




'''simple docstring'''







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig





def 						snake_case_							(_a	:							Optional[int]    ,   _a	:							Union[str, Any]    ,   _a	:							List[str]    ,   _a	:							List[str]     ):
    UpperCAmelCase			       =		s.rsplit(_a    ,   _a     )
    return new.join(_a     )





def 						snake_case_							(_a	:							Union[str, Any]     ):
    # 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 						snake_case_							(_a	:							Optional[Any]     ):
    UpperCAmelCase			       =		{}

    UpperCAmelCase			       =		['''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:
                UpperCAmelCase			       =		key.replace(F"{group_key}."    ,   F"{group_key}.group."     )

        if "res_path" in key:
            UpperCAmelCase			       =		key.replace('''res_path.'''    ,   '''res_path.path.'''     )

        if key.endswith('''.w'''     ):
            UpperCAmelCase			       =		rreplace(_a    ,   '''.w'''    ,   '''.weight'''    ,   1     )
        if key.endswith('''.b'''     ):
            UpperCAmelCase			       =		rreplace(_a    ,   '''.b'''    ,   '''.bias'''    ,   1     )

        UpperCAmelCase			       =		value.float()

    return upgrade





@torch.no_grad()
def 						snake_case_							(_a	:							Tuple    ,   _a	:							Optional[int]    ,   _a	:							Any=None    ,   _a	:							Any=True     ):
    from dall_e import Encoder

    UpperCAmelCase			       =		Encoder()
    if os.path.exists(_a     ):
        UpperCAmelCase			       =		torch.load(_a     )
    else:
        UpperCAmelCase			       =		torch.hub.load_state_dict_from_url(_a     )

    if isinstance(_a    ,   _a     ):
        UpperCAmelCase			       =		ckpt.state_dict()
    encoder.load_state_dict(_a     )

    if config_path is not None:
        UpperCAmelCase			       =		FlavaImageCodebookConfig.from_pretrained(_a     )
    else:
        UpperCAmelCase			       =		FlavaImageCodebookConfig()

    UpperCAmelCase			       =		FlavaImageCodebook(_a     ).eval()
    UpperCAmelCase			       =		encoder.state_dict()

    UpperCAmelCase			       =		upgrade_state_dict(_a     )
    hf_model.load_state_dict(_a     )
    UpperCAmelCase			       =		hf_model.state_dict()
    UpperCAmelCase			       =		count_parameters(_a     )
    UpperCAmelCase			       =		count_parameters(_a     )

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

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


if __name__ == "__main__":
   A     =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')
   A     =parser.parse_args()

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










'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a  (	__a ):
       __a				:     int   				=					["""image_processor""", """tokenizer"""]
       __a				:     Union[str, Any]   				=					"""ChineseCLIPImageProcessor"""
       __a				:     List[Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Dict   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Dict=None   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''   ,				lowercase   ,				)
               UpperCAmelCase			       =		kwargs.pop('''feature_extractor'''				)

           UpperCAmelCase			       =		image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''				)
           if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''				)

           super().__init__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Tuple   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	int=None   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

           if text is not None:
               UpperCAmelCase			       =		self.tokenizer(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if images is not None:
               UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if text is not None and images is not None:
               UpperCAmelCase			       =		image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowercase				)   ,				tensor_type=lowercase				)
       def     A      (	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				*lowercase						:	int   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







       @property
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,				lowercase   ,				)
           return self.image_processor_class




