


'''simple docstring'''






import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:List[str]		) ->	Optional[Any]:




     '''simple docstring'''



     _a   , _a   , _a         =				pt_weights[f'{prefix}.in_proj_weight'].chunk(3					,    dim=0		)
     _a   , _a   , _a         =				pt_weights[f'{prefix}.in_proj_bias'].chunk(3					,    dim=0		)

     _a         =				pt_weights[f'{prefix}.out_proj.weight']
     _a         =				pt_weights[f'{prefix}.out_proj.bias']

     _a         =				q_proj
     _a         =				q_proj_bias

     _a         =				k_proj
     _a         =				k_proj_bias

     _a         =				v_proj
     _a         =				v_proj_bias

     _a         =				out_proj_weights
     _a         =				out_proj_bias






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:List[str]		) ->	str:




     '''simple docstring'''



     copy_linear(hf_mlp.fca					,    lowerCAmelCase__					,    f'{prefix}.c_fc'		)
     copy_linear(hf_mlp.fca					,    lowerCAmelCase__					,    f'{prefix}.c_proj'		)






def 						_A					(lowerCAmelCase__	:str					,    lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:Optional[Any]		) ->	Optional[int]:




     '''simple docstring'''



     _a         =				pt_weights[f'{prefix}.weight'].data
     _a         =				pt_weights[f'{prefix}.bias'].data






def 						_A					(lowerCAmelCase__	:Any					,    lowerCAmelCase__	:int					,    lowerCAmelCase__	:Optional[int]		) ->	str:




     '''simple docstring'''



     copy_linear(hf_layer.layer_norma					,    lowerCAmelCase__					,    f'{prefix}.ln_1'		)
     copy_linear(hf_layer.layer_norma					,    lowerCAmelCase__					,    f'{prefix}.ln_2'		)

     # copy MLP
     copy_mlp(hf_layer.mlp					,    lowerCAmelCase__					,    f'{prefix}.mlp'		)

     # copy attn
     copy_attn_layer(hf_layer.self_attn					,    lowerCAmelCase__					,    f'{prefix}.attn'		)






def 						_A					(lowerCAmelCase__	:Optional[Any]					,    lowerCAmelCase__	:int					,    lowerCAmelCase__	:Optional[int]		) ->	str:




     '''simple docstring'''



     for layer_id, hf_layer in enumerate(lowerCAmelCase__		):
          copy_layer(lowerCAmelCase__					,    lowerCAmelCase__					,    f'{prefix}.{layer_id}'		)






def 						_A					(lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:List[str]		) ->	Dict:




     '''simple docstring'''



     _a         =				pt_weights['text_projection'].data.T

     # copy text encoder
     for name, param in hf_model.text_model.named_parameters():
          _a         =				pt_weights[f'bert.{name}'].data






def 						_A					(lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:Tuple		) ->	Optional[Any]:




     '''simple docstring'''



     _a         =				pt_weights['visual.proj'].data.T

     # copy layer norms
     copy_linear(hf_model.vision_model.pre_layrnorm					,    lowerCAmelCase__					,    'visual.ln_pre'		)
     copy_linear(hf_model.vision_model.post_layernorm					,    lowerCAmelCase__					,    'visual.ln_post'		)

     # copy embeddings
     _a         =				pt_weights['visual.conv1.weight'].data
     _a         =				pt_weights['visual.class_embedding'].data
     _a         =				pt_weights['visual.positional_embedding'].data

     # copy encoder
     copy_layers(hf_model.vision_model.encoder.layers					,    lowerCAmelCase__					,    'visual.transformer.resblocks'		)






@torch.no_grad()
def 						_A					(lowerCAmelCase__	:Dict					,    lowerCAmelCase__	:str					,    lowerCAmelCase__	:List[str]=None		) ->	List[Any]:




     '''simple docstring'''



     assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
     _a         =				ChineseCLIPConfig.from_pretrained(lowerCAmelCase__		)

     _a         =				ChineseCLIPModel(lowerCAmelCase__		).eval()

     _a         =				torch.load(lowerCAmelCase__					,    map_location='cpu'		)['state_dict']
     _a         =				{(name[7:] if name.startswith('module.'		) else name): value for name, value in pt_weights.items()}

     copy_text_model_and_projection(lowerCAmelCase__					,    lowerCAmelCase__		)
     copy_vision_model_and_projection(lowerCAmelCase__					,    lowerCAmelCase__		)
     _a         =				pt_weights['logit_scale'].data

     hf_model.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
 a_       :     Tuple 							=     argparse.ArgumentParser()
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     help="Path to the output folder storing converted hf PyTorch model.",
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
 )
 parser.add_argument(
     "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
 )
 a_       :     Optional[int] 							=     parser.parse_args()

 convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
 print("The conversion is finished!")





'''simple docstring'''






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

a_       :     Union[str, Any] 							=     {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
           _a         =				CONFIG_MAPPING['swin'](
               image_size=2_24   ,					in_channels=3   ,					patch_size=4   ,					embed_dim=96   ,					depths=[2, 2, 6, 2]   ,					num_heads=[3, 6, 12, 24]   ,					window_size=7   ,					drop_path_rate=0.3   ,					use_absolute_embeddings=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _a         =				backbone_config

      _a         =				ignore_value
      _a         =				num_queries
      _a         =				no_object_weight
      _a         =				class_weight
      _a         =				mask_weight
      _a         =				dice_weight
      _a         =				contrastive_weight
      _a         =				contrastive_temperature
      _a         =				train_num_points
      _a         =				oversample_ratio
      _a         =				importance_sample_ratio
      _a         =				init_std
      _a         =				init_xavier_std
      _a         =				layer_norm_eps
      _a         =				is_training
      _a         =				use_auxiliary_loss
      _a         =				output_auxiliary_logits
      _a         =				strides
      _a         =				task_seq_len
      _a         =				text_encoder_width
      _a         =				text_encoder_context_length
      _a         =				text_encoder_num_layers
      _a         =				text_encoder_vocab_size
      _a         =				text_encoder_proj_layers
      _a         =				text_encoder_n_ctx
      _a         =				conv_dim
      _a         =				mask_dim
      _a         =				hidden_dim
      _a         =				encoder_feedforward_dim
      _a         =				norm
      _a         =				encoder_layers
      _a         =				decoder_layers
      _a         =				use_task_norm
      _a         =				num_attention_heads
      _a         =				dropout
      _a         =				dim_feedforward
      _a         =				pre_norm
      _a         =				enforce_input_proj
      _a         =				query_dec_layers
      _a         =				common_stride
      _a         =				decoder_layers

      super().__init__(**__magic_name__    )




 def 				__UpperCAmelCase		(			self    )       ->				Dict[str, any]:
      _a         =				copy.deepcopy(self.__dict__    )
      _a         =				self.backbone_config.to_dict()
      _a         =				self.__class__.model_type
      return output

