
'''simple docstring'''
import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


A__ :		int					=logging.get_logger(__name__)  # pylint: disable=invalid-name


A__ :		List[str]					='lfs-multipart-upload'







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 @staticmethod
 def 		lowercase_( lowerCamelCase      :     ArgumentParser							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		parser.add_parser(
       """lfs-enable-largefiles"""					, help=(
           """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
       )					, )
   enable_parser.add_argument("""path"""					, type=lowerCamelCase					, help="""Local path to repository you want to configure."""							)
   enable_parser.set_defaults(func=lambda lowerCamelCase							: LfsEnableCommand(lowerCamelCase							)							)

   __A		:  Dict       				=		parser.add_parser(
       lowerCamelCase					, help=(
           """Deprecated: use `huggingface-cli` instead. """
           """Command will get called by git-lfs, do not call it directly."""
       )					, )
   upload_parser.set_defaults(func=lambda lowerCamelCase							: LfsUploadCommand(lowerCamelCase							)							)







class 			__A					:
 def __init__( self      :     List[str]					, lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		args






 def 		lowercase_( self      :     int							):



   """simple docstring"""





   warnings.warn(
       """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""							)
   __A		:  Any       				=		os.path.abspath(self.args.path							)
   if not os.path.isdir(lowerCamelCase							):
     print("""This does not look like a valid git repo."""							)
     exit(1							)
   subprocess.run(
       """git config lfs.customtransfer.multipart.path transformers-cli""".split()					, check=lowerCamelCase					, cwd=lowerCamelCase							)
   subprocess.run(
       f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split()					, check=lowerCamelCase					, cwd=lowerCamelCase					, )
   print("""Local repo set up for largefiles"""							)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict			)					->							int:





  """simple docstring"""

  __A		:  Optional[Any]       				=		json.dumps(__SCREAMING_SNAKE_CASE			) + """\n"""
  sys.stdout.write(__SCREAMING_SNAKE_CASE			)
  sys.stdout.flush()







def 							A_  (    )					->							Optional[Dict]:





  """simple docstring"""

  __A		:  Optional[int]       				=		json.loads(sys.stdin.readline().strip()			)

  if "terminate" in (msg.get("""type"""			), msg.get("""event"""			)):
    # terminate message received
    return None

  if msg.get("""event"""			) not in ("download", "upload"):
    logger.critical("""Received unexpected message"""			)
    sys.exit(1			)

  return msg







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Dict					, lowerCamelCase      :     str					, lowerCamelCase      :     int					, lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  int       				=		filepath
   __A		:  List[Any]       				=		seek_from
   __A		:  int       				=		read_limit
   __A		:  Optional[int]       				=		0
 def __enter__( self      :     List[Any]							):



   """simple docstring"""





   __A		:  Tuple       				=		open(self.filepath					, """rb"""							)
   self.f.seek(self.seek_from							)
   return self
 def __len__( self      :     Dict							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		os.fstat(self.f.fileno()							).st_size
   return min(self.read_limit					, total_length - self.seek_from							)
 def 		lowercase_( self      :     Optional[int]					, lowerCamelCase      :     Dict=-1							):



   """simple docstring"""





   if self.n_seen >= self.read_limit:
     return b""
   __A		:  Optional[int]       				=		self.read_limit - self.n_seen
   __A		:  str       				=		self.f.read(remaining_amount if n < 0 else min(lowerCamelCase					, lowerCamelCase							)							)
   self.n_seen += len(lowerCamelCase							)
   return data
 def __iter__( self      :     Dict							):



   """simple docstring"""





   yield self.read(n=4 * 10_24 * 10_24							)






 def __exit__( self      :     Any					, *lowerCamelCase      :     Optional[Any]							):



   """simple docstring"""





   self.f.close()







class 			__A					:
 def __init__( self      :     Union[str, Any]					, lowerCamelCase      :     List[str]							):



   """simple docstring"""





   __A		:  int       				=		args






 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  str       				=		json.loads(sys.stdin.readline().strip()							)
   if not (init_msg.get("""event"""							) == "init" and init_msg.get("""operation"""							) == "upload"):
     write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}							)
     sys.exit(1							)

   # The transfer process should use the information it needs from the
   # initiation structure, and also perform any one-off setup tasks it
   # needs to do. It should then respond on stdout with a simple empty
   # confirmation structure, as follows:
   write_msg({}							)

   # After the initiation exchange, git-lfs will send any number of
   # transfer requests to the stdin of the transfer process, in a serial sequence.
   while True:
     __A		:  Optional[Any]       				=		read_msg()
     if msg is None:
       # When all transfers have been processed, git-lfs will send
       # a terminate event to the stdin of the transfer process.
       # On receiving this message the transfer process should
       # clean up and terminate. No response is expected.
       sys.exit(0							)

     __A		:  List[Any]       				=		msg["""oid"""]
     __A		:  List[Any]       				=		msg["""path"""]
     __A		:  int       				=		msg["""action"""]["""href"""]
     __A		:  Union[str, Any]       				=		msg["""action"""]["""header"""]
     __A		:  List[Any]       				=		int(header.pop("""chunk_size"""							)							)
     __A		:  List[str]       				=		list(header.values()							)

     __A		:  Any       				=		[]
     for i, presigned_url in enumerate(lowerCamelCase							):
       with FileSlice(lowerCamelCase					, seek_from=i * chunk_size					, read_limit=lowerCamelCase							) as data:
         __A		:  List[str]       				=		requests.put(lowerCamelCase					, data=lowerCamelCase							)
         r.raise_for_status()
         parts.append(
             {
                 """etag""": r.headers.get("""etag"""							),
                 """partNumber""": i + 1,
             }							)
         # In order to support progress reporting while data is uploading / downloading,
         # the transfer process should post messages to stdout
         write_msg(
             {
                 """event""": """progress""",
                 """oid""": oid,
                 """bytesSoFar""": (i + 1) * chunk_size,
                 """bytesSinceLast""": chunk_size,
             }							)
         # Not precise but that's ok.

     __A		:  Tuple       				=		requests.post(
         lowerCamelCase					, json={
             """oid""": oid,
             """parts""": parts,
         }					, )
     r.raise_for_status()

     write_msg({"""event""": """complete""", """oid""": oid}							)





'''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)



