"""simple docstring"""




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__SCREAMING_SNAKE_CASE						:   int         =			1_6
__SCREAMING_SNAKE_CASE						:   int         =			3_2
def  snake_case						(__lowercase ,      __lowercase = 16						)	->	Any:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   AutoTokenizer.from_pretrained("bert-base-cased"						)
						_snake_case						:					Dict    =   load_dataset("glue" ,      "mrpc"						)

						def tokenize_function(__lowercase						):
												# max_length=None => use the model max length (it's actually the default)
												_snake_case						:					Dict    =   tokenizer(examples["sentence1"] ,      examples["sentence2"] ,      truncation=a__ ,      max_length=a__						)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												_snake_case						:					Dict    =   datasets.map(
												    a__ ,      batched=a__ ,      remove_columns=["idx", "sentence1", "sentence2"] ,      )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						_snake_case						:					Dict    =   tokenized_datasets.rename_column("label" ,      "labels"						)

						def collate_fn(__lowercase						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_snake_case						:					int    =   128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		_snake_case						:					Dict    =   16
												elif accelerator.mixed_precision != "no":
																		_snake_case						:					str    =   8
												else:
																		_snake_case						:					Any    =   None

												return tokenizer.pad(
												    a__ ,      padding="longest" ,      max_length=a__ ,      pad_to_multiple_of=a__ ,      return_tensors="pt" ,      )

						# Instantiate dataloaders.
						_snake_case						:					List[str]    =   DataLoader(
						    tokenized_datasets["train"] ,      shuffle=a__ ,      collate_fn=a__ ,      batch_size=a__						)
						_snake_case						:					str    =   DataLoader(
						    tokenized_datasets["validation"] ,      shuffle=a__ ,      collate_fn=a__ ,      batch_size=a__						)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				__SCREAMING_SNAKE_CASE						:   Tuple         =			mocked_dataloaders  # noqa: F811
def  snake_case						(__lowercase ,      __lowercase						)	->	List[str]:

						'''simple docstring'''



						if os.environ.get("TESTING_MOCKED_DATALOADERS" ,      a__						) == "1":
												_snake_case						:					Any    =   2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												_snake_case						:					int    =   Accelerator(
												    cpu=args.cpu ,      mixed_precision=args.mixed_precision ,      log_with="all" ,      project_dir=args.project_dir						)
						else:
												_snake_case						:					List[Any]    =   Accelerator(cpu=args.cpu ,      mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_snake_case						:					Any    =   config["lr"]
						_snake_case						:					List[Any]    =   int(config["num_epochs"]						)
						_snake_case						:					str    =   int(config["seed"]						)
						_snake_case						:					int    =   int(config["batch_size"]						)
						set_seed(a__						)

						_snake_case    ,_snake_case						:					int    =   get_dataloaders(a__ ,      a__						)
						_snake_case						:					str    =   evaluate.load("glue" ,      "mrpc"						)

						# If the batch size is too big we use gradient accumulation
						_snake_case						:					int    =   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												_snake_case						:					Tuple    =   batch_size // MAX_GPU_BATCH_SIZE
												_snake_case						:					List[Any]    =   MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_snake_case						:					List[Any]    =   AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,      return_dict=a__						)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						_snake_case						:					Tuple    =   model.to(accelerator.device						)

						# Instantiate optimizer
						_snake_case						:					Union[str, Any]    =   AdamW(params=model.parameters() ,      lr=a__						)

						# Instantiate scheduler
						_snake_case						:					int    =   get_linear_schedule_with_warmup(
						    optimizer=a__ ,      num_warmup_steps=100 ,      num_training_steps=(len(a__						) * num_epochs) // gradient_accumulation_steps ,      )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case						:					int    =   accelerator.prepare(
						    a__ ,      a__ ,      a__ ,      a__ ,      a__						)

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												_snake_case						:					Optional[Any]    =   os.path.split(a__						)[-1].split("."						)[0]
												accelerator.init_trackers(a__ ,      a__						)

						# Now we train the model
						for epoch in range(a__						):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		_snake_case						:					str    =   0
												for step, batch in enumerate(a__						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		_snake_case						:					str    =   model(**a__						)
																		_snake_case						:					Dict    =   outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		_snake_case						:					List[str]    =   loss / gradient_accumulation_steps
																		accelerator.backward(a__						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(a__						):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device						)
																		with torch.no_grad():
																								_snake_case						:					Optional[int]    =   model(**a__						)
																		_snake_case						:					Optional[Any]    =   outputs.logits.argmax(dim=-1						)
																		_snake_case    ,_snake_case						:					Dict    =   accelerator.gather_for_metrics((predictions, batch["labels"])						)
																		metric.add_batch(
																		    predictions=a__ ,      references=a__ ,      )

												_snake_case						:					str    =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:""" ,      a__						)

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        "accuracy": eval_metric["accuracy"],
																		        "f1": eval_metric["f1"],
																		        "train_loss": total_loss.item() / len(a__						),
																		        "epoch": epoch,
																		    } ,      step=a__ ,      )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()
def  snake_case						()	->	int:

						'''simple docstring'''



						_snake_case						:					List[Any]    =   argparse.ArgumentParser(description="Simple example of training script."						)
						parser.add_argument(
						    "--mixed_precision" ,      type=a__ ,      default=a__ ,      choices=["no", "fp16", "bf16", "fp8"] ,      help="Whether to use mixed precision. Choose"
						    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
						    "and an Nvidia Ampere GPU." ,      )
						parser.add_argument("--cpu" ,      action="store_true" ,      help="If passed, will train on the CPU."						)
						parser.add_argument(
						    "--with_tracking" ,      action="store_true" ,      help="Whether to load in all available experiment trackers from the environment and use them for logging." ,      )
						parser.add_argument(
						    "--project_dir" ,      type=a__ ,      default="logs" ,      help="Location on where to store experiment tracking logs` and relevent project information" ,      )
						_snake_case						:					Any    =   parser.parse_args()
						_snake_case						:					Optional[Any]    =   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(a__ ,      a__						)


if __name__ == "__main__":
				main()
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)