


'''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 _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Union[str, Any]:






       """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 _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None       )       ->       Any:






       """simple docstring"""







       if config_path is not None:
              lowercase__      			= FlavaConfig.from_pretrained(A       )
       else:
              lowercase__      			= FlavaConfig()

       lowercase__      			= FlavaForPreTraining(A       ).eval()

       lowercase__      			= convert_dalle_checkpoint(A  , A  , save_checkpoint=A       )

       if os.path.exists(A       ):
              lowercase__      			= torch.load(A  , map_location='''cpu'''       )
       else:
              lowercase__      			= torch.hub.load_state_dict_from_url(A  , map_location='''cpu'''       )

       lowercase__      			= upgrade_state_dict(A  , A       )
       hf_model.load_state_dict(A       )
       lowercase__      			= hf_model.state_dict()
       lowercase__      			= count_parameters(A       )
       lowercase__      			= count_parameters(A       ) + count_parameters(A       )

       assert torch.allclose(A  , A  , atol=1E-3       )

       hf_model.save_pretrained(A       )


if __name__ == "__main__":
 lowerCamelCase							:   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							:   Tuple  						=	parser.parse_args()

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








'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 lowerCamelCase							:   Any  						=	None


lowerCamelCase							:   Dict  						=	logging.get_logger(__name__)

lowerCamelCase							:   Optional[int]  						=	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase							:   List[str]  						=	{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase							:   Any  						=	{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							VOCAB_FILES_NAMES
      lowerCAmelCase__					:     str  					=							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__					:     Optional[Any]  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__					:     int  					=							["""input_ids""", """attention_mask"""]
      lowerCAmelCase__					:     Optional[int]  					=							TaTokenizer

      lowerCAmelCase__					:     List[int]  					=							[]



      def __init__(self    :    Dict   ,						UpperCamelCase    :    str=None   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    Any="</s>"   ,						UpperCamelCase    :    str="<unk>"   ,						UpperCamelCase    :    List[str]="<pad>"   ,						UpperCamelCase    :    List[str]=100   ,						UpperCamelCase    :    Tuple=None   ,						**UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             if extra_ids > 0 and additional_special_tokens is None:
                    lowercase__      			= [f"<extra_id_{i}>" for i in range(UpperCamelCase )]
             elif extra_ids > 0 and additional_special_tokens is not None:
                    # Check that we have the right number of extra special tokens
                    lowercase__      			= len(set(filter(lambda UpperCamelCase : bool('''extra_id_''' in str(UpperCamelCase ) )   ,						UpperCamelCase ) ) )
                    if extra_tokens != extra_ids:
                           raise ValueError(
                               f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                               ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                               ''' tokens''' )

             super().__init__(
                 UpperCamelCase   ,						tokenizer_file=UpperCamelCase   ,						eos_token=UpperCamelCase   ,						unk_token=UpperCamelCase   ,						pad_token=UpperCamelCase   ,						extra_ids=UpperCamelCase   ,						additional_special_tokens=UpperCamelCase   ,						**UpperCamelCase   ,						)

             lowercase__      			= vocab_file
             lowercase__      			= False if not self.vocab_file else True
             lowercase__      			= extra_ids



      @staticmethod
      def       UpperCamelCase__		(UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any] ):

             '''simple docstring'''







             if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                    lowercase__      			= TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                    if init_max_model_length is not None and init_max_model_length != max_model_length:
                           return init_max_model_length
                    elif init_max_model_length is None:
                           warnings.warn(
                               '''This tokenizer was incorrectly instantiated with a model max length of'''
                               f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
                               ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                               ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                               f" {pretrained_model_name_or_path} automatically truncating your input to"
                               f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
                               f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
                               ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                               ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''   ,						UpperCamelCase   ,						)

             return max_model_length



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[str] = None ):

             '''simple docstring'''







             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                        '''tokenizer.''' )

             if not os.path.isdir(UpperCamelCase ):
                    logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
                    return
             lowercase__      			= os.path.join(
                 UpperCamelCase   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

             if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase ):
                    copyfile(self.vocab_file   ,						UpperCamelCase )
                    logger.info(f"Copy vocab file to {out_vocab_file}" )

             return (out_vocab_file,)



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a
             else:
                    lowercase__      			= token_ids_a + [self.eos_token_id]
                    return self.prefix_tokens + token_ids_a + token_ids_a



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= [self.eos_token_id]

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



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return list(
                 set(filter(lambda UpperCamelCase : bool(re.search(R'''<extra_id_\d+>'''   ,						UpperCamelCase ) ) is not None   ,						self.additional_special_tokens ) ) )






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return [self.convert_tokens_to_ids(UpperCamelCase ) for token in self.get_sentinel_tokens()]




