



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 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  int = 512  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						x.shape[1] if len(x.shape  ) > 1 else 1
 __SCREAMING_SNAKE_CASE							:     int							=						1 + np.arange(0		,						feature_num * offset		,						__lowerCamelCase		,						dtype=np.intaa  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						x + feature_offset
 return x






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  int=True  ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,						["cython"]  )

 if keep_features and "edge_attr" in item.keys():  # edge_attr
  __SCREAMING_SNAKE_CASE							:     List[str]							=						np.asarray(item["edge_attr"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     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
  __SCREAMING_SNAKE_CASE							:     Dict							=						np.asarray(item["node_feat"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						np.ones((item["num_nodes"], 1)		,						dtype=np.intaa  )  # same embedding for all

 __SCREAMING_SNAKE_CASE							:     Any							=						np.asarray(item["edge_index"]		,						dtype=np.intaa  )

 __SCREAMING_SNAKE_CASE							:     Any							=						convert_to_single_emb(__lowerCamelCase  ) + 1
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						item["num_nodes"]

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

 # node adj matrix [num_nodes, num_nodes] bool
 __SCREAMING_SNAKE_CASE							:     int							=						np.zeros([num_nodes, num_nodes]		,						dtype=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						True

 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.floyd_warshall(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Tuple							=						np.amax(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.gen_edge_input(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						np.zeros([num_nodes + 1, num_nodes + 1]		,						dtype=np.single  )  # with graph token

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

 return item






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				List[str]=2_0  ,	UpperCamelCase			:				Dict=False					)->Any:
      if not is_cython_available():
       raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						spatial_pos_max
      __SCREAMING_SNAKE_CASE							:     Dict							=						on_the_fly_processing






     def __call__(							self			:				Optional[int]  ,	UpperCamelCase			:				List[dict]					)->Dict[str, Any]:
      if self.on_the_fly_processing:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[preprocess_item(UpperCamelCase					) for i in features]

      if not isinstance(features[0]  ,	UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						[vars(UpperCamelCase					) for f in features]
      __SCREAMING_SNAKE_CASE							:     str							=						{}

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

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.zeros(UpperCamelCase  ,	max_node_num + 1  ,	max_node_num + 1  ,	dtype=torch.float					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Any							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.zeros(
          UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)

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

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

       __SCREAMING_SNAKE_CASE							:     str							=						f["attn_bias"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f[
           "attn_edge_type"
       ]
       __SCREAMING_SNAKE_CASE							:     Dict							=						f["spatial_pos"]
       __SCREAMING_SNAKE_CASE							:     int							=						f["in_degree"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f["input_nodes"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						f["input_edges"]

      __SCREAMING_SNAKE_CASE							:     Any							=						batch["in_degree"]

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

      return batch













import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

		# Encoder Layers
		for layer in range(config.num_hidden_layers		):
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''scale''']		)
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''kernel''']		).T
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''bias''']		)

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

		# Masked LM Layers
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''bias''']		)
		lowerCamelCase :			Optional[Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''scale''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''bias''']		)
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_output''']['''output_bias''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']		)

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			Any    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']		)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

		# Save PreTrained
		print(F"Saving pretrained model to {save_path}"		)
		fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_				=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--fnet_config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained FNet model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

