
'''simple docstring'''
import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
A__ :		List[str]					=logging.get_logger(__name__)

A__ :		Dict					=[
    (
        'protein1',
        'MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA',
    ),
    ('protein2', 'MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'),
    ('protein3', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'),
    ('protein4', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'),
]

A__ :		str					={
    'esm1b_t33_650M_UR50S': esm_module.pretrained.esm1b_t33_650M_UR50S,
    'esm1v_t33_650M_UR90S_1': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    'esm1v_t33_650M_UR90S_2': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    'esm1v_t33_650M_UR90S_3': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    'esm1v_t33_650M_UR90S_4': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    'esm1v_t33_650M_UR90S_5': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    'esm2_t48_15B_UR50D': esm_module.pretrained.esm2_t48_15B_UR50D,
    'esm2_t36_3B_UR50D': esm_module.pretrained.esm2_t36_3B_UR50D,
    'esm2_t33_650M_UR50D': esm_module.pretrained.esm2_t33_650M_UR50D,
    'esm2_t30_150M_UR50D': esm_module.pretrained.esm2_t30_150M_UR50D,
    'esm2_t12_35M_UR50D': esm_module.pretrained.esm2_t12_35M_UR50D,
    'esm2_t6_8M_UR50D': esm_module.pretrained.esm2_t6_8M_UR50D,
    'esmfold_v1': esmfold_va,
}

A__ :		List[Any]					=list('ARNDCQEGHILKMFPSTWYV')

A__ :		int					=restypes + ['X']
A__ :		List[str]					=restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']







def 							A_  (    )					->							List[Any]:





  """simple docstring"""

  with TemporaryDirectory() as tempdir:
    __A		:  int       				=		"""\n""".join(__SCREAMING_SNAKE_CASE			)
    __A		:  Dict       				=		Path(__SCREAMING_SNAKE_CASE			) / """vocab.txt"""
    vocab_file.write_text(__SCREAMING_SNAKE_CASE			)
    __A		:  Dict       				=		EsmTokenizer(vocab_file=str(__SCREAMING_SNAKE_CASE			)			)
  __A		:  Optional[Any]       				=		0  # Overlaps with 'A' but that seems to be what they want
  return hf_tokenizer







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	Dict			)					->							Tuple:





  """simple docstring"""

  __A		:  int       				=		our_module.load_state_dict(original_module.state_dict()			)
  if status.missing_keys:
    raise ValueError(F"Missing keys: {status.missing_keys}"			)
  if status.unexpected_keys:
    raise ValueError(F"Unexpected keys: {status.unexpected_keys}"			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	bool      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Tuple:





  """simple docstring"""

  if model.startswith("""esmfold"""			):
    __A		:  Optional[int]       				=		MODEL_MAPPING[model]()
  else:
    __A				,       __A		:  Any       				=		MODEL_MAPPING[model]()
  esm.eval()  # disable dropout

  if model.startswith("""esmfold"""			):
    __A		:  Optional[int]       				=		esm.esm.embed_dim
    __A		:  Dict       				=		esm.esm.num_layers
    __A		:  Any       				=		esm.esm.attention_heads
    __A		:  Union[str, Any]       				=		4 * embed_dim
    __A		:  Optional[int]       				=		esm.esm.token_dropout
    __A		:  Optional[Any]       				=		False  # This code path does not exist in ESM-2
    __A		:  Union[str, Any]       				=		"""rotary"""
    __A		:  str       				=		True
    __A		:  List[str]       				=		EsmFoldConfig()
    for key, val in esm.cfg.items():
      if hasattr(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			) and key != "trunk":
        setattr(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
    for key, val in esm.cfg.trunk.items():
      if hasattr(esmfold_config.trunk      ,  __SCREAMING_SNAKE_CASE			) and key != "structure_module":
        setattr(esmfold_config.trunk      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
    for key, val in esm.cfg.trunk.structure_module.items():
      if hasattr(esmfold_config.trunk.structure_module      ,  __SCREAMING_SNAKE_CASE			):
        setattr(esmfold_config.trunk.structure_module      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
  elif hasattr(__SCREAMING_SNAKE_CASE      ,  """args"""			):
    # Indicates an ESM-1b or ESM-1v model
    __A		:  str       				=		esm.args.embed_dim
    __A		:  Any       				=		esm.args.layers
    __A		:  Optional[Any]       				=		esm.args.attention_heads
    __A		:  Any       				=		esm.args.ffn_embed_dim
    __A		:  List[str]       				=		esm.args.token_dropout
    __A		:  List[str]       				=		True if esm.emb_layer_norm_before else False
    __A		:  Tuple       				=		"""absolute"""
    __A		:  Optional[int]       				=		False
    __A		:  Optional[Any]       				=		None
  else:
    # Indicates an ESM-2 model
    __A		:  Any       				=		esm.embed_dim
    __A		:  int       				=		esm.num_layers
    __A		:  Tuple       				=		esm.attention_heads
    __A		:  Dict       				=		4 * embed_dim  # This is hardcoded in ESM-2
    __A		:  List[str]       				=		esm.token_dropout
    __A		:  Optional[Any]       				=		False  # This code path does not exist in ESM-2
    __A		:  int       				=		"""rotary"""
    __A		:  Any       				=		False
    __A		:  str       				=		None

