



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

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


A:   List[str]								=		{
    "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",
}

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

A:   Union[str, Any]								=		{
    "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",
}

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

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

A:   Union[str, Any]								=		{
    "num_train_timesteps": 1_5_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

def       _snake_case						(						UpperCamelCase						:		List[Any]						):
 if isinstance(UpperCamelCase			,		UpperCamelCase						):
  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       _snake_case						(						UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		List[Any]=False						):
 UpperCAmelCase	:					Dict	=	checkpoint[F"{old_prefix}.in_layers.0.weight"]
 UpperCAmelCase	:					Any	=	checkpoint[F"{old_prefix}.in_layers.0.bias"]
 UpperCAmelCase	:					Optional[Any]	=	checkpoint[F"{old_prefix}.in_layers.2.weight"]
 UpperCAmelCase	:					List[Any]	=	checkpoint[F"{old_prefix}.in_layers.2.bias"]
 UpperCAmelCase	:					Optional[Any]	=	checkpoint[F"{old_prefix}.emb_layers.1.weight"]
 UpperCAmelCase	:					Optional[Any]	=	checkpoint[F"{old_prefix}.emb_layers.1.bias"]
 UpperCAmelCase	:					Union[str, Any]	=	checkpoint[F"{old_prefix}.out_layers.0.weight"]
 UpperCAmelCase	:					Dict	=	checkpoint[F"{old_prefix}.out_layers.0.bias"]
 UpperCAmelCase	:					str	=	checkpoint[F"{old_prefix}.out_layers.3.weight"]
 UpperCAmelCase	:					str	=	checkpoint[F"{old_prefix}.out_layers.3.bias"]

 if has_skip:
  UpperCAmelCase	:					List[Any]	=	checkpoint[F"{old_prefix}.skip_connection.weight"]
  UpperCAmelCase	:					Optional[Any]	=	checkpoint[F"{old_prefix}.skip_connection.bias"]

 return new_checkpoint

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[Any]=None						):
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	checkpoint[F"{old_prefix}.qkv.weight"].chunk(3			,		dim=0						)
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	checkpoint[F"{old_prefix}.qkv.bias"].chunk(3			,		dim=0						)

 UpperCAmelCase	:					List[str]	=	checkpoint[F"{old_prefix}.norm.weight"]
 UpperCAmelCase	:					List[Any]	=	checkpoint[F"{old_prefix}.norm.bias"]

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

 UpperCAmelCase	:					Optional[int]	=	(
     checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1						).squeeze(-1						)
 )
 UpperCAmelCase	:					List[str]	=	checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1						).squeeze(-1						)

 return new_checkpoint

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		List[str]						):
 UpperCAmelCase	:					Tuple	=	torch.load(UpperCamelCase			,		map_location="""cpu"""						)
 UpperCAmelCase	:					str	=	{}

 UpperCAmelCase	:					int	=	checkpoint["""time_embed.0.weight"""]
 UpperCAmelCase	:					Union[str, Any]	=	checkpoint["""time_embed.0.bias"""]
 UpperCAmelCase	:					List[Any]	=	checkpoint["""time_embed.2.weight"""]
 UpperCAmelCase	:					str	=	checkpoint["""time_embed.2.bias"""]

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

 UpperCAmelCase	:					Optional[Any]	=	checkpoint["""input_blocks.0.0.weight"""]
 UpperCAmelCase	:					List[str]	=	checkpoint["""input_blocks.0.0.bias"""]

 UpperCAmelCase	:					Optional[Any]	=	unet_config["""down_block_types"""]
 UpperCAmelCase	:					Any	=	unet_config["""layers_per_block"""]
 UpperCAmelCase	:					Any	=	unet_config["""attention_head_dim"""]
 UpperCAmelCase	:					Optional[int]	=	unet_config["""block_out_channels"""]
 UpperCAmelCase	:					Tuple	=	1
 UpperCAmelCase	:					Dict	=	channels_list[0]

 for i, layer_type in enumerate(UpperCamelCase						):
  UpperCAmelCase	:					List[Any]	=	channels_list[i]
  UpperCAmelCase	:					Any	=	current_channels != prev_channels
  if layer_type == "ResnetDownsampleBlock2D":
   for j in range(UpperCamelCase						):
    UpperCAmelCase	:					List[Any]	=	F"down_blocks.{i}.resnets.{j}"
    UpperCAmelCase	:					Union[str, Any]	=	F"input_blocks.{current_layer}.0"
    UpperCAmelCase	:					Union[str, Any]	=	True if j == 0 and downsample_block_has_skip else False
    UpperCAmelCase	:					int	=	convert_resnet(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		has_skip=UpperCamelCase						)
    current_layer += 1

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

  if i != len(UpperCamelCase						) - 1:
   UpperCAmelCase	:					Tuple	=	F"down_blocks.{i}.downsamplers.0"
   UpperCAmelCase	:					Tuple	=	F"input_blocks.{current_layer}.0"
   UpperCAmelCase	:					Union[str, Any]	=	convert_resnet(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
   current_layer += 1

