




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Dict				)  ->							Optional[int]:
    UpperCAmelCase_										=					s.rsplit(__UpperCamelCase	,					__UpperCamelCase				)
    return new.join(__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]				)  ->							Tuple:
    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(__UpperCamelCase	,					'''.w'''	,					'''.weight'''	,					1				)
        if key.endswith('''.b'''				):
            UpperCAmelCase_										=					rreplace(__UpperCamelCase	,					'''.b'''	,					'''.bias'''	,					1				)

        UpperCAmelCase_										=					value.float()

    return upgrade




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Optional[int]=None	,					__UpperCamelCase      :						List[Any]=True				)  ->							Tuple:
    from dall_e import Encoder

    UpperCAmelCase_										=					Encoder()
    if os.path.exists(__UpperCamelCase				):
        UpperCAmelCase_										=					torch.load(__UpperCamelCase				)
    else:
        UpperCAmelCase_										=					torch.hub.load_state_dict_from_url(__UpperCamelCase				)

    if isinstance(__UpperCamelCase	,					__UpperCamelCase				):
        UpperCAmelCase_										=					ckpt.state_dict()
    encoder.load_state_dict(__UpperCamelCase				)

    if config_path is not None:
        UpperCAmelCase_										=					FlavaImageCodebookConfig.from_pretrained(__UpperCamelCase				)
    else:
        UpperCAmelCase_										=					FlavaImageCodebookConfig()

    UpperCAmelCase_										=					FlavaImageCodebook(__UpperCamelCase				).eval()
    UpperCAmelCase_										=					encoder.state_dict()

    UpperCAmelCase_										=					upgrade_state_dict(__UpperCamelCase				)
    hf_model.load_state_dict(__UpperCamelCase				)
    UpperCAmelCase_										=					hf_model.state_dict()
    UpperCAmelCase_										=					count_parameters(__UpperCamelCase				)
    UpperCAmelCase_										=					count_parameters(__UpperCamelCase				)

    assert torch.allclose(__UpperCamelCase	,					__UpperCamelCase	,					atol=1e-3				)

    if save_checkpoint:
        hf_model.save_pretrained(__UpperCamelCase				)
    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)











from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )

         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					position_embedding_type
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					classifier_dropout



class       a					(       _A      ):

     '''simple docstring'''





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