  if is_folding_model:
    __A		:  Optional[Any]       				=		esm.esm.alphabet
  __A		:  Optional[Any]       				=		tuple(alphabet.all_toks			)
  __A		:  Tuple       				=		alphabet.mask_idx
  __A		:  List[str]       				=		alphabet.padding_idx

  if is_folding_model:
    __A		:  List[str]       				=		esm.esm
  else:
    __A		:  List[str]       				=		esm

  __A		:  Optional[Any]       				=		EsmConfig(
      vocab_size=original_esm_model.embed_tokens.num_embeddings      ,  mask_token_id=__SCREAMING_SNAKE_CASE      ,  hidden_size=__SCREAMING_SNAKE_CASE      ,  num_hidden_layers=__SCREAMING_SNAKE_CASE      ,  num_attention_heads=__SCREAMING_SNAKE_CASE      ,  intermediate_size=__SCREAMING_SNAKE_CASE      ,  max_position_embeddings=1026      ,  layer_norm_eps=1e-5      ,  attention_probs_dropout_prob=0.0      ,  hidden_dropout_prob=0.0      ,  pad_token_id=__SCREAMING_SNAKE_CASE      ,  emb_layer_norm_before=__SCREAMING_SNAKE_CASE      ,  token_dropout=__SCREAMING_SNAKE_CASE      ,  position_embedding_type=__SCREAMING_SNAKE_CASE      ,  is_folding_model=__SCREAMING_SNAKE_CASE      ,  esmfold_config=__SCREAMING_SNAKE_CASE      ,  vocab_list=__SCREAMING_SNAKE_CASE      ,  )
  if classification_head:
    __A		:  List[str]       				=		esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
  print("""Our ESM config:"""      ,  __SCREAMING_SNAKE_CASE			)

  if model.startswith("""esmfold"""			):
    __A		:  int       				=		EsmForProteinFolding
  elif classification_head:
    __A		:  Optional[Any]       				=		EsmForSequenceClassification
  else:
    __A		:  Dict       				=		EsmForMaskedLM
  __A		:  List[Any]       				=		model_class(__SCREAMING_SNAKE_CASE			)
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  __A		:  Optional[Any]       				=		original_esm_model.embed_tokens.weight
  if position_embedding_type == "absolute":
    __A		:  int       				=		original_esm_model.embed_positions.weight

  if config.emb_layer_norm_before:
    __A		:  List[Any]       				=		original_esm_model.emb_layer_norm_before.weight
    __A		:  List[Any]       				=		original_esm_model.emb_layer_norm_before.bias

  __A		:  int       				=		original_esm_model.emb_layer_norm_after.weight
  __A		:  Dict       				=		original_esm_model.emb_layer_norm_after.bias

  for i in range(config.num_hidden_layers			):
    # Encoder: start of layer
    __A		:  EsmLayer       				=		model.esm.encoder.layer[i]
    # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
    __A		:  List[str]       				=		original_esm_model.layers[i]

    # self attention
    __A		:  EsmSelfAttention       				=		layer.attention.self
    assert (
        esm_layer.self_attn.k_proj.weight.data.shape
        == esm_layer.self_attn.q_proj.weight.data.shape
        == esm_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)			)
    )

    __A		:  Dict       				=		esm_layer.self_attn.q_proj.weight
    __A		:  Optional[int]       				=		esm_layer.self_attn.q_proj.bias
    __A		:  int       				=		esm_layer.self_attn.k_proj.weight
    __A		:  int       				=		esm_layer.self_attn.k_proj.bias
    __A		:  str       				=		esm_layer.self_attn.v_proj.weight
    __A		:  Union[str, Any]       				=		esm_layer.self_attn.v_proj.bias

    if getattr(esm_layer.self_attn      ,  """rot_emb"""      ,  __SCREAMING_SNAKE_CASE			) is not None:
      # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
      # During the training of ESM-2 the model was converted to float16 precision, which also converts
      # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
      # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
      # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
      # we make sure the new model copies the data from the old inv_freq.
      __A		:  Optional[Any]       				=		esm_layer.self_attn.rot_emb.inv_freq

