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_rag import save_json
def  snake_case						(__lowercase						)	->	Optional[int]:

						'''simple docstring'''



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


__SCREAMING_SNAKE_CASE						:   Tuple         =			logging.getLogger(__name__)
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						if metric == "rouge2":
												_snake_case						:					int    =   "{val_avg_rouge2:.4f}-{step_count}"
						elif metric == "bleu":
												_snake_case						:					Dict    =   "{val_avg_bleu:.4f}-{step_count}"
						elif metric == "em":
												_snake_case						:					Optional[int]    =   "{val_avg_em:.4f}-{step_count}"
						else:
												raise NotImplementedError(
												    F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
												    " function."						)

						_snake_case						:					Union[str, Any]    =   ModelCheckpoint(
						    dirpath=__lowercase ,      filename=__lowercase ,      monitor=F"""val_{metric}""" ,      mode="max" ,      save_top_k=3 ,      every_n_epochs=1 ,      )
						return checkpoint_callback
def  snake_case						(__lowercase ,      __lowercase						)	->	List[Any]:

						'''simple docstring'''



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



class        lowercase_   (					pl.Callback		):



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[Any]    =   {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
													pl_module.logger.log_metrics(lowercase_    )



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

													if not save_generations:
																			return

													if "preds" in metrics:
																			_snake_case						:					Union[str, Any]    =   "\n".join(metrics["preds"]    )
																			generations_file.open("w+"    ).write(lowercase_    )



							@rank_zero_only
							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													try:
																			_snake_case						:					Tuple    =   pl_module.model.model.num_parameters()
													except AttributeError:
																			_snake_case						:					List[str]    =   pl_module.model.num_parameters()

													_snake_case						:					Union[str, Any]    =   count_trainable_parameters(lowercase_    )
													# 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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													save_json(pl_module.metrics				,    pl_module.metrics_save_path    )
													return self._write_logs(lowercase_				,    lowercase_				,    "test"    )



							@rank_zero_only
							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													save_json(pl_module.metrics				,    pl_module.metrics_save_path    )
													# Uncommenting this will save val generations
													# return self._write_logs(trainer, pl_module, "valid")
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

						print(F"""Load fine-pruned model from {model_name_or_path}"""						)
						_snake_case						:					List[Any]    =   torch.load(os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						_snake_case						:					List[str]    =   {}

						for name, tensor in model.items():
												if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																		_snake_case						:					Tuple    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "classifier" in name or "qa_output" in name:
																		_snake_case						:					Optional[int]    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "bias" in name:
																		_snake_case						:					List[Any]    =   tensor
																		print(F"""Copied layer {name}"""						)
												else:
																		if pruning_method == "magnitude":
																								_snake_case						:					Tuple    =   MagnitudeBinarizer.apply(inputs=__lowercase ,      threshold=__lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "topK":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					Optional[Any]    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					Tuple    =   TopKBinarizer.apply(__lowercase ,      __lowercase						)
																								_snake_case						:					Optional[Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "sigmoied_threshold":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					List[Any]    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					List[str]    =   ThresholdBinarizer.apply(__lowercase ,      __lowercase ,      __lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "l0":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case    ,_snake_case						:					Union[str, Any]    =   -0.1, 1.1
																								_snake_case						:					Dict    =   torch.sigmoid(__lowercase						)
																								_snake_case						:					List[str]    =   s * (r - l) + l
																								_snake_case						:					Tuple    =   s_bar.clamp(min=0.0 ,      max=1.0						)
																								_snake_case						:					Union[str, Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		else:
																								raise ValueError("Unknown pruning method"						)

						if target_model_path is None:
												_snake_case						:					Any    =   os.path.join(
												    os.path.dirname(__lowercase						) ,      F"""bertarized_{os.path.basename(__lowercase						)}"""						)

						if not os.path.isdir(__lowercase						):
												shutil.copytree(__lowercase ,      __lowercase						)
												print(F"""\nCreated folder {target_model_path}"""						)

						torch.save(__lowercase ,      os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			argparse.ArgumentParser()

				parser.add_argument(
				    '--pruning_method',
				    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
				    type=str,
				    required=True,
				    help=(
				        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
				        ' sigmoied_threshold = Soft movement pruning)'
				    ),
				)
				parser.add_argument(
				    '--threshold',
				    type=float,
				    required=False,
				    help=(
				        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
				        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
				        'Not needed for `l0`'
				    ),
				)
				parser.add_argument(
				    '--model_name_or_path',
				    type=str,
				    required=True,
				    help='Folder containing the model that was previously fine-pruned',
				)
				parser.add_argument(
				    '--target_model_path',
				    default=None,
				    type=str,
				    required=False,
				    help='Folder containing the model that was previously fine-pruned',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)