



"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def        A_  (      _lowercase, _lowercase, _lowercase     ):







						'''simple docstring'''


						snake_case_			:Optional[Any]						=      TaConfig.from_pretrained(_lowercase     )
						snake_case_			:Any						=      FlaxTaForConditionalGeneration(config=_lowercase     )
						snake_case_			:Tuple						=      checkpoints.load_tax_checkpoint(_lowercase     )

						snake_case_			:Tuple						=      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

						# Encoder
						for layer_index in range(config.num_layers     ):
												snake_case_			:List[str]						=      f"""layers_{str(_lowercase     )}"""

												# Self-Attention
												snake_case_			:List[Any]						=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
												snake_case_			:Optional[int]						=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
												snake_case_			:Tuple						=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
												snake_case_			:int						=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

												# Layer Normalization
												snake_case_			:Any						=      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

												if split_mlp_wi:
																		snake_case_			:Tuple						=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
																		snake_case_			:Tuple						=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
												else:
																		snake_case_			:Dict						=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

												snake_case_			:List[str]						=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

												# Layer Normalization
												snake_case_			:Optional[Any]						=      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

												# Assigning
												snake_case_			:Tuple						=      tax_attention_key
												snake_case_			:Optional[Any]						=      tax_attention_out
												snake_case_			:Optional[int]						=      tax_attention_query
												snake_case_			:str						=      tax_attention_value

												snake_case_			:Any						=      tax_attention_layer_norm

												if split_mlp_wi:
																		snake_case_			:Any						=      tax_mlp_wi_a
																		snake_case_			:Optional[int]						=      tax_mlp_wi_a
												else:
																		snake_case_			:List[str]						=      tax_mlp_wi

												snake_case_			:List[str]						=      tax_mlp_wo
												snake_case_			:Union[str, Any]						=      tax_mlp_layer_norm

						# Only for layer 0:
						snake_case_			:str						=      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
						snake_case_			:List[str]						=      tax_encoder_rel_embedding

						# Assigning
						snake_case_			:List[str]						=      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
						snake_case_			:str						=      tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers     ):
												snake_case_			:Tuple						=      f"""layers_{str(_lowercase     )}"""

												# Self-Attention
												snake_case_			:List[str]						=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
												snake_case_			:List[Any]						=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
												snake_case_			:int						=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
												snake_case_			:Union[str, Any]						=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

												# Layer Normalization
												snake_case_			:Optional[int]						=      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
												    """scale"""
												]

												# Encoder-Decoder-Attention
												snake_case_			:Union[str, Any]						=      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
												    """kernel"""
												]
												snake_case_			:Optional[int]						=      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
												    """kernel"""
												]
												snake_case_			:List[Any]						=      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
												    """kernel"""
												]
												snake_case_			:Dict						=      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
												    """kernel"""
												]

												# Layer Normalization
												snake_case_			:Any						=      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

												# MLP
												if split_mlp_wi:
																		snake_case_			:str						=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
																		snake_case_			:Union[str, Any]						=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
												else:
																		snake_case_			:int						=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

												snake_case_			:List[str]						=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

												# Layer Normalization
												snake_case_			:List[Any]						=      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

												# Assigning
												snake_case_			:int						=      tax_attention_key
												snake_case_			:Any						=      tax_attention_out
												snake_case_			:Any						=      tax_attention_query
												snake_case_			:Tuple						=      tax_attention_value

												snake_case_			:Tuple						=      tax_pre_attention_layer_norm

												snake_case_			:Union[str, Any]						=      tax_enc_dec_attention_key
												snake_case_			:Optional[int]						=      tax_enc_dec_attention_out
												snake_case_			:Dict						=      tax_enc_dec_attention_query
												snake_case_			:Union[str, Any]						=      tax_enc_dec_attention_value

												snake_case_			:List[str]						=      tax_cross_layer_norm

												if split_mlp_wi:
																		snake_case_			:Optional[Any]						=      tax_mlp_wi_a
																		snake_case_			:List[str]						=      tax_mlp_wi_a
												else:
																		snake_case_			:Union[str, Any]						=      tax_mlp_wi

												snake_case_			:List[Any]						=      tax_mlp_wo

												snake_case_			:str						=      txa_mlp_layer_norm

						# Decoder Normalization
						snake_case_			:Dict						=      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
						snake_case_			:int						=      txa_decoder_norm

