




'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint




def   lowercase__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






      return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()    )




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						{}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('heads.cmd.mim_head.cls.predictions'      ,'mmm_image_head'    )
            SCREAMING_SNAKE_CASE   :      int    					=						key.replace('heads.cmd.mlm_head.cls.predictions'      ,'mmm_text_head'    )
            SCREAMING_SNAKE_CASE   :      str    					=						key.replace('heads.cmd.itm_head.cls'      ,'itm_head'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('heads.cmd.itm_head.pooler'      ,'itm_head.pooler'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('heads.cmd.clip_head.logit_scale'      ,'flava.logit_scale'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('heads.fairseq_mlm.cls.predictions'      ,'mlm_head'    )
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						key.replace('heads.imagenet.mim_head.cls.predictions'      ,'mim_head'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('mm_text_projection'      ,'flava.text_to_mm_projection'    )
            SCREAMING_SNAKE_CASE   :      List[str]    					=						key.replace('mm_image_projection'      ,'flava.image_to_mm_projection'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						key.replace('image_encoder.module'      ,'flava.image_model'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('text_encoder.module'      ,'flava.text_model'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('mm_encoder.module.encoder.cls_token'      ,'flava.multimodal_model.cls_token'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('mm_encoder.module'      ,'flava.multimodal_model'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						key.replace('text_projection'      ,'flava.text_projection'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('image_projection'      ,'flava.image_projection'    )

            SCREAMING_SNAKE_CASE   :      Dict    					=						value.float()

      for key, value in codebook_state_dict.items():
            SCREAMING_SNAKE_CASE   :      int    					=						value

      return upgrade




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[int]=None    ):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      int    					=						FlavaConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						FlavaConfig()

      SCREAMING_SNAKE_CASE   :      str    					=						FlavaForPreTraining(__UpperCamelCase    ).eval()

      SCREAMING_SNAKE_CASE   :      int    					=						convert_dalle_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,save_checkpoint=__UpperCamelCase    )

      if os.path.exists(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      int    					=						torch.load(__UpperCamelCase      ,map_location='cpu'    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.hub.load_state_dict_from_url(__UpperCamelCase      ,map_location='cpu'    )

      SCREAMING_SNAKE_CASE   :      int    					=						upgrade_state_dict(__UpperCamelCase      ,__UpperCamelCase    )
      hf_model.load_state_dict(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						hf_model.state_dict()
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						count_parameters(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      int    					=						count_parameters(__UpperCamelCase    ) + count_parameters(__UpperCamelCase    )

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

      hf_model.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
      UpperCamelCase_    	=				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("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      UpperCamelCase_    	=				parser.parse_args()

      convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)





'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


UpperCAmelCase					:			Tuple       =	HfArgumentParser(PretokenizationArguments)
UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase					:			List[Any]       =	multiprocessing.cpu_count()
UpperCAmelCase					:			str       =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase					:			Optional[int]       =	time.time()
UpperCAmelCase					:			Any       =	load_dataset(args.dataset_name, split='train')
print(f"""Dataset loaded in {time.time()-t_start:.2f}s""")

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[str]       =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(f"""Dataset tokenized in {time.time()-t_start:.2f}s""")

UpperCAmelCase					:			List[Any]       =	time.time()
ds.push_to_hub(args.tokenized_data_repo)



print(f"""Data pushed to the hub in {time.time()-t_start:.2f}s""")






