



'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		_a     :      List[str]        =							s.rsplit(lowerCAmelCase__						,			lowerCAmelCase__  )
		return new.join(lowerCAmelCase__  )



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[str]:
		return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()  )



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Dict:
		_a     :      str        =							{}

		_a     :      int        =							['group_1', 'group_2', 'group_3', 'group_4']
		for key, value in state_dict.items():
				for group_key in group_keys:
						if group_key in key:
								_a     :      Any        =							key.replace(f"""{group_key}."""						,			f"""{group_key}.group."""  )

				if "res_path" in key:
						_a     :      Optional[Any]        =							key.replace('res_path.'						,			'res_path.path.'  )

				if key.endswith('.w'  ):
						_a     :      Optional[Any]        =							rreplace(lowerCAmelCase__						,			'.w'						,			'.weight'						,			1  )
				if key.endswith('.b'  ):
						_a     :      Tuple        =							rreplace(lowerCAmelCase__						,			'.b'						,			'.bias'						,			1  )

				_a     :      Dict        =							value.float()

		return upgrade



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=True  )			->      str:
		from dall_e import Encoder

		_a     :      int        =							Encoder()
		if os.path.exists(lowerCAmelCase__  ):
				_a     :      str        =							torch.load(lowerCAmelCase__  )
		else:
				_a     :      Tuple        =							torch.hub.load_state_dict_from_url(lowerCAmelCase__  )

		if isinstance(lowerCAmelCase__						,			lowerCAmelCase__  ):
				_a     :      Optional[Any]        =							ckpt.state_dict()
		encoder.load_state_dict(lowerCAmelCase__  )

		if config_path is not None:
				_a     :      List[Any]        =							FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase__  )
		else:
				_a     :      Tuple        =							FlavaImageCodebookConfig()

		_a     :      Optional[int]        =							FlavaImageCodebook(lowerCAmelCase__  ).eval()
		_a     :      Optional[Any]        =							encoder.state_dict()

		_a     :      Dict        =							upgrade_state_dict(lowerCAmelCase__  )
		hf_model.load_state_dict(lowerCAmelCase__  )
		_a     :      List[str]        =							hf_model.state_dict()
		_a     :      Union[str, Any]        =							count_parameters(lowerCAmelCase__  )
		_a     :      Optional[Any]        =							count_parameters(lowerCAmelCase__  )

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

		if save_checkpoint:
				hf_model.save_pretrained(lowerCAmelCase__  )
		else:
				return hf_state_dict


if __name__ == "__main__":
							__lowerCAmelCase									=   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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							__lowerCAmelCase									=   parser.parse_args()

							convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase    :       List[str]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       str					     ={
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Tuple							  =	'dpr'

      def __init__(						self		,       __lowerCAmelCase=3_0522		,       __lowerCAmelCase=768		,       __lowerCAmelCase=12		,       __lowerCAmelCase=12		,       __lowerCAmelCase=3072		,       __lowerCAmelCase="gelu"		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=512		,       __lowerCAmelCase=2		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-12		,       __lowerCAmelCase=0		,       __lowerCAmelCase="absolute"		,       __lowerCAmelCase = 0		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(pad_token_id=__lowerCAmelCase		,       **__lowerCAmelCase						)

            lowercase														=	vocab_size
            lowercase														=	hidden_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	intermediate_size
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_probs_dropout_prob
            lowercase														=	max_position_embeddings
            lowercase														=	type_vocab_size
            lowercase														=	initializer_range
            lowercase														=	layer_norm_eps
            lowercase														=	projection_dim
            lowercase														=	position_embedding_type



