

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       UpperCamelCase_          =  s.rsplit(UpperCamelCase_					,		UpperCamelCase_  )
       return new.join(UpperCamelCase_  )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, 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  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  {}

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

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

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

              UpperCamelCase_          =  value.float()

       return upgrade




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=True  )     ->		str:
       from dall_e import Encoder

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

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

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

       UpperCamelCase_          =  FlavaImageCodebook(UpperCamelCase_  ).eval()
       UpperCamelCase_          =  encoder.state_dict()

       UpperCamelCase_          =  upgrade_state_dict(UpperCamelCase_  )
       hf_model.load_state_dict(UpperCamelCase_  )
       UpperCamelCase_          =  hf_model.state_dict()
       UpperCamelCase_          =  count_parameters(UpperCamelCase_  )
       UpperCamelCase_          =  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__":
     _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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     _UpperCAmelCase        =				parser.parse_args()

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








from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCAmelCase        =				{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCAmelCase        =				{
    'facebook/blenderbot_small-90M': 5_1_2,
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[Any]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					str         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					List[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					str         =   BlenderbotSmallTokenizer

       def __init__(		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]=None					,   _SCREAMING_SNAKE_CASE: List[str]=None					,   _SCREAMING_SNAKE_CASE: Optional[int]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[Any]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[str]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False					,   _SCREAMING_SNAKE_CASE: int=True					,   **_SCREAMING_SNAKE_CASE: Optional[int]					,   )       -> int:




              """simple docstring"""


              super().__init__(
                  ByteLevelBPETokenizer(
                      vocab=_SCREAMING_SNAKE_CASE					,   merges=_SCREAMING_SNAKE_CASE					,   add_prefix_space=_SCREAMING_SNAKE_CASE					,   trim_offsets=_SCREAMING_SNAKE_CASE					,   )					,   bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  add_prefix_space

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Any=None		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [self.cls_token_id]

              if token_ids_a is None:
                     return len(cls + token_ids_a + sep		) * [0]
              return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]

