


import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


_snake_case  :	int			      =       logging.getLogger(__name__)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							# save results
							if os.path.exists(__lowerCamelCase			):
														if os.path.exists(os.path.join(__lowerCamelCase							,			"config.json"			)			) and os.path.isfile(
														    os.path.join(__lowerCamelCase							,			"config.json"			)			):
																					os.remove(os.path.join(__lowerCamelCase							,			"config.json"			)			)
														if os.path.exists(os.path.join(__lowerCamelCase							,			"pytorch_model.bin"			)			) and os.path.isfile(
														    os.path.join(__lowerCamelCase							,			"pytorch_model.bin"			)			):
																					os.remove(os.path.join(__lowerCamelCase							,			"pytorch_model.bin"			)			)
							else:
														os.makedirs(__lowerCamelCase			)
							model.save_pretrained(__lowerCamelCase			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case	:    List[Any]											=     2
							if unlogit:
														__snake_case	:    Union[str, Any]											=     torch.pow(__lowerCamelCase							,			__lowerCamelCase			)
							__snake_case	:    str											=     p * torch.log(__lowerCamelCase			)
							__snake_case	:    List[Any]											=     0
							return -plogp.sum(dim=-1			)







def        lowerCAmelCase_  (      __lowerCamelCase			):
							logger.info("lv, h >\t" + "\t".join(F'{x + 1}' for x in range(len(__lowerCamelCase			)			)			)			)
							for row in range(len(__lowerCamelCase			)			):
														if tensor.dtype != torch.long:
																					logger.info(F'layer {row + 1}:\t' + "\t".join(F'{x:.5f}' for x in tensor[row].cpu().data			)			)
														else:
																					logger.info(F'layer {row + 1}:\t' + "\t".join(F'{x:d}' for x in tensor[row].cpu().data			)			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=True							,			__lowerCamelCase=True							,			__lowerCamelCase=None							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[Any]											=     model.config.num_hidden_layers, model.config.num_attention_heads
							__snake_case	:    Optional[Any]											=     torch.zeros(__lowerCamelCase							,			__lowerCamelCase			).to(args.device			)
							__snake_case	:    Union[str, Any]											=     torch.zeros(__lowerCamelCase							,			__lowerCamelCase			).to(args.device			)

							if head_mask is None:
														__snake_case	:    List[str]											=     torch.ones(__lowerCamelCase							,			__lowerCamelCase			).to(args.device			)

							head_mask.requires_grad_(requires_grad=__lowerCamelCase			)
							# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
							if actually_pruned:
														__snake_case	:    Optional[int]											=     None

							__snake_case	:    Optional[Any]											=     0.0
							__snake_case	:    Tuple											=     0.0
							for step, inputs in enumerate(tqdm(__lowerCamelCase							,			desc="Iteration"							,			disable=args.local_rank not in [-1, 0]			)			):
														__snake_case	:    int											=     tuple(t.to(args.device			) for t in inputs			)
														((__snake_case)				,    )	:    Optional[int]											=     inputs

														# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
														__snake_case	:    int											=     model(__lowerCamelCase							,			labels=__lowerCamelCase							,			head_mask=__lowerCamelCase			)
														#  (loss), lm_logits, presents, (all hidden_states), (attentions)
														__snake_case				,    __snake_case				,    __snake_case	:    List[str]											=     (
														    outputs[0],
														    outputs[1],
														    outputs[-1],
														)  # Loss and logits are the first, attention the last
														loss.backward()  # Backpropagate to populate the gradients in the head mask
														total_loss += loss.detach().cpu().numpy()
														if compute_entropy:
																					for layer, attn in enumerate(__lowerCamelCase			):
																												__snake_case	:    str											=     entropy(attn.detach()							,			__lowerCamelCase			)
																												attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).sum(0			).detach()

														if compute_importance:
																					head_importance += head_mask.grad.abs().detach()
														tot_tokens += torch.ones_like(__lowerCamelCase			).float().detach().sum().data

