:       Any     				=					checkpoint['time_embed.0.weight']
     lowercase_					:       Optional[int]     				=					checkpoint['time_embed.0.bias']
     lowercase_					:       List[str]     				=					checkpoint['time_embed.2.weight']
     lowercase_					:       Optional[Any]     				=					checkpoint['time_embed.2.bias']

     if unet_config["num_class_embeds"] is not None:
          lowercase_					:       Union[str, Any]     				=					checkpoint['label_emb.weight']

     lowercase_					:       Optional[Any]     				=					checkpoint['input_blocks.0.0.weight']
     lowercase_					:       str     				=					checkpoint['input_blocks.0.0.bias']

     lowercase_					:       str     				=					unet_config['down_block_types']
     lowercase_					:       int     				=					unet_config['layers_per_block']
     lowercase_					:       str     				=					unet_config['attention_head_dim']
     lowercase_					:       Tuple     				=					unet_config['block_out_channels']
     lowercase_					:       Optional[int]     				=					1
     lowercase_					:       str     				=					channels_list[0]

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

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

          if i != len(SCREAMING_SNAKE_CASE_							) - 1:
               lowercase_					:       str     				=					f'''down_blocks.{i}.downsamplers.0'''
               lowercase_					:       Union[str, Any]     				=					f'''input_blocks.{current_layer}.0'''
               lowercase_					:       Tuple     				=					convert_resnet(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               current_layer += 1

          lowercase_					:       Tuple     				=					current_channels

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

     lowercase_					:       Union[str, Any]     				=					0
     lowercase_					:       Optional[Any]     				=					unet_config['up_block_types']

     for i, layer_type in enumerate(SCREAMING_SNAKE_CASE_							):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1							):
                    lowercase_					:       List[str]     				=					f'''up_blocks.{i}.resnets.{j}'''
                    lowercase_					:       Tuple     				=					f'''output_blocks.{current_layer}.0'''
                    lowercase_					:       Optional[int]     				=					convert_resnet(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       has_skip=SCREAMING_SNAKE_CASE_							)
                    current_layer += 1

               if i != len(SCREAMING_SNAKE_CASE_							) - 1:
                    lowercase_					:       Dict     				=					f'''up_blocks.{i}.upsamplers.0'''
                    lowercase_					:       Optional[Any]     				=					f'''output_blocks.{current_layer-1}.1'''
                    lowercase_					:       int     				=					convert_resnet(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1							):
                    lowercase_					:       int     				=					f'''up_blocks.{i}.resnets.{j}'''
                    lowercase_					:       Dict     				=					f'''output_blocks.{current_layer}.0'''
                    lowercase_					:       Dict     				=					convert_resnet(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       has_skip=SCREAMING_SNAKE_CASE_							)
                    lowercase_					:       Optional[Any]     				=					f'''up_blocks.{i}.attentions.{j}'''
                    lowercase_					:       int     				=					f'''output_blocks.{current_layer}.1'''
                    lowercase_					:       Optional[Any]     				=					convert_attention(
                        SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
                    current_layer += 1

               if i != len(SCREAMING_SNAKE_CASE_							) - 1:
                    lowercase_					:       Optional[int]     				=					f'''up_blocks.{i}.upsamplers.0'''
                    lowercase_					:       Any     				=					f'''output_blocks.{current_layer-1}.2'''
                    lowercase_					:       Tuple     				=					convert_resnet(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Optional[int]     				=					checkpoint['out.0.weight']
     lowercase_					:       Any     				=					checkpoint['out.0.bias']
     lowercase_					:       Optional[int]     				=					checkpoint['out.2.weight']
     lowercase_					:       Union[str, Any]     				=					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)





'''simple docstring'''




from decimal import Decimal, getcontext
from math import ceil, factorial

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     if not isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
          raise TypeError('Undefined for non-integers'							)
     elif precision < 1:
          raise ValueError('Undefined for non-natural numbers'							)

     lowercase_					:       str     				=					precision
     lowercase_					:       List[str]     				=					ceil(precision / 14							)
     lowercase_					:       Union[str, Any]     				=					426_880 * Decimal(10_005							).sqrt()
     lowercase_					:       List[Any]     				=					1
     lowercase_					:       Optional[int]     				=					13_591_409
     lowercase_					:       Dict     				=					Decimal(SCREAMING_SNAKE_CASE_							)
     for k in range(1  ,       SCREAMING_SNAKE_CASE_							):
          lowercase_					:       List[str]     				=					factorial(6 * k							) // (factorial(3 * k							) * factorial(SCREAMING_SNAKE_CASE_							) ** 3)
          linear_term += 545_140_134
          exponential_term *= -262_537_412_640_768_000
          partial_sum += Decimal(multinomial_term * linear_term							) / exponential_term
     return str(constant_term / partial_sum							)[:-1]


if __name__ == "__main__":
 _A								=   5_0
 print(F"""The first {n} digits of pi is: {pi(n)}""")




