



from typing import Dict, List, Optional, Tuple

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


UpperCAmelCase							=       logging.get_logger(__name__)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Tuple       =       ["""input_ids""", """attention_mask"""]


							def __init__(				self	,	a_="[PAD]"	,	a_="[BOS]"	,	a_="[EOS]"	,	a_="[MASK]"	,	a_="[CLS]"	,	a_="[SEP]"	,	a_=2_048	,	**a_	,	):
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else pad_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else bos_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else eos_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else mask_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else cls_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else sep_token

											super().__init__(
											    pad_token=a_	,	bos_token=a_	,	eos_token=a_	,	mask_token=a_	,	cls_token=a_	,	sep_token=a_	,	model_max_length=a_	,	**a_	,	)

											a__					   =		2**8  # utf is 8 bits

											# define special tokens dict
											a__					   =		{
											    self.pad_token: 0,
											    self.bos_token: 1,
											    self.eos_token: 2,
											    self.mask_token: 3,
											    self.cls_token: 4,
											    self.sep_token: 5,
											}
											a__					   =		len(self.special_tokens_encoder		)
											a__					   =		{v: k for k, v in self.special_tokens_encoder.items()}


							def _a   (				self		):
											a__					   =		self.special_tokens_encoder.copy()
											vocab.update(self.added_tokens_encoder		)
											for i in range(self._utf_vocab_size		):
															a__					   =		chr(a_		)
															a__					   =		i + len(self.special_tokens_encoder		)
											return vocab


							@property
							def _a   (				self		):
											return self._utf_vocab_size + self._num_special_tokens


							def _a   (				self	,	a_	,	a_ = None	,	a_ = False		):
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a_	,	token_ids_a=a_	,	already_has_special_tokens=a_		)

											# normal case: some special tokens
											if token_ids_a is None:
															return [1] + [0] * len(a_		) + [1]
											return [1] + ([0] * len(a_		)) + [1] + ([0] * len(a_		)) + [1]


							def _a   (				self	,	a_	,	a_ = None		):
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											else:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


							def _a   (				self	,	a_		):
											a__					   =		[chr(a_		) for i in text.encode("""utf-8"""		)]
											return tokens


							def _a   (				self	,	a_		):
											if token in self.special_tokens_encoder:
															a__					   =		self.special_tokens_encoder[token]
											elif token in self.added_tokens_encoder:
															a__					   =		self.added_tokens_encoder[token]
											elif len(a_		) != 1:
															a__					   =		self.unk_token_id
											else:
															a__					   =		ord(a_		) + self._num_special_tokens
											return token_id


							def _a   (				self	,	a_		):
											if index in self.special_tokens_decoder:
															a__					   =		self.special_tokens_decoder[index]
											elif index in self.added_tokens_decoder:
															a__					   =		self.added_tokens_decoder[index]
											else:
															a__					   =		chr(index - self._num_special_tokens		)
											return token


							def _a   (				self	,	a_		):
											a__					   =		B""""""
											for token in tokens:
															if token in self.special_tokens_decoder:
																			a__					   =		self.special_tokens_decoder[token].encode("""utf-8"""		)
															elif token in self.added_tokens_decoder:
																			a__					   =		self.special_tokens_decoder[token].encode("""utf-8"""		)
															elif token in self.special_tokens_encoder:
																			a__					   =		token.encode("""utf-8"""		)
															elif token in self.added_tokens_encoder:
																			a__					   =		token.encode("""utf-8"""		)
															else:
																			a__					   =		bytes([ord(a_		)]		)
															bstring += tok_string
											a__					   =		bstring.decode("""utf-8"""	,	errors="""replace"""		)
											return string







							def _a   (				self	,	a_	,	a_ = None		):
											return ()



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

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A__ :		Tuple					={512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							Tuple:





  """simple docstring"""

  __A		:  List[Any]       				=		F"pretrained_models/{model_name}"
  if not os.path.isfile(__SCREAMING_SNAKE_CASE			):
    os.makedirs("""pretrained_models"""      ,  exist_ok=__SCREAMING_SNAKE_CASE			)
    __A		:  int       				=		F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
    download_url(__SCREAMING_SNAKE_CASE      ,  """pretrained_models"""			)
  __A		:  Optional[Any]       				=		torch.load(__SCREAMING_SNAKE_CASE      ,  map_location=lambda __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			: storage			)
  return model







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Dict:





  """simple docstring"""

  __A		:  Optional[Any]       				=		download_model(pretrained_models[args.image_size]			)

  __A		:  Tuple       				=		state_dict["""x_embedder.proj.weight"""]
  __A		:  Dict       				=		state_dict["""x_embedder.proj.bias"""]
  state_dict.pop("""x_embedder.proj.weight"""			)
  state_dict.pop("""x_embedder.proj.bias"""			)

  for depth in range(28			):
    __A		:  Union[str, Any]       				=		state_dict[
        """t_embedder.mlp.0.weight"""
    ]
    __A		:  int       				=		state_dict[
        """t_embedder.mlp.0.bias"""
    ]
    __A		:  List[Any]       				=		state_dict[
        """t_embedder.mlp.2.weight"""
    ]
    __A		:  str       				=		state_dict[
        """t_embedder.mlp.2.bias"""
    ]
    __A		:  Optional[int]       				=		state_dict[
        """y_embedder.embedding_table.weight"""
    ]

