


"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def   snake_case						(				_a:     Optional[int]		,   _a:     Any		,   _a:     Optional[int]		,   _a:     Tuple							)->		Any:
      '''simple docstring'''


      lowerCamelCase__           =		s.rsplit(_a		,   _a							)
      return new.join(_a							)



def   snake_case						(				_a:     List[str]							)->		Optional[Any]:
      '''simple docstring'''


      return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()							)



def   snake_case						(				_a:     str							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		{}

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

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

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

            lowerCamelCase__           =		value.float()

      return upgrade



@torch.no_grad()
def   snake_case						(				_a:     List[str]		,   _a:     Union[str, Any]		,   _a:     Any=None		,   _a:     Dict=True							)->		int:
      '''simple docstring'''


      from dall_e import Encoder

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

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

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

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

      lowerCamelCase__           =		upgrade_state_dict(_a							)
      hf_model.load_state_dict(_a							)
      lowerCamelCase__           =		hf_model.state_dict()
      lowerCamelCase__           =		count_parameters(_a							)
      lowerCamelCase__           =		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__":
   _snake_case          =							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")
   _snake_case          =							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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