    # LayerNorm changes for pre-activation
    __A		:  List[Any]       				=		esm_layer.self_attn_layer_norm.weight
    __A		:  Tuple       				=		esm_layer.self_attn_layer_norm.bias
    __A		:  List[str]       				=		esm_layer.final_layer_norm.weight
    __A		:  List[Any]       				=		esm_layer.final_layer_norm.bias

    # self-attention output
    __A		:  EsmSelfOutput       				=		layer.attention.output
    assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
    __A		:  Optional[int]       				=		esm_layer.self_attn.out_proj.weight
    __A		:  int       				=		esm_layer.self_attn.out_proj.bias

    # intermediate
    __A		:  EsmIntermediate       				=		layer.intermediate
    assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
    __A		:  List[Any]       				=		esm_layer.fca.weight
    __A		:  Optional[Any]       				=		esm_layer.fca.bias

    # output
    __A		:  EsmOutput       				=		layer.output
    assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
    __A		:  str       				=		esm_layer.fca.weight
    __A		:  Dict       				=		esm_layer.fca.bias
    # end of layer

  if is_folding_model:
    __A		:  Optional[int]       				=		esm.esm_s_combine.data
    __A		:  Optional[int]       				=		esm.afa_to_esm.data
    transfer_and_check_weights(esm.embedding      ,  model.embedding			)
    transfer_and_check_weights(esm.esm_s_mlp      ,  model.esm_s_mlp			)
    transfer_and_check_weights(esm.trunk      ,  model.trunk			)
    transfer_and_check_weights(esm.distogram_head      ,  model.distogram_head			)
    transfer_and_check_weights(esm.ptm_head      ,  model.ptm_head			)
    transfer_and_check_weights(esm.lm_head      ,  model.lm_head			)
    transfer_and_check_weights(esm.lddt_head      ,  model.lddt_head			)

  elif classification_head:
    __A		:  Tuple       				=		esm.esm.classification_heads["""mnli"""].dense.weight
    __A		:  Tuple       				=		esm.classification_heads["""mnli"""].dense.bias
    __A		:  List[Any]       				=		esm.classification_heads["""mnli"""].out_proj.weight
    __A		:  Dict       				=		esm.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    __A		:  Tuple       				=		esm.lm_head.dense.weight
    __A		:  Dict       				=		esm.lm_head.dense.bias
    __A		:  Optional[int]       				=		esm.lm_head.layer_norm.weight
    __A		:  List[Any]       				=		esm.lm_head.layer_norm.bias
    __A		:  Dict       				=		esm.lm_head.weight
    __A		:  Optional[Any]       				=		esm.lm_head.bias

  # Contact prediction head
  transfer_and_check_weights(esm.contact_head      ,  model.esm.contact_head			)

  # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
  if is_folding_model:
    # Folding models aren't trained on masked inputs and don't like mask tokens.
    __A		:  Tuple       				=		SAMPLE_DATA[:2]
  else:
    __A		:  Union[str, Any]       				=		SAMPLE_DATA

  if is_folding_model:
    __A		:  Dict       				=		get_esmfold_tokenizer()
    __A		:  List[str]       				=		hf_tokenizer(
        [row[1] for row in sample_data]      ,  return_tensors="""pt"""      ,  padding=__SCREAMING_SNAKE_CASE      ,  add_special_tokens=__SCREAMING_SNAKE_CASE			)
    __A				,       __A				,       __A				,       __A				,       __A		:  Optional[Any]       				=		esmfold_encode_sequences([row[1] for row in sample_data]			)
    __A		:  str       				=		torch.all(hf_tokens["""input_ids"""] == esmfold_aas			) and torch.all(
        hf_tokens["""attention_mask"""] == esmfold_mask			)
  else:
    # Let's check that we get the same results.
    __A		:  Dict       				=		alphabet.get_batch_converter()
    __A				,       __A				,       __A		:  Tuple       				=		batch_converter(__SCREAMING_SNAKE_CASE			)
    # Prepare tokenizer and make sure it matches
    with TemporaryDirectory() as tempdir:
      __A		:  Union[str, Any]       				=		"""\n""".join(alphabet.all_toks			)
      __A		:  Any       				=		Path(__SCREAMING_SNAKE_CASE			) / """vocab.txt"""
      vocab_file.write_text(__SCREAMING_SNAKE_CASE			)
      __A		:  Optional[int]       				=		EsmTokenizer(vocab_file=str(__SCREAMING_SNAKE_CASE			)			)

