

"""simple docstring"""


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": 1_000,
    "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": 1_000,
    "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													={
    "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													={
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCAmelCase													={
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCAmelCase													={
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}



def   _A					(	_a	:						Tuple				):



      """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   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[Any]     ,   _a	:						Any     ,   _a	:						int     ,   _a	:						Union[str, Any]=False				):



      """simple docstring"""






      A				  =		checkpoint[f'{old_prefix}.in_layers.0.weight']
      A				  =		checkpoint[f'{old_prefix}.in_layers.0.bias']
      A				  =		checkpoint[f'{old_prefix}.in_layers.2.weight']
      A				  =		checkpoint[f'{old_prefix}.in_layers.2.bias']
      A				  =		checkpoint[f'{old_prefix}.emb_layers.1.weight']
      A				  =		checkpoint[f'{old_prefix}.emb_layers.1.bias']
      A				  =		checkpoint[f'{old_prefix}.out_layers.0.weight']
      A				  =		checkpoint[f'{old_prefix}.out_layers.0.bias']
      A				  =		checkpoint[f'{old_prefix}.out_layers.3.weight']
      A				  =		checkpoint[f'{old_prefix}.out_layers.3.bias']

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

      return new_checkpoint



def   _A					(	_a	:						Any     ,   _a	:						Union[str, Any]     ,   _a	:						str     ,   _a	:						Tuple     ,   _a	:						Union[str, Any]=None				):



      """simple docstring"""






      A  ,			A  ,			A				  =		checkpoint[f'{old_prefix}.qkv.weight'].chunk(3     ,   dim=0				)
      A  ,			A  ,			A				  =		checkpoint[f'{old_prefix}.qkv.bias'].chunk(3     ,   dim=0				)

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

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

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

      return new_checkpoint



def   _A					(	_a	:						str     ,   _a	:						Tuple				):



      """simple docstring"""






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

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

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

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

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

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

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

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

            A				  =		current_channels

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

      A				  =		0
      A				  =		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				  =		f'up_blocks.{i}.resnets.{j}'
                        A				  =		f'output_blocks.{current_layer}.0'
                        A				  =		convert_resnet(_a     ,   _a     ,   _a     ,   _a     ,   has_skip=_a				)
                        current_layer += 1

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

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

      A				  =		checkpoint["""out.0.weight"""]
      A				  =		checkpoint["""out.0.bias"""]
      A				  =		checkpoint["""out.2.weight"""]
      A				  =		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)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   field(default='''summarization'''     ,   metadata={'''include_in_asdict_even_if_is_default''': True}    )
    _lowerCamelCase					=   Features({'''text''': Value('''string'''    )}    )
    _lowerCamelCase					=   Features({'''summary''': Value('''string'''    )}    )
    _lowerCamelCase					=   "text"
    _lowerCamelCase					=   "summary"




    @property
    def UpperCamelCase__		( self    )    ->      Dict[str, str]:
          return {self.text_column: "text", self.summary_column: "summary"}




