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 		__lowerCamelCase							(UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				filter(lambda UpperCAmelCase__     : p.requires_grad					,       model.parameters()     )
					SCREAMING_SNAKE_CASE				=				sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


_lowerCamelCase    :      int	 =						logging.getLogger(__name__)


class   lowercase			(   pl.Callback   ):




							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Optional[int]					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				{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 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    pl.Trainer		,							_UpperCamelCase      :    pl.LightningModule		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Dict=True					)    -> None:



												'''simple docstring'''

												logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
												SCREAMING_SNAKE_CASE				=				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
												SCREAMING_SNAKE_CASE				=				Path(pl_module.hparams.output_dir					)
												if type_path == "test":
																	SCREAMING_SNAKE_CASE				=				od / "test_results.txt"
																	SCREAMING_SNAKE_CASE				=				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.
																	SCREAMING_SNAKE_CASE				=				od / F"{type_path}_results/{trainer.global_step:05d}.txt"
																	SCREAMING_SNAKE_CASE				=				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
																						SCREAMING_SNAKE_CASE				=				metrics[key]
																						if isinstance(_UpperCamelCase		,							torch.Tensor					):
																											SCREAMING_SNAKE_CASE				=				val.item()
																						SCREAMING_SNAKE_CASE				=				F"{key}: {val:.6f}\n"
																						writer.write(_UpperCamelCase					)

												if not save_generations:
																	return

												if "preds" in metrics:
																	SCREAMING_SNAKE_CASE				=				"\n".join(metrics["preds"]					)
																	generations_file.open("w+"					).write(_UpperCamelCase					)




							@rank_zero_only
							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Dict					)    -> List[str]:



												'''simple docstring'''

												try:
																	SCREAMING_SNAKE_CASE				=				pl_module.model.model.num_parameters()
												except AttributeError:
																	SCREAMING_SNAKE_CASE				=				pl_module.model.num_parameters()

												SCREAMING_SNAKE_CASE				=				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 		__snake_case(  self      :    str		,							_UpperCamelCase      :    pl.Trainer		,							_UpperCamelCase      :    pl.LightningModule					)    -> Dict:



												'''simple docstring'''

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





							@rank_zero_only
							def 		__snake_case(  self      :    Optional[int]		,							_UpperCamelCase      :    pl.Trainer		,							_UpperCamelCase      :    List[str]					)    -> Dict:



												'''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 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							List[Any]=1					,       UpperCAmelCase__   :							Any=False     ):
					if metric == "rouge2":
										SCREAMING_SNAKE_CASE				=				"{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										SCREAMING_SNAKE_CASE				=				"{val_avg_bleu:.4f}-{step_count}"
					elif metric == "loss":
										SCREAMING_SNAKE_CASE				=				"{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."     )

					SCREAMING_SNAKE_CASE				=				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 		__lowerCamelCase							(UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							Optional[Any]     ):
					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




