




"""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 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


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


_lowercase		 =      logging.getLogger(__name__)

class  __lowercase    (     pl.Callback		):


				'''simple docstring'''


				def  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ):
										snake_case__    :   Tuple							     =   {F'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
										pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_ )

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

										if not save_generations:
																return

										if "preds" in metrics:
																snake_case__    :   Optional[Any]							     =   '''\n'''.join(metrics['''preds'''] )
																generations_file.open('''w+''' ).write(SCREAMING_SNAKE_CASE_ )

				@rank_zero_only
				def  _lowerCamelCase   (				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       int ):
										try:
																snake_case__    :   Dict							     =   pl_module.model.model.num_parameters()
										except AttributeError:
																snake_case__    :   Tuple							     =   pl_module.model.num_parameters()

										snake_case__    :   List[Any]							     =   count_trainable_parameters(SCREAMING_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  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       pl.Trainer ,SCREAMING_SNAKE_CASE_				:       pl.LightningModule ):
										save_json(pl_module.metrics ,pl_module.metrics_save_path )
										return self._write_logs(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,'''test''' )





				@rank_zero_only
				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       pl.Trainer ,SCREAMING_SNAKE_CASE_				:       Optional[int] ):
										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							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase=1 ,     lowerCamelCase=False						):
						'''simple docstring'''


						if metric == "rouge2":
												snake_case__    :   Dict							     =   '''{val_avg_rouge2:.4f}-{step_count}'''
						elif metric == "bleu":
												snake_case__    :   Any							     =   '''{val_avg_bleu:.4f}-{step_count}'''
						elif metric == "loss":
												snake_case__    :   List[str]							     =   '''{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.'''						)

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

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


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








"""simple docstring"""





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						if "model" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''model.''' ,     ''''''						)
						if "norm1" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''norm1''' ,     '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''norm2''' ,     '''output.LayerNorm'''						)
						if "norm" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''norm''' ,     '''LayerNorm'''						)
						if "transformer" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												snake_case__    :   Optional[Any]							     =   orig_key.replace(f'''transformer_{layer_num}''' ,     f'''encoder.layer.{layer_num}'''						)
						if "mha.attn" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''mha.attn''' ,     '''attention.self'''						)
						if "mha" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''mha''' ,     '''attention'''						)
						if "W_q" in orig_key:
												snake_case__    :   Optional[int]							     =   orig_key.replace('''W_q''' ,     '''self.query'''						)
						if "W_k" in orig_key:
												snake_case__    :   Tuple							     =   orig_key.replace('''W_k''' ,     '''self.key'''						)
						if "W_v" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''W_v''' ,     '''self.value'''						)
						if "ff.0" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''ff.0''' ,     '''intermediate.dense'''						)
						if "ff.2" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''ff.2''' ,     '''output.dense'''						)
						if "ff" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''ff''' ,     '''output.dense'''						)
						if "mlm_class" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''mlm.mlm_class''' ,     '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												snake_case__    :   List[Any]							     =   orig_key.replace('''mlm''' ,     '''cls.predictions.transform'''						)
						if "backbone.backbone.encoders" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.replace('''backbone.backbone.encoders''' ,     '''encoder.layer'''						)
						if "cls" not in orig_key:
												snake_case__    :   Union[str, Any]							     =   '''mra.''' + orig_key

						return orig_key

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case__    :   int							     =   orig_state_dict.pop(lowerCamelCase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		snake_case__    :   Optional[int]							     =   val

						snake_case__    :   Union[str, Any]							     =   orig_state_dict['''cls.predictions.decoder.bias''']
						snake_case__    :   Any							     =   torch.arange(lowerCamelCase						).expand((1, -1)						) + 2

						return orig_state_dict

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   torch.load(lowerCamelCase ,     map_location='''cpu'''						)['''model_state_dict''']
						snake_case__    :   Optional[Any]							     =   MraConfig.from_json_file(lowerCamelCase						)
						snake_case__    :   List[Any]							     =   MraForMaskedLM(lowerCamelCase						)

						snake_case__    :   List[Any]							     =   convert_checkpoint_helper(config.max_position_embeddings ,     lowerCamelCase						)

						print(model.load_state_dict(lowerCamelCase						)						)
						model.eval()
						model.save_pretrained(lowerCamelCase						)

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


if __name__ == "__main__":
							_lowercase		 =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The json file for Mra model config.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							_lowercase		 =      parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


