import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	Any     )							-> Any:
  SCREAMING_SNAKE_CASE__      							=				s.rsplit(__UpperCamelCase			,					__UpperCamelCase     )
  return new.join(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple     )							-> Optional[int]:
  return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> Dict:
  SCREAMING_SNAKE_CASE__      							=				{}

  SCREAMING_SNAKE_CASE__      							=				["""group_1""", """group_2""", """group_3""", """group_4"""]
  for key, value in state_dict.items():
    for group_key in group_keys:
      if group_key in key:
        SCREAMING_SNAKE_CASE__      							=				key.replace(f"""{group_key}."""			,					f"""{group_key}.group."""     )

    if "res_path" in key:
      SCREAMING_SNAKE_CASE__      							=				key.replace("""res_path."""			,					"""res_path.path."""     )

    if key.endswith(""".w"""     ):
      SCREAMING_SNAKE_CASE__      							=				rreplace(__UpperCamelCase			,					""".w"""			,					""".weight"""			,					1     )
    if key.endswith(""".b"""     ):
      SCREAMING_SNAKE_CASE__      							=				rreplace(__UpperCamelCase			,					""".b"""			,					""".bias"""			,					1     )

    SCREAMING_SNAKE_CASE__      							=				value.float()

  return upgrade



@torch.no_grad()
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	Union[str, Any]=None			,					__UpperCamelCase   :	str=True     )							-> List[str]:
  from dall_e import Encoder

  SCREAMING_SNAKE_CASE__      							=				Encoder()
  if os.path.exists(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				torch.load(__UpperCamelCase     )
  else:
    SCREAMING_SNAKE_CASE__      							=				torch.hub.load_state_dict_from_url(__UpperCamelCase     )

  if isinstance(__UpperCamelCase			,					__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				ckpt.state_dict()
  encoder.load_state_dict(__UpperCamelCase     )

  if config_path is not None:
    SCREAMING_SNAKE_CASE__      							=				FlavaImageCodebookConfig.from_pretrained(__UpperCamelCase     )
  else:
    SCREAMING_SNAKE_CASE__      							=				FlavaImageCodebookConfig()

  SCREAMING_SNAKE_CASE__      							=				FlavaImageCodebook(__UpperCamelCase     ).eval()
  SCREAMING_SNAKE_CASE__      							=				encoder.state_dict()

  SCREAMING_SNAKE_CASE__      							=				upgrade_state_dict(__UpperCamelCase     )
  hf_model.load_state_dict(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				hf_model.state_dict()
  SCREAMING_SNAKE_CASE__      							=				count_parameters(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				count_parameters(__UpperCamelCase     )

  assert torch.allclose(__UpperCamelCase			,					__UpperCamelCase			,					atol=1E-3     )

  if save_checkpoint:
    hf_model.save_pretrained(__UpperCamelCase     )
  else:
    return hf_state_dict


if __name__ == "__main__":
   __lowerCamelCase      :			Optional[Any]							       =		argparse.ArgumentParser()
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   __lowerCamelCase      :			int							       =		parser.parse_args()

   convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCamelCase      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  for shard in shards:
    for i in range(__UpperCamelCase     ):
      yield {"i": i, "shard": shard}



def      __SCREAMING_SNAKE_CASE  ( )							-> Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

  SCREAMING_SNAKE_CASE__      							=				ArgumentParser()
  parser.add_argument("""--streaming"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--local_rank"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--num_workers"""			,					type=__UpperCamelCase			,					default=0     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  SCREAMING_SNAKE_CASE__      							=				args.streaming
  SCREAMING_SNAKE_CASE__      							=				args.num_workers

  SCREAMING_SNAKE_CASE__      							=				{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(__UpperCamelCase     )]}
  SCREAMING_SNAKE_CASE__      							=				IterableDataset.from_generator(__UpperCamelCase			,					gen_kwargs=__UpperCamelCase     )
  if not streaming:
    SCREAMING_SNAKE_CASE__      							=				Dataset.from_list(list(__UpperCamelCase     )     )

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				NUM_SHARDS * NUM_ITEMS_PER_SHARD
  SCREAMING_SNAKE_CASE__      							=				full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)     )

  SCREAMING_SNAKE_CASE__      							=				sum(1 for _ in dataloader     )
  if local_size != expected_local_size:
    raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
   main()