    __A		:  Optional[Any]       				=		hf_tokenizer([row[1] for row in sample_data]      ,  return_tensors="""pt"""      ,  padding=__SCREAMING_SNAKE_CASE			)
    __A		:  str       				=		torch.all(hf_tokens["""input_ids"""] == batch_tokens			)

  print("""Do both models tokenizers output the same tokens?"""      ,  """🔥""" if success else """💩"""			)
  if not success:
    raise Exception("""Tokenization does not match!"""			)

  with torch.no_grad():
    if is_folding_model:
      # Let's test the model in parts
      # ESMFold always converts the ESM stem to float16, which requires float16 ops
      # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
      # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
      # original and the converted model on the GPU at the same time.
      __A		:  Tuple       				=		esm.cuda().infer([row[1] for row in sample_data]			)
      __A		:  Tuple       				=		model.cuda()(
          input_ids=hf_tokens["""input_ids"""].cuda()      ,  attention_mask=hf_tokens["""attention_mask"""].cuda()			)
    else:
      __A		:  str       				=		model(**__SCREAMING_SNAKE_CASE      ,  output_hidden_states=__SCREAMING_SNAKE_CASE			)
      __A		:  Optional[Any]       				=		our_output["""logits"""]
      if classification_head:
        __A		:  Union[str, Any]       				=		esm.model.classification_heads["""mnli"""](esm.extract_features(__SCREAMING_SNAKE_CASE			)			)
      else:
        __A		:  Optional[Any]       				=		esm(hf_tokens["""input_ids"""]      ,  repr_layers=list(range(999			)			)			)
        __A		:  Optional[Any]       				=		their_output["""logits"""]

    if is_folding_model:
      __A		:  str       				=		torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]			)			).item()
      __A		:  int       				=		torch.allclose(our_output["""positions"""]      ,  their_output["""positions"""]      ,  atol=1e-5			)
    else:
      __A		:  List[Any]       				=		torch.max(torch.abs(our_output - their_output			)			).item()
      __A		:  Optional[int]       				=		torch.allclose(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  atol=1e-5			)

    print(F"max_absolute_diff = {max_absolute_diff}"			)  # ~ 1e-5
    print("""Do both models output the same tensors?"""      ,  """🔥""" if success else """💩"""			)

    if not success:
      raise Exception("""Something went wRoNg"""			)

    if not is_folding_model:
      # Let's check contact prediction too
      __A		:  List[str]       				=		model.predict_contacts(hf_tokens["""input_ids"""]      ,  hf_tokens["""attention_mask"""]			)
      __A		:  List[Any]       				=		esm.predict_contacts(hf_tokens["""input_ids"""]			)
      __A		:  str       				=		torch.max(torch.abs(our_output - their_output			)			).item()
      __A		:  List[Any]       				=		torch.allclose(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  atol=1e-5			)

      print("""Contact prediction testing:"""			)
      print(F"max_absolute_diff = {max_absolute_diff}"			)  # ~ 1e-5
      print("""Do both models output the same tensors?"""      ,  """🔥""" if success else """💩"""			)

      if not success:
        raise Exception("""Something went wRoNg"""			)

    pathlib.Path(__SCREAMING_SNAKE_CASE			).mkdir(parents=__SCREAMING_SNAKE_CASE      ,  exist_ok=__SCREAMING_SNAKE_CASE			)
    print(F"Saving model to {pytorch_dump_folder_path}"			)
    model.save_pretrained(__SCREAMING_SNAKE_CASE			)

    del esm  # Free up some memory before continuing

  print(F"Saving tokenizer to {pytorch_dump_folder_path}"			)
  hf_tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE			)

  if push_to_repo:
    model.push_to_hub(repo_id=__SCREAMING_SNAKE_CASE      ,  use_auth_token=__SCREAMING_SNAKE_CASE			)
    hf_tokenizer.push_to_hub(repo_id=__SCREAMING_SNAKE_CASE      ,  use_auth_token=__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
     A__ :		Any					=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_dump_folder_path', type=str, required=True, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--classification_head', action='store_true', help='Whether to convert a final classification head.'
     )
     parser.add_argument('--model', default=None, type=str, required=True, help='Name of model to convert.')
     parser.add_argument('--push_to_repo', type=str, help='Repo to upload to (including username!).')
     parser.add_argument('--auth_token', type=str, help='HuggingFace auth token.')
     A__ :		Tuple					=parser.parse_args()
     convert_esm_checkpoint_to_pytorch(
         args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
     )





'''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)



