'''simple docstring'''
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase__  :      Tuple        =					{
    "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",
}

lowercase__  :      Optional[Any]        =					{
    "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",
}

lowercase__  :      Optional[Any]        =					{
    "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",
}

lowercase__  :      Optional[Any]        =					{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

lowercase__  :      List[str]        =					{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      if isinstance(lowerCamelCase__							,    lowerCamelCase__					):
            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       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  str=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  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Union[str, Any]=None					):


      '''simple docstring'''


      UpperCAmelCase_            =  checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3							,    dim=0					)
      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  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Tuple					):


      '''simple docstring'''


      UpperCAmelCase_            =  torch.load(lowerCamelCase__							,    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(lowerCamelCase__					):
            UpperCAmelCase_            =  channels_list[i]
            UpperCAmelCase_            =  current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(lowerCamelCase__					):
                        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(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    has_skip=lowerCamelCase__					)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(lowerCamelCase__					):
                        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(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    has_skip=lowerCamelCase__					)
                        UpperCAmelCase_            =  F"""down_blocks.{i}.attentions.{j}"""
                        UpperCAmelCase_            =  F"""input_blocks.{current_layer}.1"""
                        UpperCAmelCase_            =  convert_attention(
                            lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__					)
                        current_layer += 1

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

            UpperCAmelCase_            =  current_channels

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

      UpperCAmelCase_            =  0
      UpperCAmelCase_            =  unet_config["up_block_types"]

      for i, layer_type in enumerate(lowerCamelCase__					):
            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(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    has_skip=lowerCamelCase__					)
                        current_layer += 1

                  if i != len(lowerCamelCase__					) - 1:
                        UpperCAmelCase_            =  F"""up_blocks.{i}.upsamplers.0"""
                        UpperCAmelCase_            =  F"""output_blocks.{current_layer-1}.1"""
                        UpperCAmelCase_            =  convert_resnet(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__					)
            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(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    has_skip=lowerCamelCase__					)
                        UpperCAmelCase_            =  F"""up_blocks.{i}.attentions.{j}"""
                        UpperCAmelCase_            =  F"""output_blocks.{current_layer}.1"""
                        UpperCAmelCase_            =  convert_attention(
                            lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__					)
                        current_layer += 1

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

      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__":
      lowercase__  :      Optional[int]        =					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.")

      lowercase__  :      Any        =					parser.parse_args()
      lowercase__  :      Optional[Any]        =					strabool(args.class_cond)

      lowercase__  :      Any        =					os.path.basename(args.unet_path)
      print(F'''Checkpoint: {ckpt_name}''')

      # Get U-Net config
      if "imagenet64" in ckpt_name:
            lowercase__  :      Optional[int]        =					IMAGENET_64_UNET_CONFIG
      elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
            lowercase__  :      str        =					LSUN_256_UNET_CONFIG
      elif "test" in ckpt_name:
            lowercase__  :      List[Any]        =					TEST_UNET_CONFIG
      else:
            raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

      if not args.class_cond:
            lowercase__  :      List[str]        =					None

      lowercase__  :      str        =					con_pt_to_diffuser(args.unet_path, unet_config)

      lowercase__  :      str        =					UNetaDModel(**unet_config)
      image_unet.load_state_dict(converted_unet_ckpt)

      # Get scheduler config
      if "cd" in ckpt_name or "test" in ckpt_name:
            lowercase__  :      Union[str, Any]        =					CD_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
            lowercase__  :      List[Any]        =					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)):
            lowercase__  :      List[str]        =					CT_LSUN_256_SCHEDULER_CONFIG
      else:
            raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

      lowercase__  :      Optional[int]        =					CMStochasticIterativeScheduler(**scheduler_config)

      lowercase__  :      Optional[Any]        =					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
      consistency_model.save_pretrained(args.dump_path)






'''simple docstring'''
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase__  :      Tuple        =					pytest.mark.integration



@pytest.mark.parametrize('''path'''							,    ['''paws''', '''csv''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      inspect_dataset(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''					)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''					)
@pytest.mark.parametrize('''path'''							,    ['''accuracy''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      inspect_metric(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)
      assert info.config_name == config_name
      assert list(info.splits.keys()					) == expected_splits



@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, expected'''							,    [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_names(_UpperCamelCase					)
      assert expected in config_names



@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''							,    [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert list(infos.keys()					) == expected_configs
      UpperCAmelCase_            =  expected_configs[0]
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits_in_first_config



@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits





@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_split_names(_UpperCamelCase							,    config_name=_UpperCamelCase					)





