
'''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 :     str				):



      """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 :     Optional[Any]					,						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: str		    = {}

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

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

            _lowerCamelCase					: Dict		    = value.float()

      for key, value in codebook_state_dict.items():
            _lowerCamelCase					: Union[str, Any]		    = value

      return upgrade







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



      """simple docstring"""




      if config_path is not None:
            _lowerCamelCase					: Optional[Any]		    = FlavaConfig.from_pretrained(_lowerCAmelCase				)
      else:
            _lowerCamelCase					: List[Any]		    = FlavaConfig()

      _lowerCamelCase					: Union[str, Any]		    = FlavaForPreTraining(_lowerCAmelCase				).eval()

      _lowerCamelCase					: List[Any]		    = convert_dalle_checkpoint(_lowerCAmelCase					,						_lowerCAmelCase					,						save_checkpoint=_lowerCAmelCase				)

      if os.path.exists(_lowerCAmelCase				):
            _lowerCamelCase					: str		    = torch.load(_lowerCAmelCase					,						map_location="cpu"				)
      else:
            _lowerCamelCase					: Tuple		    = torch.hub.load_state_dict_from_url(_lowerCAmelCase					,						map_location="cpu"				)

      _lowerCamelCase					: Union[str, Any]		    = upgrade_state_dict(_lowerCAmelCase					,						_lowerCAmelCase				)
      hf_model.load_state_dict(_lowerCAmelCase				)
      _lowerCamelCase					: Optional[Any]		    = hf_model.state_dict()
      _lowerCamelCase					: Dict		    = count_parameters(_lowerCAmelCase				)
      _lowerCamelCase					: Tuple		    = count_parameters(_lowerCAmelCase				) + count_parameters(_lowerCAmelCase				)

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

      hf_model.save_pretrained(_lowerCAmelCase				)


if __name__ == "__main__":
 UpperCAmelCase_							: Optional[Any] =							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_							: Tuple =							parser.parse_args()

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



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     List[str]       =     {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''camembert'''


     def __init__(  self							,					A_=3_05_22							,					A_=7_68							,					A_=12							,					A_=12							,					A_=30_72							,					A_="gelu"							,					A_=0.1							,					A_=0.1							,					A_=5_12							,					A_=2							,					A_=0.0_2							,					A_=1e-12							,					A_=1							,					A_=0							,					A_=2							,					A_="absolute"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					bos_token_id=A_							,					eos_token_id=A_							,					**A_    )

       _UpperCAmelCase		:							Optional[Any]							=     vocab_size
       _UpperCAmelCase		:							int							=     hidden_size
       _UpperCAmelCase		:							Any							=     num_hidden_layers
       _UpperCAmelCase		:							Optional[Any]							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     hidden_act
       _UpperCAmelCase		:							str							=     intermediate_size
       _UpperCAmelCase		:							str							=     hidden_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     max_position_embeddings
       _UpperCAmelCase		:							Union[str, Any]							=     type_vocab_size
       _UpperCAmelCase		:							List[Any]							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							int							=     position_embedding_type
       _UpperCAmelCase		:							Tuple							=     use_cache
       _UpperCAmelCase		:							Any							=     classifier_dropout


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     @property
     def        __snake_case(  self    ):
       if self.task == "multiple-choice":
         _UpperCAmelCase		:							Any							=     {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _UpperCAmelCase		:							List[Any]							=     {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]    )





