


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A			=						{
    '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',
}

_A			=						{
    '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',
}

_A			=						{
    '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',
}

_A			=						{
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_A			=						{
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_A			=						{
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]    ):
     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__      : Any      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : int=False    ):
     __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    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : List[Any]=None    ):
     __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    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=torch.load(SCREAMING_SNAKE_CASE__      ,					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(SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=channels_list[i]
          __UpperCamelCase   							=current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(SCREAMING_SNAKE_CASE__    ):
                    __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(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__    ):
                    __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(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					has_skip=SCREAMING_SNAKE_CASE__    )
                    __UpperCamelCase   							=F'down_blocks.{i}.attentions.{j}'
                    __UpperCamelCase   							=F'input_blocks.{current_layer}.1'
                    __UpperCamelCase   							=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:
               __UpperCamelCase   							=F'down_blocks.{i}.downsamplers.0'
               __UpperCamelCase   							=F'input_blocks.{current_layer}.0'
               __UpperCamelCase   							=convert_resnet(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
               current_layer += 1

          __UpperCamelCase   							=current_channels

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

     __UpperCamelCase   							=0
     __UpperCamelCase   							=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    ):
                    __UpperCamelCase   							=F'up_blocks.{i}.resnets.{j}'
                    __UpperCamelCase   							=F'output_blocks.{current_layer}.0'
                    __UpperCamelCase   							=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:
                    __UpperCamelCase   							=F'up_blocks.{i}.upsamplers.0'
                    __UpperCamelCase   							=F'output_blocks.{current_layer-1}.1'
                    __UpperCamelCase   							=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    ):
                    __UpperCamelCase   							=F'up_blocks.{i}.resnets.{j}'
                    __UpperCamelCase   							=F'output_blocks.{current_layer}.0'
                    __UpperCamelCase   							=convert_resnet(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					has_skip=SCREAMING_SNAKE_CASE__    )
                    __UpperCamelCase   							=F'up_blocks.{i}.attentions.{j}'
                    __UpperCamelCase   							=F'output_blocks.{current_layer}.1'
                    __UpperCamelCase   							=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:
                    __UpperCamelCase   							=F'up_blocks.{i}.upsamplers.0'
                    __UpperCamelCase   							=F'output_blocks.{current_layer-1}.2'
                    __UpperCamelCase   							=convert_resnet(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     __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__":
   _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)









import pprint

import requests

_A			=						'https://zenquotes.io/api'

def 						_UpperCAmelCase    (      ):
     return requests.get(API_ENDPOINT_URL + '/today'    ).json()

def 						_UpperCAmelCase    (      ):
     return requests.get(API_ENDPOINT_URL + '/random'    ).json()


if __name__ == "__main__":
   _A			=						random_quotes()
   pprint.pprint(response)





