



'''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


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


_UpperCamelCase  						=			"""lfs-multipart-upload"""

class      lowerCamelCase__     (     _a				):






	'''simple docstring'''

	@staticmethod
	def      lowercase__							(  __A		:  ArgumentParser					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   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=__A   ,		help="""Local path to repository you want to configure."""					)
								enable_parser.set_defaults(func=lambda __A					: LfsEnableCommand(__A					)					)

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





class      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  List[Any]   ,		__A		:  List[Any]					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   args




	def      lowercase__							(  self		:  List[str]					)     ->			Optional[Any]:




								'''simple docstring'''





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





def 			_lowerCAmelCase(      UpperCAmelCase_      :	Dict						) ->							str:
							lowerCAmelCase__  =   json.dumps(__lowercase						) + """\n"""
							sys.stdout.write(__lowercase						)
							sys.stdout.flush()
def 			_lowerCAmelCase(      ) ->							List[Any]:
							lowerCAmelCase__  =   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      lowerCamelCase__     (     _a				):






	'''simple docstring'''

	def __init__(  self		:  List[Any]   ,		__A		:  str   ,		__A		:  int   ,		__A		:  int					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   filepath
								lowerCAmelCase__  =   seek_from
								lowerCAmelCase__  =   read_limit
								lowerCAmelCase__  =   0




	def __enter__(  self		:  Optional[int]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   open(self.filepath   ,		"""rb"""					)
								self.f.seek(self.seek_from					)
								return self




	def __len__(  self		:  Any					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   os.fstat(self.f.fileno()					).st_size
								return min(self.read_limit   ,		total_length - self.seek_from					)




	def      lowercase__							(  self		:  Dict   ,		__A		:  List[str]=-1					)     ->			str:




								'''simple docstring'''





								if self.n_seen >= self.read_limit:
															return b""
								lowerCAmelCase__  =   self.read_limit - self.n_seen
								lowerCAmelCase__  =   self.f.read(remaining_amount if n < 0 else min(__A   ,		__A					)					)
								self.n_seen += len(__A					)
								return data




	def __iter__(  self		:  Union[str, Any]					)     ->			Dict:




								'''simple docstring'''





								yield self.read(n=4 * 1024 * 1024					)




	def __exit__(  self		:  Optional[int]   ,		*__A		:  Union[str, Any]					)     ->			Tuple:




								'''simple docstring'''





								self.f.close()





class      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  Optional[Any]   ,		__A		:  Optional[int]					)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   args




	def      lowercase__							(  self		:  List[str]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   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:
															lowerCAmelCase__  =   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					)

															lowerCAmelCase__  =   msg["""oid"""]
															lowerCAmelCase__  =   msg["""path"""]
															lowerCAmelCase__  =   msg["""action"""]["""href"""]
															lowerCAmelCase__  =   msg["""action"""]["""header"""]
															lowerCAmelCase__  =   int(header.pop("""chunk_size"""					)					)
															lowerCAmelCase__  =   list(header.values()					)

															lowerCAmelCase__  =   []
															for i, presigned_url in enumerate(__A					):
																						with FileSlice(__A   ,		seek_from=i * chunk_size   ,		read_limit=__A					) as data:
																													lowerCAmelCase__  =   requests.put(__A   ,		data=__A					)
																													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.

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

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












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowercase				    =      {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					F'''pretrained_models/{model_name}'''
  if not os.path.isfile(__lowercase):
    os.makedirs('''pretrained_models'''  ,				exist_ok=__lowercase)
    __lowerCAmelCase								=					F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
    download_url(__lowercase  ,				'''pretrained_models''')
  __lowerCAmelCase								=					torch.load(__lowercase  ,				map_location=lambda __lowercase  ,				__lowercase: storage)
  return model


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

  __lowerCAmelCase								=					state_dict['''x_embedder.proj.weight''']
  __lowerCAmelCase								=					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):
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''y_embedder.embedding_table.weight'''
    ]

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.bias'''
    ]

    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']  ,				3  ,				dim=0)
    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']  ,				3  ,				dim=0)

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.attn.proj.weight'''
    ]
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.attn.proj.bias''']

    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
    __lowerCAmelCase								=					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''')

  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					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
  __lowerCAmelCase								=					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=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

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


if __name__ == "__main__":
      __lowercase				    =      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."""
      )

      __lowercase				    =      parser.parse_args()
      main(args)