  UpperCAmelCase	:					List[str]	=	current_channels

 # hardcoded the mid-block for now
 UpperCAmelCase	:					Any	=	"""mid_block.resnets.0"""
 UpperCAmelCase	:					Optional[Any]	=	"""middle_block.0"""
 UpperCAmelCase	:					Optional[Any]	=	convert_resnet(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
 UpperCAmelCase	:					str	=	"""mid_block.attentions.0"""
 UpperCAmelCase	:					Optional[int]	=	"""middle_block.1"""
 UpperCAmelCase	:					Dict	=	convert_attention(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
 UpperCAmelCase	:					Any	=	"""mid_block.resnets.1"""
 UpperCAmelCase	:					Tuple	=	"""middle_block.2"""
 UpperCAmelCase	:					Tuple	=	convert_resnet(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

 UpperCAmelCase	:					List[Any]	=	0
 UpperCAmelCase	:					str	=	unet_config["""up_block_types"""]

 for i, layer_type in enumerate(UpperCamelCase						):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1						):
    UpperCAmelCase	:					str	=	F"up_blocks.{i}.resnets.{j}"
    UpperCAmelCase	:					str	=	F"output_blocks.{current_layer}.0"
    UpperCAmelCase	:					Dict	=	convert_resnet(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		has_skip=UpperCamelCase						)
    current_layer += 1

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

   if i != len(UpperCamelCase						) - 1:
    UpperCAmelCase	:					int	=	F"up_blocks.{i}.upsamplers.0"
    UpperCAmelCase	:					Dict	=	F"output_blocks.{current_layer-1}.2"
    UpperCAmelCase	:					Union[str, Any]	=	convert_resnet(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

 UpperCAmelCase	:					List[Any]	=	checkpoint["""out.0.weight"""]
 UpperCAmelCase	:					str	=	checkpoint["""out.0.bias"""]
 UpperCAmelCase	:					str	=	checkpoint["""out.2.weight"""]
 UpperCAmelCase	:					Tuple	=	checkpoint["""out.2.bias"""]

 return new_checkpoint


if __name__ == "__main__":
      A:   Tuple								=		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:   str								=		parser.parse_args()
      A:   List[Any]								=		strabool(args.class_cond)

      A:   str								=		os.path.basename(args.unet_path)
      print(f"""Checkpoint: {ckpt_name}""")

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

      if not args.class_cond:
            A:   List[Any]								=		None

      A:   Any								=		con_pt_to_diffuser(args.unet_path, unet_config)

      A:   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:
            A:   Tuple								=		CD_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
            A:   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)):
            A:   int								=		CT_LSUN_256_SCHEDULER_CONFIG
      else:
            raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

      A:   Tuple								=		CMStochasticIterativeScheduler(**scheduler_config)

      A:   Union[str, Any]								=		ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
      consistency_model.save_pretrained(args.dump_path)





"""simple docstring"""
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


A:   str								=		logging.get_logger(__name__)

A:   List[Any]								=		{"vocab_file": "vocab.txt"}

A:   List[str]								=		{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

A:   Dict								=		{
    "facebook/esm2_t6_8M_UR50D": 1_0_2_4,
    "facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}

def       _snake_case						(						UpperCamelCase						:		int						):
 with open(UpperCamelCase			,		"""r"""						) as f:
  UpperCAmelCase	:					int	=	f.read().splitlines()
  return [l.strip() for l in lines]



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Union[str, Any]						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   str						=						['input_ids', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<cls>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<mask>"      ,     _SCREAMING_SNAKE_CASE="<eos>"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	load_vocab_file(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	dict(enumerate(self.all_tokens						)						)
    UpperCAmelCase	:					List[str]	=	{tok: ind for ind, tok in enumerate(self.all_tokens						)}
    UpperCAmelCase	:					Any	=	unk_token
    UpperCAmelCase	:					str	=	cls_token
    UpperCAmelCase	:					int	=	pad_token
    UpperCAmelCase	:					Tuple	=	mask_token
    UpperCAmelCase	:					str	=	eos_token
    UpperCAmelCase	:					List[str]	=	self.all_tokens
    self._create_trie(self.unique_no_split_tokens						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    return text.split()

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Union[str, Any]:





    '''simple docstring'''




    return len(self._id_to_token						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return {token: i for i, token in enumerate(self.all_tokens						)}

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	[self.cls_token_id]
    UpperCAmelCase	:					Tuple	=	[self.eos_token_id]  # No sep token in ESM vocabulary
    if token_ids_a is None:
     if self.eos_token_id is None:
      return cls + token_ids_a
     else:
      return cls + token_ids_a + sep
    elif self.eos_token_id is None:
     raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""						)
    return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False						)  ->      List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     if token_ids_a is not None:
      raise ValueError(
          """You should not supply a second sequence if the provided sequence of """
          """ids is already formatted with special tokens for the model."""						)

     return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
    UpperCAmelCase	:					str	=	[1] + ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1]
    if token_ids_a is not None:
     mask += [0] * len(_SCREAMING_SNAKE_CASE						) + [1]
    return mask

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	os.path.join(_SCREAMING_SNAKE_CASE      ,     (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""						)
    with open(_SCREAMING_SNAKE_CASE      ,     """w"""						) as f:
     f.write("""\n""".join(self.all_tokens						)						)
    return (vocab_file,)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = False						)  ->      int:





    '''simple docstring'''




    return super()._add_tokens(_SCREAMING_SNAKE_CASE      ,     special_tokens=_SCREAMING_SNAKE_CASE						)
