



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig





def 		a__      (		_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			List[Any]				):
   __lowerCamelCase    				=     s.rsplit(_UpperCamelCase			,_UpperCamelCase				)
   return new.join(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			Optional[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 		a__      (		_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     {}

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

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

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

      __lowerCamelCase    				=     value.float()

   return upgrade





@torch.no_grad()
def 		a__      (		_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			int			,_UpperCamelCase      :			Tuple=None			,_UpperCamelCase      :			str=True				):
   from dall_e import Encoder

   __lowerCamelCase    				=     Encoder()
   if os.path.exists(_UpperCamelCase				):
      __lowerCamelCase    				=     torch.load(_UpperCamelCase				)
   else:
      __lowerCamelCase    				=     torch.hub.load_state_dict_from_url(_UpperCamelCase				)

   if isinstance(_UpperCamelCase			,_UpperCamelCase				):
      __lowerCamelCase    				=     ckpt.state_dict()
   encoder.load_state_dict(_UpperCamelCase				)

   if config_path is not None:
      __lowerCamelCase    				=     FlavaImageCodebookConfig.from_pretrained(_UpperCamelCase				)
   else:
      __lowerCamelCase    				=     FlavaImageCodebookConfig()

   __lowerCamelCase    				=     FlavaImageCodebook(_UpperCamelCase				).eval()
   __lowerCamelCase    				=     encoder.state_dict()

   __lowerCamelCase    				=     upgrade_state_dict(_UpperCamelCase				)
   hf_model.load_state_dict(_UpperCamelCase				)
   __lowerCamelCase    				=     hf_model.state_dict()
   __lowerCamelCase    				=     count_parameters(_UpperCamelCase				)
   __lowerCamelCase    				=     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__":
  a_								=       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""")
  a_								=       parser.parse_args()

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











from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(
        features=__UpperCAmelCase						,					cache_dir=__UpperCAmelCase						,					keep_in_memory=__UpperCAmelCase						,					streaming=__UpperCAmelCase						,					num_proc=__UpperCAmelCase						,					**__UpperCAmelCase						,					)
    __lowerCamelCase    				=     Generator(
        cache_dir=__UpperCAmelCase						,					features=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					gen_kwargs=__UpperCAmelCase						,					**__UpperCAmelCase						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Build iterable dataset
    if self.streaming:
       __lowerCamelCase    				=     self.builder.as_streaming_dataset(split='''train'''					)
    # Build regular (map-style) dataset
    else:
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase						,					download_mode=__UpperCAmelCase						,					verification_mode=__UpperCAmelCase						,					base_path=__UpperCAmelCase						,					num_proc=self.num_proc						,					)
       __lowerCamelCase    				=     self.builder.as_dataset(
           split='''train'''						,					verification_mode=__UpperCAmelCase						,					in_memory=self.keep_in_memory					)
    return dataset