    __A		:  Union[str, Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.weight"
    ]
    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.bias"
    ]

    __A				,       __A				,       __A		:  Tuple       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"]      ,  3      ,  dim=0			)
    __A				,       __A				,       __A		:  Union[str, Any]       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"]      ,  3      ,  dim=0			)

    __A		:  Tuple       				=		q
    __A		:  Tuple       				=		q_bias
    __A		:  Tuple       				=		k
    __A		:  int       				=		k_bias
    __A		:  Union[str, Any]       				=		v
    __A		:  Optional[Any]       				=		v_bias

    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.attn.proj.weight"
    ]
    __A		:  str       				=		state_dict[F"blocks.{depth}.attn.proj.bias"]

    __A		:  str       				=		state_dict[F"blocks.{depth}.mlp.fc1.weight"]
    __A		:  Union[str, Any]       				=		state_dict[F"blocks.{depth}.mlp.fc1.bias"]
    __A		:  int       				=		state_dict[F"blocks.{depth}.mlp.fc2.weight"]
    __A		:  str       				=		state_dict[F"blocks.{depth}.mlp.fc2.bias"]

    state_dict.pop(F"blocks.{depth}.attn.qkv.weight"			)
    state_dict.pop(F"blocks.{depth}.attn.qkv.bias"			)
    state_dict.pop(F"blocks.{depth}.attn.proj.weight"			)
    state_dict.pop(F"blocks.{depth}.attn.proj.bias"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc1.weight"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc1.bias"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc2.weight"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc2.bias"			)
    state_dict.pop(F"blocks.{depth}.adaLN_modulation.1.weight"			)
    state_dict.pop(F"blocks.{depth}.adaLN_modulation.1.bias"			)

  state_dict.pop("""t_embedder.mlp.0.weight"""			)
  state_dict.pop("""t_embedder.mlp.0.bias"""			)
  state_dict.pop("""t_embedder.mlp.2.weight"""			)
  state_dict.pop("""t_embedder.mlp.2.bias"""			)
  state_dict.pop("""y_embedder.embedding_table.weight"""			)

  __A		:  List[str]       				=		state_dict["""final_layer.adaLN_modulation.1.weight"""]
  __A		:  Optional[int]       				=		state_dict["""final_layer.adaLN_modulation.1.bias"""]
  __A		:  List[str]       				=		state_dict["""final_layer.linear.weight"""]
  __A		:  Optional[int]       				=		state_dict["""final_layer.linear.bias"""]

  state_dict.pop("""final_layer.linear.weight"""			)
  state_dict.pop("""final_layer.linear.bias"""			)
  state_dict.pop("""final_layer.adaLN_modulation.1.weight"""			)
  state_dict.pop("""final_layer.adaLN_modulation.1.bias"""			)

  # DiT XL/2
  __A		:  Union[str, Any]       				=		TransformeraDModel(
      sample_size=args.image_size // 8      ,  num_layers=28      ,  attention_head_dim=72      ,  in_channels=4      ,  out_channels=8      ,  patch_size=2      ,  attention_bias=__SCREAMING_SNAKE_CASE      ,  num_attention_heads=16      ,  activation_fn="""gelu-approximate"""      ,  num_embeds_ada_norm=1000      ,  norm_type="""ada_norm_zero"""      ,  norm_elementwise_affine=__SCREAMING_SNAKE_CASE      ,  )
  transformer.load_state_dict(__SCREAMING_SNAKE_CASE      ,  strict=__SCREAMING_SNAKE_CASE			)

  __A		:  str       				=		DDIMScheduler(
      num_train_timesteps=1000      ,  beta_schedule="""linear"""      ,  prediction_type="""epsilon"""      ,  clip_sample=__SCREAMING_SNAKE_CASE      ,  )

  __A		:  List[Any]       				=		AutoencoderKL.from_pretrained(args.vae_model			)

  __A		:  Optional[int]       				=		DiTPipeline(transformer=__SCREAMING_SNAKE_CASE      ,  vae=__SCREAMING_SNAKE_CASE      ,  scheduler=__SCREAMING_SNAKE_CASE			)

  if args.save:
    pipeline.save_pretrained(args.checkpoint_path			)


if __name__ == "__main__":
     A__ :		List[str]					=argparse.ArgumentParser()

     parser.add_argument(
         '--image_size',
         default=256,
         type=int,
         required=False,
         help='Image size of pretrained model, either 256 or 512.',
     )
     parser.add_argument(
         '--vae_model',
         default='stabilityai/sd-vae-ft-ema',
         type=str,
         required=False,
         help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
     )
     parser.add_argument(
         '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
     )
     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
     )

     A__ :		Dict					=parser.parse_args()
     main(args)



