

'''simple docstring'''

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					if "model" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("model."			,   ""							)
					if "norm1" in orig_key:
										UpperCAmelCase		:							List[Any]        =       orig_key.replace("norm1"			,   "attention.output.LayerNorm"							)
					if "norm2" in orig_key:
										UpperCAmelCase		:							Dict        =       orig_key.replace("norm2"			,   "output.LayerNorm"							)
					if "norm" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("norm"			,   "LayerNorm"							)
					if "transformer" in orig_key:
										UpperCAmelCase		:							Dict        =       orig_key.split("."							)[0].split("_"							)[-1]
										UpperCAmelCase		:							str        =       orig_key.replace(F"transformer_{layer_num}"			,   F"encoder.layer.{layer_num}"							)
					if "mha.attn" in orig_key:
										UpperCAmelCase		:							Dict        =       orig_key.replace("mha.attn"			,   "attention.self"							)
					if "mha" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("mha"			,   "attention"							)
					if "W_q" in orig_key:
										UpperCAmelCase		:							List[Any]        =       orig_key.replace("W_q"			,   "self.query"							)
					if "W_k" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("W_k"			,   "self.key"							)
					if "W_v" in orig_key:
										UpperCAmelCase		:							List[Any]        =       orig_key.replace("W_v"			,   "self.value"							)
					if "ff1" in orig_key:
										UpperCAmelCase		:							str        =       orig_key.replace("ff1"			,   "intermediate.dense"							)
					if "ff2" in orig_key:
										UpperCAmelCase		:							Optional[Any]        =       orig_key.replace("ff2"			,   "output.dense"							)
					if "ff" in orig_key:
										UpperCAmelCase		:							Optional[Any]        =       orig_key.replace("ff"			,   "output.dense"							)
					if "mlm_class" in orig_key:
										UpperCAmelCase		:							List[str]        =       orig_key.replace("mlm.mlm_class"			,   "cls.predictions.decoder"							)
					if "mlm" in orig_key:
										UpperCAmelCase		:							Union[str, Any]        =       orig_key.replace("mlm"			,   "cls.predictions.transform"							)
					if "cls" not in orig_key:
										UpperCAmelCase		:							str        =       "nystromformer." + orig_key

					return orig_key





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					for key in orig_state_dict.copy().keys():
										UpperCAmelCase		:							Optional[Any]        =       orig_state_dict.pop(__magic_name__							)

										if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
															continue
										else:
															UpperCAmelCase		:							str        =       val

					UpperCAmelCase		:							int        =       orig_state_dict["cls.predictions.decoder.bias"]
					UpperCAmelCase		:							List[str]        =       (
					    torch.arange(config.max_position_embeddings							).expand((1, -1)							) + 2
					)

					return orig_state_dict





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[str]        =       torch.load(__magic_name__			,   map_location="cpu"							)["model_state_dict"]
					UpperCAmelCase		:							Dict        =       NystromformerConfig.from_json_file(__magic_name__							)
					UpperCAmelCase		:							Dict        =       NystromformerForMaskedLM(__magic_name__							)

					UpperCAmelCase		:							Union[str, Any]        =       convert_checkpoint_helper(__magic_name__			,   __magic_name__							)

					model.load_state_dict(__magic_name__							)
					model.eval()
					model.save_pretrained(__magic_name__							)

					print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"							)


if __name__ == "__main__":
				a       :      List[Any]  	=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for Nystromformer model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				a       :      Tuple  	=			parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



'''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 				lowercase (					__magic_name__							):



					'''simple docstring'''







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


a       :      int  	=			logging.getLogger(__name__)







class   UpperCamelCase__   (       pl.Callback       ):


		"""simple docstring"""

		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
							pl_module.logger.log_metrics(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case=True      ):


							'''simple docstring'''

							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							UpperCAmelCase		:							Optional[Any]        =       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		:							List[str]        =       Path(pl_module.hparams.output_dir      )
							if type_path == "test":
												UpperCAmelCase		:							Dict        =       od / "test_results.txt"
												UpperCAmelCase		:							Dict        =       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		:							Union[str, Any]        =       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
												UpperCAmelCase		:							int        =       od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
												results_file.parent.mkdir(exist_ok=snake_case      )
												generations_file.parent.mkdir(exist_ok=snake_case      )
							with open(snake_case   ,      "a+"      ) as writer:
												for key in sorted(snake_case      ):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	UpperCAmelCase		:							Optional[Any]        =       metrics[key]
																	if isinstance(snake_case   ,      torch.Tensor      ):
																						UpperCAmelCase		:							Union[str, Any]        =       val.item()
																	UpperCAmelCase		:							List[str]        =       f"{key}: {val:.6f}\n"
																	writer.write(snake_case      )

							if not save_generations:
												return

							if "preds" in metrics:
												UpperCAmelCase		:							List[str]        =       "\n".join(metrics["preds"]      )
												generations_file.open("w+"      ).write(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							try:
												UpperCAmelCase		:							Tuple        =       pl_module.model.model.num_parameters()
							except AttributeError:
												UpperCAmelCase		:							int        =       pl_module.model.num_parameters()

							UpperCAmelCase		:							List[Any]        =       count_trainable_parameters(snake_case      )
							# 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   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

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







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__=1			,   __magic_name__=False							):



					'''simple docstring'''







					if metric == "rouge2":
										UpperCAmelCase		:							Dict        =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										UpperCAmelCase		:							int        =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "loss":
										UpperCAmelCase		:							Tuple        =       "{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		:							int        =       ModelCheckpoint(
					    dirpath=__magic_name__			,   filename=__magic_name__			,   monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   save_top_k=__magic_name__			,   )
					return checkpoint_callback





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







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