





'''simple docstring'''




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A:					Optional[int]							=				{
    """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""",
}

_A:					str							=				{
    """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""",
}

_A:					str							=				{
    """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:					str							=				{
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_A:					Optional[Any]							=				{
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_A:					List[Any]							=				{
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Optional[Any]:
 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     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=False    )->     Union[str, Any]:
 __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     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=None    )->     Any:
 __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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 __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__":
    _A:					List[Any]							=				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:					List[Any]							=				parser.parse_args()
    _A:					Union[str, Any]							=				strabool(args.class_cond)

    _A:					Dict							=				os.path.basename(args.unet_path)
    print(F"""Checkpoint: {ckpt_name}""")

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

    if not args.class_cond:
        _A:					Optional[Any]							=				None

    _A:					Optional[int]							=				con_pt_to_diffuser(args.unet_path, unet_config)

    _A:					Union[str, Any]							=				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:					Union[str, Any]							=				CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        _A:					Dict							=				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:					Union[str, Any]							=				CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

    _A:					Tuple							=				CMStochasticIterativeScheduler(**scheduler_config)

    _A:					int							=				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
    consistency_model.save_pretrained(args.dump_path)












'''simple docstring'''




import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
    import sqlitea

    import sqlalchemy






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A = None     ,			__A = None     ,			__A = False     ,			**__A     ,			):
     super().__init__(features=__A     ,			cache_dir=__A     ,			keep_in_memory=__A     ,			**__A					)
     __UpperCAmelCase					=   Sql(
         cache_dir=__A     ,			features=__A     ,			sql=__A     ,			con=__A     ,			**__A     ,			)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None

     self.builder.download_and_prepare(
         download_config=__A     ,			download_mode=__A     ,			verification_mode=__A     ,			base_path=__A     ,			)

     # Build dataset for splits
     __UpperCAmelCase					=   self.builder.as_dataset(
         split='train'     ,			verification_mode=__A     ,			in_memory=self.keep_in_memory					)
     return dataset






class UpperCAmelCase							:



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     if num_proc is not None and num_proc <= 0:
      raise ValueError(f'num_proc {num_proc} must be an integer > 0.'					)

     __UpperCAmelCase					=   dataset
     __UpperCAmelCase					=   name
     __UpperCAmelCase					=   con
     __UpperCAmelCase					=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     __UpperCAmelCase					=   num_proc
     __UpperCAmelCase					=   to_sql_kwargs



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.to_sql_kwargs.pop('sql'     ,			__A					)
     __UpperCAmelCase					=   self.to_sql_kwargs.pop('con'     ,			__A					)
     __UpperCAmelCase					=   self.to_sql_kwargs.pop('index'     ,			__A					)

     __UpperCAmelCase					=   self._write(index=__A     ,			**self.to_sql_kwargs					)
     return written



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   args
     __UpperCAmelCase					=   {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
     __UpperCAmelCase					=   query_table(
         table=self.dataset.data     ,			key=slice(__A     ,			offset + self.batch_size					)     ,			indices=self.dataset._indices     ,			)
     __UpperCAmelCase					=   batch.to_pandas()
     __UpperCAmelCase					=   df.to_sql(self.name     ,			self.con     ,			index=__A     ,			**__A					)
     return num_rows or len(__A					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			**__A					):
     __UpperCAmelCase					=   0

     if self.num_proc is None or self.num_proc == 1:
      for offset in logging.tqdm(
          range(0     ,			len(self.dataset					)     ,			self.batch_size					)     ,			unit='ba'     ,			disable=not logging.is_progress_bar_enabled()     ,			desc='Creating SQL from Arrow format'     ,			):
       written += self._batch_sql((offset, index, to_sql_kwargs)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   len(self.dataset					), self.batch_size
      with multiprocessing.Pool(self.num_proc					) as pool:
       for num_rows in logging.tqdm(
           pool.imap(
               self._batch_sql     ,			[(offset, index, to_sql_kwargs) for offset in range(0     ,			__A     ,			__A					)]     ,			)     ,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,			unit='ba'     ,			disable=not logging.is_progress_bar_enabled()     ,			desc='Creating SQL from Arrow format'     ,			):
        written += num_rows

     return written





