






'''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					(  _UpperCamelCase							):


   """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					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     {}

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

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

      lowercase_	:    List[str]           =     value.float()

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

   return upgrade




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




   if config_path is not None:
      lowercase_	:    List[Any]           =     FlavaConfig.from_pretrained(_UpperCamelCase							)
   else:
      lowercase_	:    str           =     FlavaConfig()

   lowercase_	:    Optional[Any]           =     FlavaForPreTraining(_UpperCamelCase							).eval()

   lowercase_	:    Any           =     convert_dalle_checkpoint(_UpperCamelCase						,		_UpperCamelCase						,		save_checkpoint=_UpperCamelCase							)

   if os.path.exists(_UpperCamelCase							):
      lowercase_	:    Optional[int]           =     torch.load(_UpperCamelCase						,		map_location="cpu"							)
   else:
      lowercase_	:    str           =     torch.hub.load_state_dict_from_url(_UpperCamelCase						,		map_location="cpu"							)

   lowercase_	:    str           =     upgrade_state_dict(_UpperCamelCase						,		_UpperCamelCase							)
   hf_model.load_state_dict(_UpperCamelCase							)
   lowercase_	:    Tuple           =     hf_model.state_dict()
   lowercase_	:    Optional[Any]           =     count_parameters(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{
    'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   List[Any]		       =    'gpt_neox_japanese'




    def __init__(			self  :       List[str]  ,     a  :       List[Any]=3_2_0_0_0  ,     a  :       Union[str, Any]=2_5_6_0  ,     a  :       Optional[Any]=3_2  ,     a  :       Any=3_2  ,     a  :       str=4  ,     a  :       Optional[int]="gelu"  ,     a  :       Optional[Any]=1.00  ,     a  :       Dict=1_0_0_0_0  ,     a  :       List[Any]=2_0_4_8  ,     a  :       Dict=0.02  ,     a  :       int=1e-5  ,     a  :       Optional[int]=True  ,     a  :       Union[str, Any]=3_1_9_9_6  ,     a  :       List[Any]=3_1_9_9_9  ,     a  :       List[str]=0.1  ,     a  :       Dict=0.0  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       super().__init__(bos_token_id=a  ,     eos_token_id=a  ,     **a			)
       lowercase_	:    int           =     vocab_size
       lowercase_	:    int           =     max_position_embeddings
       lowercase_	:    List[str]           =     hidden_size
       lowercase_	:    Any           =     num_hidden_layers
       lowercase_	:    Any           =     num_attention_heads
       lowercase_	:    List[Any]           =     intermediate_multiple_size
       lowercase_	:    List[str]           =     hidden_act
       lowercase_	:    Optional[int]           =     rotary_pct
       lowercase_	:    Tuple           =     rotary_emb_base
       lowercase_	:    Optional[Any]           =     initializer_range
       lowercase_	:    List[str]           =     layer_norm_eps
       lowercase_	:    List[str]           =     use_cache
       lowercase_	:    Any           =     attention_dropout
       lowercase_	:    List[Any]           =     hidden_dropout




