



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


UpperCAmelCase:  List[str]			     =				logging.getLogger(__name__)





class   UpperCamelCase	(     pl.Callback       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   {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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_=True   ):
			logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
			_lowercase				: 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
			_lowercase				: str     =   Path(pl_module.hparams.output_dir   )
			if type_path == "test":
					_lowercase				: str     =   od / """test_results.txt"""
					_lowercase				: Any     =   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.
					_lowercase				: str     =   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
					_lowercase				: Tuple     =   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
							_lowercase				: Optional[int]     =   metrics[key]
							if isinstance(UpperCAmelCase_				,torch.Tensor   ):
									_lowercase				: Tuple     =   val.item()
							_lowercase				: List[str]     =   f"""{key}: {val:.6f}\n"""
							writer.write(UpperCAmelCase_   )

			if not save_generations:
					return

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






	@rank_zero_only
	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			try:
					_lowercase				: Optional[Any]     =   pl_module.model.model.num_parameters()
			except AttributeError:
					_lowercase				: str     =   pl_module.model.num_parameters()

			_lowercase				: List[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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			save_json(pl_module.metrics				,pl_module.metrics_save_path   )
			return self._write_logs(UpperCAmelCase_				,UpperCAmelCase_				,"""test"""   )






	@rank_zero_only
	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			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 (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=1   ,				__UpperCAmelCase=False      ):
		if metric == "rouge2":
				_lowercase				: List[Any]     =   """{val_avg_rouge2:.4f}-{step_count}"""
		elif metric == "bleu":
				_lowercase				: Tuple     =   """{val_avg_bleu:.4f}-{step_count}"""
		elif metric == "loss":
				_lowercase				: 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."""      )

		_lowercase				: Union[str, Any]     =   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 (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""   ,				mode="""min""" if """loss""" in metric else """max"""   ,				patience=__UpperCAmelCase   ,				verbose=__UpperCAmelCase   ,				)









import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			if "model" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""model."""       ,						"""""" )
			if "norm1" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm1"""       ,						"""attention.output.LayerNorm""" )
			if "norm2" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm2"""       ,						"""output.LayerNorm""" )
			if "norm" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm"""       ,						"""LayerNorm""" )
			if "transformer" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.split(""".""" )[0].split("""_""" )[-1]
						SCREAMING_SNAKE_CASE     =       orig_key.replace(f"""transformer_{layer_num}"""       ,						f"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mha.attn"""       ,						"""attention.self""" )
			if "mha" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mha"""       ,						"""attention""" )
			if "W_q" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_q"""       ,						"""self.query""" )
			if "W_k" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_k"""       ,						"""self.key""" )
			if "W_v" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_v"""       ,						"""self.value""" )
			if "ff1" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff1"""       ,						"""intermediate.dense""" )
			if "ff2" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff2"""       ,						"""output.dense""" )
			if "ff" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff"""       ,						"""output.dense""" )
			if "mlm_class" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mlm.mlm_class"""       ,						"""cls.predictions.decoder""" )
			if "mlm" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mlm"""       ,						"""cls.predictions.transform""" )
			if "cls" not in orig_key:
						SCREAMING_SNAKE_CASE     =       """nystromformer.""" + orig_key

			return orig_key
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

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

			SCREAMING_SNAKE_CASE     =       orig_state_dict["""cls.predictions.decoder.bias"""]
			SCREAMING_SNAKE_CASE     =       (
			    torch.arange(config.max_position_embeddings ).expand((1, -1) ) + 2
			)

			return orig_state_dict
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.load(SCREAMING_SNAKE_CASE       ,						map_location="""cpu""" )["""model_state_dict"""]
			SCREAMING_SNAKE_CASE     =       NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       NystromformerForMaskedLM(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       convert_checkpoint_helper(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

			model.load_state_dict(SCREAMING_SNAKE_CASE )
			model.eval()
			model.save_pretrained(SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
		__A    :				List[str]    						=	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    :				Union[str, Any]    						=	parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






