





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
 import pyximport

 pyximport.install(setup_args={'''include_dirs''': np.get_include()})
 from . import algos_graphormer  # noqa E402

def      __snake_case						(					_lowerCAmelCase : Tuple				,				_lowerCAmelCase : int = 512	) ->						Optional[int]:
   A_					:				int          =			x.shape[1] if len(x.shape	) > 1 else 1
   A_					:				Union[str, Any]          =			1 + np.arange(0				,				feature_num * offset				,				_lowerCAmelCase				,				dtype=np.intaa	)
   A_					:				Union[str, Any]          =			x + feature_offset
   return x

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Union[str, Any]=True	) ->						Optional[Any]:
   requires_backends(_lowerCAmelCase				,				["cython"]	)

   if keep_features and "edge_attr" in item.keys():  # edge_attr
      A_					:				Union[str, Any]          =			np.asarray(item["edge_attr"]				,				dtype=np.intaa	)
   else:
      A_					:				Any          =			np.ones((len(item["edge_index"][0]	), 1)				,				dtype=np.intaa	)  # same embedding for all

   if keep_features and "node_feat" in item.keys():  # input_nodes
      A_					:				Optional[Any]          =			np.asarray(item["node_feat"]				,				dtype=np.intaa	)
   else:
      A_					:				Tuple          =			np.ones((item["num_nodes"], 1)				,				dtype=np.intaa	)  # same embedding for all

   A_					:				Tuple          =			np.asarray(item["edge_index"]				,				dtype=np.intaa	)

   A_					:				Dict          =			convert_to_single_emb(_lowerCAmelCase	) + 1
   A_					:				Optional[Any]          =			item["num_nodes"]

   if len(edge_attr.shape	) == 1:
      A_					:				Tuple          =			edge_attr[:, None]
   A_					:				str          =			np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]				,				dtype=np.intaa	)
   A_					:				Union[str, Any]          =			convert_to_single_emb(_lowerCAmelCase	) + 1

   # node adj matrix [num_nodes, num_nodes] bool
   A_					:				Optional[int]          =			np.zeros([num_nodes, num_nodes]				,				dtype=_lowerCAmelCase	)
   A_					:				Optional[Any]          =			True

   A_      ,						A_					:				str          =			algos_graphormer.floyd_warshall(_lowerCAmelCase	)
   A_					:				Optional[Any]          =			np.amax(_lowerCAmelCase	)

   A_					:				Optional[Any]          =			algos_graphormer.gen_edge_input(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Optional[Any]          =			np.zeros([num_nodes + 1, num_nodes + 1]				,				dtype=np.single	)  # with graph token

   # combine
   A_					:				Dict          =			input_nodes + 1  # we shift all indices by one for padding
   A_					:				List[str]          =			attn_bias
   A_					:				str          =			attn_edge_type
   A_					:				Any          =			shortest_path_result.astype(np.intaa	) + 1  # we shift all indices by one for padding
   A_					:				Any          =			np.sum(_lowerCAmelCase				,				axis=1	).reshape(-1	) + 1  # we shift all indices by one for padding
   A_					:				List[str]          =			item["in_degree"]  # for undirected graph
   A_					:				List[str]          =			input_edges + 1  # we shift all indices by one for padding
   if "labels" not in item:
      A_					:				List[Any]          =			item["y"]

   return item





class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :str			,      snake_case       :Dict=20			,      snake_case       :Dict=False  ):





        '''simple docstring'''




        if not is_cython_available():
           raise ImportError("Graphormer preprocessing needs Cython (pyximport)"  )

        A_					:				Union[str, Any]          =			spatial_pos_max
        A_					:				Union[str, Any]          =			on_the_fly_processing





     def __call__(							self       :List[str]			,      snake_case       :List[dict]  ):





        '''simple docstring'''




        if self.on_the_fly_processing:
           A_					:				Any          =			[preprocess_item(snake_case  ) for i in features]

        if not isinstance(features[0]			,      snake_case  ):
           A_					:				Tuple          =			[vars(snake_case  ) for f in features]
        A_					:				List[str]          =			{}

        A_					:				List[Any]          =			max(len(i["input_nodes"]  ) for i in features  )
        A_					:				Optional[int]          =			len(features[0]["input_nodes"][0]  )
        A_					:				Optional[int]          =			len(features[0]["attn_edge_type"][0][0]  )
        A_					:				str          =			max(len(i["input_edges"][0][0]  ) for i in features  )
        A_					:				List[Any]          =			len(features[0]["input_edges"][0][0][0]  )
        A_					:				Union[str, Any]          =			len(snake_case  )

