
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


SCREAMING_SNAKE_CASE_			      =					logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

SCREAMING_SNAKE_CASE_			      =					{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
SCREAMING_SNAKE_CASE_			      =					{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
SCREAMING_SNAKE_CASE_			      =					sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE_			      =					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class      lowerCAmelCase_						(							pl.LightningModule				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=None  ,							snake_case_="base"  ,							snake_case_=None  ,							snake_case_=None  ,							snake_case_=None  ,							**snake_case_  ,							)   ->       Dict:
													super().__init__()
													# TODO: move to self.save_hyperparameters()
													# self.save_hyperparameters()
													# can also expand arguments into trainer signature for easier reading

													self.save_hyperparameters(__SCREAMING_SNAKE_CASE							)
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       Path(self.hparams.output_dir							)
													__lowerCAmelCase        =       self.hparams.cache_dir if self.hparams.cache_dir else None
													if config is None:
																			__lowerCAmelCase        =       AutoConfig.from_pretrained(
																			    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path  ,							**({"""num_labels""": num_labels} if num_labels is not None else {})  ,							cache_dir=__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_SNAKE_CASE  ,							)
													else:
																			__lowerCAmelCase        =       config

													__lowerCAmelCase        =       ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
													for p in extra_model_params:
																			if getattr(self.hparams  ,							__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE							):
																									assert hasattr(self.config  ,							__SCREAMING_SNAKE_CASE							), f"""model config doesn't have a `{p}` attribute"""
																									setattr(self.config  ,							__SCREAMING_SNAKE_CASE  ,							getattr(self.hparams  ,							__SCREAMING_SNAKE_CASE							)							)

													if tokenizer is None:
																			__lowerCAmelCase        =       AutoTokenizer.from_pretrained(
																			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,							cache_dir=__SCREAMING_SNAKE_CASE  ,							)
													else:
																			__lowerCAmelCase        =       tokenizer
													__lowerCAmelCase        =       MODEL_MODES[mode]
													if model is None:
																			__lowerCAmelCase        =       self.model_type.from_pretrained(
																			    self.hparams.model_name_or_path  ,							from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path							)  ,							config=self.config  ,							cache_dir=__SCREAMING_SNAKE_CASE  ,							)
													else:
																			__lowerCAmelCase        =       model




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       Any:
													__lowerCAmelCase        =       self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_SNAKE_CASE							)




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase        =       arg_to_scheduler[self.hparams.lr_scheduler]
													__lowerCAmelCase        =       get_schedule_func(
													    self.opt  ,							num_warmup_steps=self.hparams.warmup_steps  ,							num_training_steps=self.total_steps()							)
													__lowerCAmelCase        =       {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
													return scheduler




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       self.model
													__lowerCAmelCase        =       ["""bias""", """LayerNorm.weight"""]
													__lowerCAmelCase        =       [
													    {
													        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)],
													        """weight_decay""": self.hparams.weight_decay,
													    },
													    {
													        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)],
													        """weight_decay""": 0.0,
													    },
													]
													if self.hparams.adafactor:
																			__lowerCAmelCase        =       Adafactor(
																			    __SCREAMING_SNAKE_CASE  ,							lr=self.hparams.learning_rate  ,							scale_parameter=__SCREAMING_SNAKE_CASE  ,							relative_step=__SCREAMING_SNAKE_CASE							)

													else:
																			__lowerCAmelCase        =       AdamW(
																			    __SCREAMING_SNAKE_CASE  ,							lr=self.hparams.learning_rate  ,							eps=self.hparams.adam_epsilon							)
													__lowerCAmelCase        =       optimizer

													__lowerCAmelCase        =       self.get_lr_scheduler()

													return [optimizer], [scheduler]




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       int:
													return self.validation_step(__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE							)




							def  A__		(				self  ,							snake_case_							)   ->       List[Any]:
													return self.validation_end(__SCREAMING_SNAKE_CASE							)




