



"""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 __lowercase    (							_a					):
    # 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 __lowercase    (							_a	,    _a					):
    snake_case_				:  Any       =			{}

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

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

        snake_case_				:  int       =			value.float()

    for key, value in codebook_state_dict.items():
        snake_case_				:  List[Any]       =			value

    return upgrade




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a	,    _a=None					):
    if config_path is not None:
        snake_case_				:  Dict       =			FlavaConfig.from_pretrained(_a					)
    else:
        snake_case_				:  Dict       =			FlavaConfig()

    snake_case_				:  Optional[Any]       =			FlavaForPreTraining(_a					).eval()

    snake_case_				:  Dict       =			convert_dalle_checkpoint(_a	,    _a	,    save_checkpoint=_a					)

    if os.path.exists(_a					):
        snake_case_				:  int       =			torch.load(_a	,    map_location='''cpu'''					)
    else:
        snake_case_				:  Optional[int]       =			torch.hub.load_state_dict_from_url(_a	,    map_location='''cpu'''					)

    snake_case_				:  str       =			upgrade_state_dict(_a	,    _a					)
    hf_model.load_state_dict(_a					)
    snake_case_				:  Any       =			hf_model.state_dict()
    snake_case_				:  Any       =			count_parameters(_a					)
    snake_case_				:  int       =			count_parameters(_a					) + count_parameters(_a					)

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

    hf_model.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						Union[str, 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''')
     lowercase__					:						Optional[Any]												=			parser.parse_args()

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










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

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





class  _UpperCAmelCase (    lowerCAmelCase__):

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





