import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCAmelCase_ : int						  =      logging.get_logger(__name__)


lowerCAmelCase_ : List[Any]						  =      {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase_ : Optional[int]						  =      {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase_ : Dict						  =      {
    """facebook/bart-base""": 1_024,
    """facebook/bart-large""": 1_024,
    """facebook/bart-large-mnli""": 1_024,
    """facebook/bart-large-cnn""": 1_024,
    """facebook/bart-large-xsum""": 1_024,
    """yjernite/bart_eli5""": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=["input_ids", "attention_mask"]
   lowerCamelCase_    					=BartTokenizer


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	int=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Optional[Any]="replace"   ,		__lowerCAmelCase				:	Any="<s>"   ,		__lowerCAmelCase				:	int="</s>"   ,		__lowerCAmelCase				:	Optional[Any]="</s>"   ,		__lowerCAmelCase				:	int="<s>"   ,		__lowerCAmelCase				:	Optional[int]="<unk>"   ,		__lowerCAmelCase				:	Union[str, Any]="<pad>"   ,		__lowerCAmelCase				:	Optional[int]="<mask>"   ,		__lowerCAmelCase				:	Dict=False   ,		__lowerCAmelCase				:	Dict=True   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					Any:
         super().__init__(
             UpperCamelCase__   ,		UpperCamelCase__   ,		tokenizer_file=UpperCamelCase__   ,		errors=UpperCamelCase__   ,		bos_token=UpperCamelCase__   ,		eos_token=UpperCamelCase__   ,		sep_token=UpperCamelCase__   ,		cls_token=UpperCamelCase__   ,		unk_token=UpperCamelCase__   ,		pad_token=UpperCamelCase__   ,		mask_token=UpperCamelCase__   ,		add_prefix_space=UpperCamelCase__   ,		trim_offsets=UpperCamelCase__   ,		**UpperCamelCase__   ,		)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		UpperCamelCase__) != add_prefix_space:
               lowercase_     =      getattr(UpperCamelCase__   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**UpperCamelCase__)

         lowercase_     =      add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         lowercase_     =      "post_processor"
         lowercase_     =      getattr(self.backend_tokenizer   ,		UpperCamelCase__   ,		UpperCamelCase__)
         if tokenizer_component_instance:
               lowercase_     =      json.loads(tokenizer_component_instance.__getstate__())

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                     lowercase_     =      tuple(state["sep"])
               if "cls" in state:
                     lowercase_     =      tuple(state["cls"])

               lowercase_     =      False

               if state.get("add_prefix_space"   ,		UpperCamelCase__) != add_prefix_space:
                     lowercase_     =      add_prefix_space
                     lowercase_     =      True

               if state.get("trim_offsets"   ,		UpperCamelCase__) != trim_offsets:
                     lowercase_     =      trim_offsets
                     lowercase_     =      True

               if changes_to_apply:
                     lowercase_     =      getattr(UpperCamelCase__   ,		state.pop("type"))
                     lowercase_     =      component_class(**UpperCamelCase__)
                     setattr(self.backend_tokenizer   ,		UpperCamelCase__   ,		UpperCamelCase__)


   @property
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					str:





         if self._mask_token is None:
               if self.verbose:
                     logger.error("Using mask_token, but it is not set yet.")
               return None
         return str(self._mask_token)


   @mask_token.setter
   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[Any])      ->					Tuple:
         lowercase_     =      AddedToken(UpperCamelCase__   ,		lstrip=UpperCamelCase__   ,		rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__   ,		UpperCamelCase__) else value
         lowercase_     =      value


   def __UpperCAmelCase       (   self				:	Tuple   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Dict)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		UpperCamelCase__)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                   "to use it with pretokenized inputs.")

         return super()._batch_encode_plus(*UpperCamelCase__   ,		**UpperCamelCase__)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		*__lowerCAmelCase				:	Optional[Any]   ,		**__lowerCAmelCase				:	Dict)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		UpperCamelCase__)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                   "to use it with pretokenized inputs.")

         return super()._encode_plus(*UpperCamelCase__   ,		**UpperCamelCase__)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Union[str, Any] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(UpperCamelCase__   ,		name=UpperCamelCase__)
         return tuple(UpperCamelCase__)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[int]=None)      ->					List[Any]:
         lowercase_     =      [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 __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[Any] = None)      ->					List[int]:
         lowercase_     =      [self.sep_token_id]
         lowercase_     =      [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]







'''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 __a (	__lowerCamelCase			:						List[Any]				)	->							Tuple:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      {}

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

            lowercase_     =      key.replace("heads.cmd.mim_head.cls.predictions" ,		"mmm_image_head"				)
            lowercase_     =      key.replace("heads.cmd.mlm_head.cls.predictions" ,		"mmm_text_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.cls" ,		"itm_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.pooler" ,		"itm_head.pooler"				)
            lowercase_     =      key.replace("heads.cmd.clip_head.logit_scale" ,		"flava.logit_scale"				)
            lowercase_     =      key.replace("heads.fairseq_mlm.cls.predictions" ,		"mlm_head"				)
            lowercase_     =      key.replace("heads.imagenet.mim_head.cls.predictions" ,		"mim_head"				)
            lowercase_     =      key.replace("mm_text_projection" ,		"flava.text_to_mm_projection"				)
            lowercase_     =      key.replace("mm_image_projection" ,		"flava.image_to_mm_projection"				)
            lowercase_     =      key.replace("image_encoder.module" ,		"flava.image_model"				)
            lowercase_     =      key.replace("text_encoder.module" ,		"flava.text_model"				)
            lowercase_     =      key.replace("mm_encoder.module.encoder.cls_token" ,		"flava.multimodal_model.cls_token"				)
            lowercase_     =      key.replace("mm_encoder.module" ,		"flava.multimodal_model"				)
            lowercase_     =      key.replace("text_projection" ,		"flava.text_projection"				)
            lowercase_     =      key.replace("image_projection" ,		"flava.image_projection"				)

            lowercase_     =      value.float()

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

      return upgrade


@torch.no_grad()
def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple=None				)	->							Optional[int]:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      FlavaConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      FlavaConfig()

      lowercase_     =      FlavaForPreTraining(__lowerCamelCase				).eval()

      lowercase_     =      convert_dalle_checkpoint(__lowerCamelCase ,		__lowerCamelCase ,		save_checkpoint=__lowerCamelCase				)

      if os.path.exists(__lowerCamelCase				):
            lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      else:
            lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      upgrade_state_dict(__lowerCamelCase ,		__lowerCamelCase				)
      hf_model.load_state_dict(__lowerCamelCase				)
      lowercase_     =      hf_model.state_dict()
      lowercase_     =      count_parameters(__lowerCamelCase				)
      lowercase_     =      count_parameters(__lowerCamelCase				) + count_parameters(__lowerCamelCase				)

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

      hf_model.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      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")
       lowerCAmelCase_ : List[Any]						  =      parser.parse_args()

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