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       max(1  ,							self.hparams.gpus							)  # TODO: consider num_tpu_cores
													__lowerCAmelCase        =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
													return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




							def  A__		(				self  ,							snake_case_							)   ->       Optional[int]:
													if mode == "test":
																			__lowerCAmelCase        =       len(self.test_dataloader().dataset							)
													else:
																			__lowerCAmelCase        =       self.get_dataloader("""train"""  ,							self.hparams.train_batch_size  ,							shuffle=__SCREAMING_SNAKE_CASE							)
																			__lowerCAmelCase        =       len(self.train_dataloader().dataset							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_ = False							)   ->       Optional[Any]:
													raise NotImplementedError("""You must implement this for your task"""							)




							def  A__		(				self							)   ->       Any:
													return self.train_loader




							def  A__		(				self							)   ->       Optional[Any]:
													return self.get_dataloader("""dev"""  ,							self.hparams.eval_batch_size  ,							shuffle=__SCREAMING_SNAKE_CASE							)




							def  A__		(				self							)   ->       Optional[int]:
													return self.get_dataloader("""test"""  ,							self.hparams.eval_batch_size  ,							shuffle=__SCREAMING_SNAKE_CASE							)




							def  A__		(				self  ,							snake_case_							)   ->       Optional[int]:
													return os.path.join(
													    self.hparams.data_dir  ,							"""cached_{}_{}_{}""".format(
													        __SCREAMING_SNAKE_CASE  ,							list(filter(__SCREAMING_SNAKE_CASE  ,							self.hparams.model_name_or_path.split("""/"""							)							)							).pop()  ,							str(self.hparams.max_seq_length							)  ,							)  ,							)




							@pl.utilities.rank_zero_only
							def  A__		(				self  ,							snake_case_							)   ->       None:
													__lowerCAmelCase        =       self.output_dir.joinpath("""best_tfmr"""							)
													__lowerCAmelCase        =       self.step_count
													self.model.save_pretrained(__SCREAMING_SNAKE_CASE							)
													self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE							)




							@staticmethod
							def  A__		(				snake_case_  ,							snake_case_							)   ->       Optional[int]:
													parser.add_argument(
													    """--model_name_or_path"""  ,							default=__SCREAMING_SNAKE_CASE  ,							type=__SCREAMING_SNAKE_CASE  ,							required=__SCREAMING_SNAKE_CASE  ,							help="""Path to pretrained model or model identifier from huggingface.co/models"""  ,							)
													parser.add_argument(
													    """--config_name"""  ,							default=""""""  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Pretrained config name or path if not the same as model_name"""							)
													parser.add_argument(
													    """--tokenizer_name"""  ,							default=__SCREAMING_SNAKE_CASE  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Pretrained tokenizer name or path if not the same as model_name"""  ,							)
													parser.add_argument(
													    """--cache_dir"""  ,							default=""""""  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""  ,							)
													parser.add_argument(
													    """--encoder_layerdrop"""  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Encoder layer dropout probability (Optional). Goes into model.config"""  ,							)
													parser.add_argument(
													    """--decoder_layerdrop"""  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Decoder layer dropout probability (Optional). Goes into model.config"""  ,							)
													parser.add_argument(
													    """--dropout"""  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Dropout probability (Optional). Goes into model.config"""  ,							)
													parser.add_argument(
													    """--attention_dropout"""  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Attention dropout probability (Optional). Goes into model.config"""  ,							)
													parser.add_argument("""--learning_rate"""  ,							default=5e-5  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""The initial learning rate for Adam."""							)
													parser.add_argument(
													    """--lr_scheduler"""  ,							default="""linear"""  ,							choices=__SCREAMING_SNAKE_CASE  ,							metavar=__SCREAMING_SNAKE_CASE  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Learning rate scheduler"""  ,							)
													parser.add_argument("""--weight_decay"""  ,							default=0.0  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Weight decay if we apply some."""							)
													parser.add_argument("""--adam_epsilon"""  ,							default=1e-8  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Epsilon for Adam optimizer."""							)
													parser.add_argument("""--warmup_steps"""  ,							default=0  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""Linear warmup over warmup_steps."""							)
													parser.add_argument("""--num_workers"""  ,							default=4  ,							type=__SCREAMING_SNAKE_CASE  ,							help="""kwarg passed to DataLoader"""							)
													parser.add_argument("""--num_train_epochs"""  ,							dest="""max_epochs"""  ,							default=3  ,							type=__SCREAMING_SNAKE_CASE							)
													parser.add_argument("""--train_batch_size"""  ,							default=32  ,							type=__SCREAMING_SNAKE_CASE							)
													parser.add_argument("""--eval_batch_size"""  ,							default=32  ,							type=__SCREAMING_SNAKE_CASE							)
													parser.add_argument("""--adafactor"""  ,							action="""store_true"""							)
class      lowerCAmelCase_						(							pl.Callback				):



							'''simple docstring'''





