





"""simple docstring"""
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCamelCase_								=		{
    "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",
}

lowerCamelCase_								=		{
    "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",
}

lowerCamelCase_								=		{
    "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",
}

lowerCamelCase_								=		{
    "num_train_timesteps": 4_0,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowerCamelCase_								=		{
    "num_train_timesteps": 2_0_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowerCamelCase_								=		{
    "num_train_timesteps": 1_5_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}



def      __lowerCamelCase   (							a_     :		Union[str, Any]							)			->      Any:
      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      __lowerCamelCase   (							a_     :		Tuple ,   a_     :		Tuple ,   a_     :		Dict ,   a_     :		Union[str, Any] ,   a_     :		Any=False							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  checkpoint[f'''{old_prefix}.in_layers.0.weight''']
      __SCREAMING_SNAKE_CASE   :List[Any]						=  checkpoint[f'''{old_prefix}.in_layers.0.bias''']
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  checkpoint[f'''{old_prefix}.in_layers.2.weight''']
      __SCREAMING_SNAKE_CASE   :List[str]						=  checkpoint[f'''{old_prefix}.in_layers.2.bias''']
      __SCREAMING_SNAKE_CASE   :int						=  checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  checkpoint[f'''{old_prefix}.out_layers.0.weight''']
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  checkpoint[f'''{old_prefix}.out_layers.0.bias''']
      __SCREAMING_SNAKE_CASE   :int						=  checkpoint[f'''{old_prefix}.out_layers.3.weight''']
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  checkpoint[f'''{old_prefix}.out_layers.3.bias''']

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

      return new_checkpoint



def      __lowerCamelCase   (							a_     :		List[str] ,   a_     :		Optional[int] ,   a_     :		Optional[int] ,   a_     :		Tuple ,   a_     :		int=None							)			->      Any:
      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :str						=  checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3 ,   dim=0							)
      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :List[str]						=  checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3 ,   dim=0							)

      __SCREAMING_SNAKE_CASE   :int						=  checkpoint[f'''{old_prefix}.norm.weight''']
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  checkpoint[f'''{old_prefix}.norm.bias''']

      __SCREAMING_SNAKE_CASE   :Tuple						=  weight_q.squeeze(-1							).squeeze(-1							)
      __SCREAMING_SNAKE_CASE   :List[str]						=  bias_q.squeeze(-1							).squeeze(-1							)
      __SCREAMING_SNAKE_CASE   :Dict						=  weight_k.squeeze(-1							).squeeze(-1							)
      __SCREAMING_SNAKE_CASE   :Tuple						=  bias_k.squeeze(-1							).squeeze(-1							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  weight_v.squeeze(-1							).squeeze(-1							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  bias_v.squeeze(-1							).squeeze(-1							)

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

      return new_checkpoint



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.load(a_ ,   map_location='''cpu'''							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {}

      __SCREAMING_SNAKE_CASE   :List[Any]						=  checkpoint['''time_embed.0.weight''']
      __SCREAMING_SNAKE_CASE   :Dict						=  checkpoint['''time_embed.0.bias''']
      __SCREAMING_SNAKE_CASE   :int						=  checkpoint['''time_embed.2.weight''']
      __SCREAMING_SNAKE_CASE   :Tuple						=  checkpoint['''time_embed.2.bias''']

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

      __SCREAMING_SNAKE_CASE   :Dict						=  checkpoint['''input_blocks.0.0.weight''']
      __SCREAMING_SNAKE_CASE   :List[str]						=  checkpoint['''input_blocks.0.0.bias''']

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  unet_config['''down_block_types''']
      __SCREAMING_SNAKE_CASE   :List[Any]						=  unet_config['''layers_per_block''']
      __SCREAMING_SNAKE_CASE   :List[Any]						=  unet_config['''attention_head_dim''']
      __SCREAMING_SNAKE_CASE   :str						=  unet_config['''block_out_channels''']
      __SCREAMING_SNAKE_CASE   :str						=  1
      __SCREAMING_SNAKE_CASE   :Any						=  channels_list[0]

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

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

            if i != len(a_							) - 1:
                  __SCREAMING_SNAKE_CASE   :Dict						=  f'''down_blocks.{i}.downsamplers.0'''
                  __SCREAMING_SNAKE_CASE   :List[str]						=  f'''input_blocks.{current_layer}.0'''
                  __SCREAMING_SNAKE_CASE   :Any						=  convert_resnet(a_ ,   a_ ,   a_ ,   a_							)
                  current_layer += 1

            __SCREAMING_SNAKE_CASE   :str						=  current_channels

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

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  0
      __SCREAMING_SNAKE_CASE   :Dict						=  unet_config['''up_block_types''']

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

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

                  if i != len(a_							) - 1:
                        __SCREAMING_SNAKE_CASE   :str						=  f'''up_blocks.{i}.upsamplers.0'''
                        __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  f'''output_blocks.{current_layer-1}.2'''
                        __SCREAMING_SNAKE_CASE   :List[str]						=  convert_resnet(a_ ,   a_ ,   a_ ,   a_							)

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  checkpoint['''out.0.weight''']
      __SCREAMING_SNAKE_CASE   :Tuple						=  checkpoint['''out.0.bias''']
      __SCREAMING_SNAKE_CASE   :Dict						=  checkpoint['''out.2.weight''']
      __SCREAMING_SNAKE_CASE   :str						=  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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_								=		{
    "configuration_blip_2": [
        "BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Blip2Config",
        "Blip2QFormerConfig",
        "Blip2VisionConfig",
    ],
    "processing_blip_2": ["Blip2Processor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Blip2Model",
      "Blip2QFormerModel",
      "Blip2PreTrainedModel",
      "Blip2ForConditionalGeneration",
      "Blip2VisionModel",
  ]

if TYPE_CHECKING:
  from .configuration_blip_a import (
      BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlipaConfig,
      BlipaQFormerConfig,
      BlipaVisionConfig,
  )
  from .processing_blip_a import BlipaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip_a import (
        BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipaForConditionalGeneration,
        BlipaModel,
        BlipaPreTrainedModel,
        BlipaQFormerModel,
        BlipaVisionModel,
    )

else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)