






"""simple docstring"""



import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[str]							)->						Dict:






     '''simple docstring'''



     UpperCAmelCase__		:  Optional[int]				=				filter(lambda snake_case							: p.requires_grad       , model.parameters()							)
     UpperCAmelCase__		:  Union[str, Any]				=				sum([np.prod(p.size()							) for p in model_parameters]							)
     return params


_lowerCAmelCase     :     int            =	logging.getLogger(__name__)




class        lowerCAmelCase__       (  pl.Callback					):
   def 	__a				(						self	:     Dict				,       snake_case__	:     Optional[int]				,       snake_case__	:     Optional[int]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				{f'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
        pl_module.logger.log_metrics(_UpperCamelCase				)
   @rank_zero_only
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     pl.Trainer				,       snake_case__	:     pl.LightningModule				,       snake_case__	:     str				,       snake_case__	:     Dict=True				):


        '''simple docstring'''
        logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'				)
        UpperCAmelCase__		:  Optional[int]				=				trainer.callback_metrics
        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}				)
        # Log results
        UpperCAmelCase__		:  int				=				Path(pl_module.hparams.output_dir				)
        if type_path == "test":
             UpperCAmelCase__		:  str				=				od / "test_results.txt"
             UpperCAmelCase__		:  Optional[int]				=				od / "test_generations.txt"
        else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             UpperCAmelCase__		:  List[str]				=				od / f'{type_path}_results/{trainer.global_step:05d}.txt'
             UpperCAmelCase__		:  Dict				=				od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
             results_file.parent.mkdir(exist_ok=_UpperCamelCase				)
             generations_file.parent.mkdir(exist_ok=_UpperCamelCase				)
        with open(_UpperCamelCase				,       "a+"				) as writer:
             for key in sorted(_UpperCamelCase				):
                  if key in ["log", "progress_bar", "preds"]:
                       continue
                  UpperCAmelCase__		:  Optional[int]				=				metrics[key]
                  if isinstance(_UpperCamelCase				,       torch.Tensor				):
                       UpperCAmelCase__		:  int				=				val.item()
                  UpperCAmelCase__		:  Union[str, Any]				=				f'{key}: {val:.6f}\n'
                  writer.write(_UpperCamelCase				)

        if not save_generations:
             return

        if "preds" in metrics:
             UpperCAmelCase__		:  Tuple				=				"\n".join(metrics["preds"]				)
             generations_file.open("w+"				).write(_UpperCamelCase				)
   @rank_zero_only
   def 	__a				(						self	:     Any				,       snake_case__	:     str				,       snake_case__	:     Dict				):


        '''simple docstring'''

        try:
             UpperCAmelCase__		:  Union[str, Any]				=				pl_module.model.model.num_parameters()
        except AttributeError:
             UpperCAmelCase__		:  str				=				pl_module.model.num_parameters()

        UpperCAmelCase__		:  Any				=				count_trainable_parameters(_UpperCamelCase				)
        # mp stands for million parameters
        trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}				)
   @rank_zero_only
   def 	__a				(						self	:     str				,       snake_case__	:     pl.Trainer				,       snake_case__	:     pl.LightningModule				):


        '''simple docstring'''
        save_json(pl_module.metrics				,       pl_module.metrics_save_path				)
        return self._write_logs(_UpperCamelCase				,       _UpperCamelCase				,       "test"				)




   @rank_zero_only
   def 	__a				(						self	:     Optional[int]				,       snake_case__	:     pl.Trainer				,       snake_case__	:     List[str]				):


        '''simple docstring'''
        save_json(pl_module.metrics				,       pl_module.metrics_save_path				)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")




def      SCREAMING_SNAKE_CASE__    (   snake_case    :      int       , snake_case    :      Optional[int]       , snake_case    :      List[Any]=1       , snake_case    :      Any=False							)->						Union[str, Any]:






     '''simple docstring'''



     if metric == "rouge2":
          UpperCAmelCase__		:  Dict				=				"{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          UpperCAmelCase__		:  Optional[Any]				=				"{val_avg_bleu:.4f}-{step_count}"
     elif metric == "loss":
          UpperCAmelCase__		:  Union[str, Any]				=				"{val_avg_loss:.4f}-{step_count}"
     else:
          raise NotImplementedError(
              f'seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'
              " this function."							)

     UpperCAmelCase__		:  str				=				ModelCheckpoint(
         dirpath=UpperCAmelCase__       , filename=UpperCAmelCase__       , monitor=f'val_{metric}'       , mode="min" if "loss" in metric else "max"       , save_top_k=UpperCAmelCase__       , )
     return checkpoint_callback



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Dict       , snake_case    :      Optional[Any]							)->						Union[str, Any]:






     '''simple docstring'''



     return EarlyStopping(
         monitor=f'val_{metric}'       , mode="min" if "loss" in metric else "max"       , patience=UpperCAmelCase__       , verbose=UpperCAmelCase__       , )



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							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

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

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

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

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				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				=				torch.tensor(f[k]					)

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

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				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				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