							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Any:
													__lowerCAmelCase        =       trainer.lr_schedulers[0]["""scheduler"""]
													__lowerCAmelCase        =       {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
													pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       List[Any]:
													rank_zero_info("""***** Validation results *****"""							)
													__lowerCAmelCase        =       trainer.callback_metrics
													# Log results
													for key in sorted(__SCREAMING_SNAKE_CASE							):
																			if key not in ["log", "progress_bar"]:
																									rank_zero_info("""{} = {}\n""".format(__SCREAMING_SNAKE_CASE  ,							str(metrics[key]							)							)							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Optional[int]:
													rank_zero_info("""***** Test results *****"""							)
													__lowerCAmelCase        =       trainer.callback_metrics
													# Log and save results to file
													__lowerCAmelCase        =       os.path.join(pl_module.hparams.output_dir  ,							"""test_results.txt"""							)
													with open(__SCREAMING_SNAKE_CASE  ,							"""w"""							) as writer:
																			for key in sorted(__SCREAMING_SNAKE_CASE							):
																									if key not in ["log", "progress_bar"]:
																															rank_zero_info("""{} = {}\n""".format(__SCREAMING_SNAKE_CASE  ,							str(metrics[key]							)							)							)
																															writer.write("""{} = {}\n""".format(__SCREAMING_SNAKE_CASE  ,							str(metrics[key]							)							)							)

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    """--output_dir"""				,  default=snake_case__				,  type=snake_case__				,  required=snake_case__				,  help="""The output directory where the model predictions and checkpoints will be written."""				,  )
						parser.add_argument(
						    """--fp16"""				,  action="""store_true"""				,  help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""				,  )

						parser.add_argument(
						    """--fp16_opt_level"""				,  type=snake_case__				,  default="""O2"""				,  help=(
						        """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
						        """See details at https://nvidia.github.io/apex/amp.html"""
						    )				,  )
						parser.add_argument("""--n_tpu_cores"""				,  dest="""tpu_cores"""				,  type=snake_case__		)
						parser.add_argument("""--max_grad_norm"""				,  dest="""gradient_clip_val"""				,  default=1.0				,  type=snake_case__				,  help="""Max gradient norm"""		)
						parser.add_argument("""--do_train"""				,  action="""store_true"""				,  help="""Whether to run training."""		)
						parser.add_argument("""--do_predict"""				,  action="""store_true"""				,  help="""Whether to run predictions on the test set."""		)
						parser.add_argument(
						    """--gradient_accumulation_steps"""				,  dest="""accumulate_grad_batches"""				,  type=snake_case__				,  default=1				,  help="""Number of updates steps to accumulate before performing a backward/update pass."""				,  )
						parser.add_argument("""--seed"""				,  type=snake_case__				,  default=42				,  help="""random seed for initialization"""		)
						parser.add_argument(
						    """--data_dir"""				,  default=snake_case__				,  type=snake_case__				,  required=snake_case__				,  help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""				,  )



def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase=None				,  _lowerCAmelCase=True				,  _lowerCAmelCase=[]				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  **_lowerCAmelCase				,  ):
						pl.seed_everything(args.seed		)

						# init model
						__lowerCAmelCase        =       Path(model.hparams.output_dir		)
						odir.mkdir(exist_ok=snake_case__		)

						# add custom checkpoints
						if checkpoint_callback is None:
												__lowerCAmelCase        =       pl.callbacks.ModelCheckpoint(
												    filepath=args.output_dir				,  prefix="""checkpoint"""				,  monitor="""val_loss"""				,  mode="""min"""				,  save_top_k=1		)
						if early_stopping_callback:
												extra_callbacks.append(snake_case__		)
						if logging_callback is None:
												__lowerCAmelCase        =       LoggingCallback()

						__lowerCAmelCase        =       {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												__lowerCAmelCase        =       16
												__lowerCAmelCase        =       args.fpaa_opt_level

						if args.gpus > 1:
												__lowerCAmelCase        =       """ddp"""

						__lowerCAmelCase        =       args.accumulate_grad_batches
						__lowerCAmelCase        =       extra_train_kwargs.get("""accelerator"""				,  snake_case__		)
						__lowerCAmelCase        =       extra_train_kwargs.get("""profiler"""				,  snake_case__		)

						__lowerCAmelCase        =       pl.Trainer.from_argparse_args(
						    snake_case__				,  weights_summary=snake_case__				,  callbacks=[logging_callback] + extra_callbacks				,  logger=snake_case__				,  checkpoint_callback=snake_case__				,  **snake_case__				,  )

						if args.do_train:
												trainer.fit(snake_case__		)

						return trainer


"""simple docstring"""
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """Remaining Weights %"""				,  """Remaining Weight"""		)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
			        ''' pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `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(
			    '''--serialization_dir''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