						# Only for layer 0:
						snake_case_			:Union[str, Any]						=      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
						snake_case_			:Tuple						=      tax_decoder_rel_embedding

						# Token Embeddings
						snake_case_			:List[str]						=      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
						snake_case_			:List[Any]						=      txa_token_embeddings

						# LM Head (only in v1.1 checkpoints)
						if "logits_dense" in tax_model["target"]["decoder"]:
												snake_case_			:Optional[Any]						=      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

						flax_model.save_pretrained(_lowercase     )
						print("""T5X Model was sucessfully converted!"""     )


if __name__ == "__main__":
						__a     =    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
						)
						parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
						parser.add_argument(
						    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
						)
						__a     =    parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""
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        A_  (      _lowercase, _lowercase = 512     ):







						'''simple docstring'''


						snake_case_			:int						=      x.shape[1] if len(x.shape     ) > 1 else 1
						snake_case_			:List[Any]						=      1 + np.arange(0, feature_num * offset, _lowercase, dtype=np.intaa     )
						snake_case_			:int						=      x + feature_offset
						return x
def        A_  (      _lowercase, _lowercase=True     ):







						'''simple docstring'''


						requires_backends(_lowercase, ["""cython"""]     )

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

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

						snake_case_			:str						=      convert_to_single_emb(_lowercase     ) + 1
						snake_case_			:str						=      item["""num_nodes"""]

						if len(edge_attr.shape     ) == 1:
												snake_case_			:Any						=      edge_attr[:, None]
						snake_case_			:Dict						=      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]], dtype=np.intaa     )
						snake_case_			:str						=      convert_to_single_emb(_lowercase     ) + 1

						# node adj matrix [num_nodes, num_nodes] bool
						snake_case_			:List[str]						=      np.zeros([num_nodes, num_nodes], dtype=_lowercase     )
						snake_case_			:int						=      True

						snake_case_,	snake_case_			:Tuple						=      algos_graphormer.floyd_warshall(_lowercase     )
						snake_case_			:List[str]						=      np.amax(_lowercase     )

						snake_case_			:Dict						=      algos_graphormer.gen_edge_input(_lowercase, _lowercase, _lowercase     )
						snake_case_			:Tuple						=      np.zeros([num_nodes + 1, num_nodes + 1], dtype=np.single     )  # with graph token

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

						return item

class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Dict						,					snake_case:							Optional[int]=20						,					snake_case:							List[Any]=False						)       ->     Union[str, Any]:
											if not is_cython_available():
																	raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""						)

											snake_case_			:List[str]						=      spatial_pos_max
											snake_case_			:int						=      on_the_fly_processing






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

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

											snake_case_			:Dict						=      max(len(i["""input_nodes"""]						) for i in features						)
											snake_case_			:List[Any]						=      len(features[0]["""input_nodes"""][0]						)
											snake_case_			:Dict						=      len(features[0]["""attn_edge_type"""][0][0]						)
											snake_case_			:List[Any]						=      max(len(i["""input_edges"""][0][0]						) for i in features						)
											snake_case_			:Any						=      len(features[0]["""input_edges"""][0][0][0]						)
											snake_case_			:List[str]						=      len(snake_case						)

											snake_case_			:Union[str, Any]						=      torch.zeros(snake_case						,					max_node_num + 1						,					max_node_num + 1						,					dtype=torch.float						)
											snake_case_			:int						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Optional[Any]						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Tuple						=      torch.zeros(snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Dict						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Union[str, 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"]:
																							snake_case_			:List[str]						=      torch.tensor(f[k]						)

																	if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]						) > 0:
																							snake_case_			:Union[str, Any]						=      float("""-inf"""						)

																	snake_case_			:Optional[Any]						=      f["""attn_bias"""]
																	snake_case_			:int						=      f[
																	    """attn_edge_type"""
																	]
																	snake_case_			:Dict						=      f["""spatial_pos"""]
																	snake_case_			:Tuple						=      f["""in_degree"""]
																	snake_case_			:List[Any]						=      f["""input_nodes"""]
																	snake_case_			:List[Any]						=      f["""input_edges"""]

											snake_case_			:int						=      batch["""in_degree"""]

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

											return batch