        A_					:				List[str]          =			torch.zeros(snake_case			,      max_node_num + 1			,      max_node_num + 1			,      dtype=torch.float  )
        A_					:				Optional[int]          =			torch.zeros(snake_case			,      snake_case			,      snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				List[str]          =			torch.zeros(snake_case			,      snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				List[str]          =			torch.zeros(snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				Any          =			torch.zeros(snake_case			,      snake_case			,      snake_case			,      dtype=torch.long  )
        A_					:				List[Any]          =			torch.zeros(
            snake_case			,      snake_case			,      snake_case			,      snake_case			,      snake_case			,      dtype=torch.long  )

        for ix, f in enumerate(snake_case  ):
           for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
              A_					:				List[Any]          =			torch.tensor(f[k]  )

           if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]  ) > 0:
              A_					:				Optional[int]          =			float("-inf"  )

           A_					:				Tuple          =			f["attn_bias"]
           A_					:				List[str]          =			f[
               "attn_edge_type"
           ]
           A_					:				str          =			f["spatial_pos"]
           A_					:				Optional[int]          =			f["in_degree"]
           A_					:				Union[str, Any]          =			f["input_nodes"]
           A_					:				str          =			f["input_edges"]

        A_					:				List[Any]          =			batch["in_degree"]

        A_					:				Any          =			features[0]["labels"]
        if len(snake_case  ) == 1:  # one task
           if isinstance(sample[0]			,      snake_case  ):  # regression
              A_					:				int          =			torch.from_numpy(np.concatenate([i["labels"] for i in features]  )  )
           else:  # binary classification
              A_					:				Optional[Any]          =			torch.from_numpy(np.concatenate([i["labels"] for i in features]  )  )
        else:  # multi task classification, left to float to keep the NaNs
           A_					:				Optional[int]          =			torch.from_numpy(np.stack([i["labels"] for i in features]			,      axis=0  )  )

        return batch







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : Optional[Any]	) ->						List[str]:
   A_      ,						A_      ,						A_					:				Dict          =			pt_weights[f"{prefix}.in_proj_weight"].chunk(3				,				dim=0	)
   A_      ,						A_      ,						A_					:				Union[str, Any]          =			pt_weights[f"{prefix}.in_proj_bias"].chunk(3				,				dim=0	)

   A_					:				int          =			pt_weights[f"{prefix}.out_proj.weight"]
   A_					:				Dict          =			pt_weights[f"{prefix}.out_proj.bias"]

   A_					:				Optional[int]          =			q_proj
   A_					:				Optional[int]          =			q_proj_bias

   A_					:				Tuple          =			k_proj
   A_					:				str          =			k_proj_bias

   A_					:				Optional[int]          =			v_proj
   A_					:				int          =			v_proj_bias

   A_					:				int          =			out_proj_weights
   A_					:				List[Any]          =			out_proj_bias

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Dict				,				_lowerCAmelCase : int	) ->						Dict:
   copy_linear(hf_mlp.fca				,				_lowerCAmelCase				,				f"{prefix}.c_fc"	)
   copy_linear(hf_mlp.fca				,				_lowerCAmelCase				,				f"{prefix}.c_proj"	)

def      __snake_case						(					_lowerCAmelCase : Tuple				,				_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : Optional[int]	) ->						List[str]:
   A_					:				List[str]          =			pt_weights[f"{prefix}.weight"].data
   A_					:				int          =			pt_weights[f"{prefix}.bias"].data

def      __snake_case						(					_lowerCAmelCase : Dict				,				_lowerCAmelCase : str				,				_lowerCAmelCase : Optional[Any]	) ->						str:
   # copy layer norms
   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      __snake_case						(					_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Optional[Any]	) ->						List[Any]:
   for layer_id, hf_layer in enumerate(_lowerCAmelCase	):
      copy_layer(_lowerCAmelCase				,				_lowerCAmelCase				,				f"{prefix}.{layer_id}"	)

def      __snake_case						(					_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : Any	) ->						Dict:
   # copy projection
   A_					:				Optional[Any]          =			pt_weights["text_projection"].data.T

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

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Any	) ->						List[str]:
   # copy projection
   A_					:				Tuple          =			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_					:				List[Any]          =			pt_weights["visual.conv1.weight"].data
   A_					:				Union[str, Any]          =			pt_weights["visual.class_embedding"].data
   A_					:				Tuple          =			pt_weights["visual.positional_embedding"].data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers				,				_lowerCAmelCase				,				"visual.transformer.resblocks"	)

@torch.no_grad()
def      __snake_case						(					_lowerCAmelCase : List[Any]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Optional[int]=None	) ->						str:
   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   A_					:				Optional[Any]          =			ChineseCLIPConfig.from_pretrained(_lowerCAmelCase	)

   A_					:				Tuple          =			ChineseCLIPModel(_lowerCAmelCase	).eval()

   A_					:				Any          =			torch.load(_lowerCAmelCase				,				map_location="cpu"	)["state_dict"]
   A_					:				Optional[Any]          =			{(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_					:				List[Any]          =			pt_weights["logit_scale"].data

   hf_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 _lowerCAmelCase							:      Optional[Any]						=	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.'''
 )
 _lowerCAmelCase							:      Tuple						=	parser.parse_args()

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