							# Normalize
							attn_entropy /= tot_tokens
							head_importance /= tot_tokens
							# Layerwise importance normalization
							if not args.dont_normalize_importance_by_layer:
														__snake_case	:    List[Any]											=     2
														__snake_case	:    Tuple											=     torch.pow(torch.pow(__lowerCamelCase							,			__lowerCamelCase			).sum(-1			)							,			1 / exponent			)
														head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-20

							if not args.dont_normalize_global_importance:
														__snake_case	:    Tuple											=     (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

							# Print matrices
							if compute_entropy:
														logger.info("Attention entropies"			)
														print_ad_tensor(__lowerCamelCase			)
							if compute_importance:
														logger.info("Head importance scores"			)
														print_ad_tensor(__lowerCamelCase			)
							logger.info("Head ranked by importance scores"			)
							__snake_case	:    List[Any]											=     torch.zeros(head_importance.numel()							,			dtype=torch.long							,			device=args.device			)
							__snake_case	:    Tuple											=     torch.arange(
							    head_importance.numel()							,			device=args.device			)
							__snake_case	:    Optional[int]											=     head_ranks.view_as(__lowerCamelCase			)
							print_ad_tensor(__lowerCamelCase			)
							return attn_entropy, head_importance, total_loss







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case				,    __snake_case				,    __snake_case	:    Optional[Any]											=     compute_heads_importance(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     1 / loss  # instead of downsteam score use the LM loss
							logger.info("Pruning: original score: %f, threshold: %f"							,			__lowerCamelCase							,			original_score * args.masking_threshold			)

							__snake_case	:    List[Any]											=     torch.ones_like(__lowerCamelCase			)
							__snake_case	:    List[str]											=     max(1							,			int(new_head_mask.numel() * args.masking_amount			)			)

							__snake_case	:    Union[str, Any]											=     original_score
							while current_score >= original_score * args.masking_threshold:
														__snake_case	:    Optional[Any]											=     new_head_mask.clone().detach()  # save current head mask
														# heads from least important to most - keep only not-masked heads
														__snake_case	:    List[Any]											=     float("Inf"			)
														__snake_case	:    int											=     head_importance.view(-1			).sort()[1]

														if len(__lowerCamelCase			) <= num_to_mask:
																					print("BREAK BY num_to_mask"			)
																					break

														# mask heads
														__snake_case	:    Union[str, Any]											=     current_heads_to_mask[:num_to_mask]
														logger.info("Heads to mask: %s"							,			str(current_heads_to_mask.tolist()			)			)
														__snake_case	:    Optional[Any]											=     new_head_mask.view(-1			)
														__snake_case	:    str											=     0.0
														__snake_case	:    List[str]											=     new_head_mask.view_as(__lowerCamelCase			)
														__snake_case	:    Any											=     new_head_mask.clone().detach()
														print_ad_tensor(__lowerCamelCase			)

														# Compute metric and head importance again
														__snake_case				,    __snake_case				,    __snake_case	:    Union[str, Any]											=     compute_heads_importance(
														    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase							,			head_mask=__lowerCamelCase			)
														__snake_case	:    List[str]											=     1 / loss
														logger.info(
														    "Masking: current score: %f, remaining heads %d (%.1f percents)"							,			__lowerCamelCase							,			new_head_mask.sum()							,			new_head_mask.sum() / new_head_mask.numel() * 1_0_0							,			)

							logger.info("Final head mask"			)
							print_ad_tensor(__lowerCamelCase			)
							np.save(os.path.join(args.output_dir							,			"head_mask.npy"			)							,			head_mask.detach().cpu().numpy()			)

							return head_mask







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    int											=     datetime.now()
							__snake_case				,    __snake_case				,    __snake_case	:    Any											=     compute_heads_importance(
							    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase							,			compute_importance=__lowerCamelCase							,			head_mask=__lowerCamelCase			)
							__snake_case	:    Dict											=     1 / loss
							__snake_case	:    Optional[Any]											=     datetime.now() - before_time

