
"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig






def 					_snake_case (						snake_case__      :   Union[str, Any]		,					snake_case__      :   Optional[int]		,					snake_case__      :   List[Any]		,					snake_case__      :   Tuple   ):
	A        =      s.rsplit(snake_case__		,					snake_case__   )
	return new.join(snake_case__   )






def 					_snake_case (						snake_case__      :   List[Any]   ):
	# encoder.embeddings are double copied in original FLAVA
	return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()   )






def 					_snake_case (						snake_case__      :   Union[str, Any]   ):
	A        =      {}

	A        =      ['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:
				A        =      key.replace(F'{group_key}.'		,					F'{group_key}.group.'   )

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

		if key.endswith('.w'   ):
			A        =      rreplace(snake_case__		,					'.w'		,					'.weight'		,					1   )
		if key.endswith('.b'   ):
			A        =      rreplace(snake_case__		,					'.b'		,					'.bias'		,					1   )

		A        =      value.float()

	return upgrade






@torch.no_grad()
def 					_snake_case (						snake_case__      :   List[Any]		,					snake_case__      :   int		,					snake_case__      :   Tuple=None		,					snake_case__      :   str=True   ):
	from dall_e import Encoder

	A        =      Encoder()
	if os.path.exists(snake_case__   ):
		A        =      torch.load(snake_case__   )
	else:
		A        =      torch.hub.load_state_dict_from_url(snake_case__   )

	if isinstance(snake_case__		,					snake_case__   ):
		A        =      ckpt.state_dict()
	encoder.load_state_dict(snake_case__   )

	if config_path is not None:
		A        =      FlavaImageCodebookConfig.from_pretrained(snake_case__   )
	else:
		A        =      FlavaImageCodebookConfig()

	A        =      FlavaImageCodebook(snake_case__   ).eval()
	A        =      encoder.state_dict()

	A        =      upgrade_state_dict(snake_case__   )
	hf_model.load_state_dict(snake_case__   )
	A        =      hf_model.state_dict()
	A        =      count_parameters(snake_case__   )
	A        =      count_parameters(snake_case__   )

	assert torch.allclose(snake_case__		,					snake_case__		,					atol=1e-3   )

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


if __name__ == "__main__":
		_lowercase							      = 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''')
		_lowercase							      = parser.parse_args()

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




'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    int    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    Dict    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    List[str]    						=      0.01
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  snake_case__  :    str    						=      time.time()
                  locka.acquire(_lowerCAmelCase		)
                  assert time.time() - _start > timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Dict    						=      """a""" * 1_000 + """.lock"""
      snake_case__  :    int    						=      FileLock(str(tmpdir / filename		)		)
      assert locka._lock_file.endswith(""".lock"""		)
      assert not locka._lock_file.endswith(_lowerCAmelCase		)
      assert len(os.path.basename(locka._lock_file		)		) <= 255
      snake_case__  :    Dict    						=      FileLock(tmpdir / filename		)
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  locka.acquire(0		)