							__snake_case	:    int											=     sum(p.numel() for p in model.parameters()			)
							__snake_case	:    Any											=     {
							    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCamelCase			)			)
							}

							for k, v in heads_to_prune.items():
														if isinstance(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Dict											=     [
																					    v,
																					]

							assert sum(len(__lowerCamelCase			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
							model.prune_heads(__lowerCamelCase			)
							__snake_case	:    Dict											=     sum(p.numel() for p in model.parameters()			)

							__snake_case	:    int											=     datetime.now()
							__snake_case				,    __snake_case				,    __snake_case	:    Optional[Any]											=     compute_heads_importance(
							    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			compute_entropy=__lowerCamelCase							,			compute_importance=__lowerCamelCase							,			head_mask=__lowerCamelCase							,			actually_pruned=__lowerCamelCase							,			)

							__snake_case	:    Optional[Any]											=     1 / loss
							__snake_case	:    Union[str, Any]											=     datetime.now() - before_time

							logger.info(
							    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"							,			__lowerCamelCase							,			__lowerCamelCase							,			pruned_num_params / original_num_params * 1_0_0							,			)
							logger.info("Pruning: score with masking: %f score with pruning: %f"							,			__lowerCamelCase							,			__lowerCamelCase			)
							logger.info("Pruning: speed ratio (original timing / new timing): %f percents"							,			original_time / new_time * 1_0_0			)
							save_model(__lowerCamelCase							,			args.output_dir			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    Tuple											=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--data_dir"							,			default=__lowerCamelCase							,			type=__lowerCamelCase							,			required=__lowerCamelCase							,			help="The input data dir. Should contain the .tsv files (or other data files) for the task."							,			)
							parser.add_argument(
							    "--model_name_or_path"							,			default=__lowerCamelCase							,			type=__lowerCamelCase							,			required=__lowerCamelCase							,			help="Path to pretrained model or model identifier from huggingface.co/models"							,			)
							parser.add_argument(
							    "--output_dir"							,			default=__lowerCamelCase							,			type=__lowerCamelCase							,			required=__lowerCamelCase							,			help="The output directory where the model predictions and checkpoints will be written."							,			)

							# Other parameters
							parser.add_argument(
							    "--config_name"							,			default=""							,			type=__lowerCamelCase							,			help="Pretrained config name or path if not the same as model_name_or_path"							,			)
							parser.add_argument(
							    "--tokenizer_name"							,			default=""							,			type=__lowerCamelCase							,			help="Pretrained tokenizer name or path if not the same as model_name_or_path"							,			)
							parser.add_argument(
							    "--cache_dir"							,			default=__lowerCamelCase							,			type=__lowerCamelCase							,			help="Where do you want to store the pre-trained models downloaded from s3"							,			)
							parser.add_argument(
							    "--data_subset"							,			type=__lowerCamelCase							,			default=-1							,			help="If > 0: limit the data to a subset of data_subset instances."			)
							parser.add_argument(
							    "--overwrite_output_dir"							,			action="store_true"							,			help="Whether to overwrite data in output directory"			)
							parser.add_argument(
							    "--overwrite_cache"							,			action="store_true"							,			help="Overwrite the cached training and evaluation sets"			)

							parser.add_argument(
							    "--dont_normalize_importance_by_layer"							,			action="store_true"							,			help="Don't normalize importance score by layers"			)
							parser.add_argument(
							    "--dont_normalize_global_importance"							,			action="store_true"							,			help="Don't normalize all importance scores between 0 and 1"							,			)

							parser.add_argument(
							    "--try_masking"							,			action="store_true"							,			help="Whether to try to mask head until a threshold of accuracy."			)
							parser.add_argument(
							    "--masking_threshold"							,			default=0.9							,			type=__lowerCamelCase							,			help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."							,			)
							parser.add_argument(
							    "--masking_amount"							,			default=0.1							,			type=__lowerCamelCase							,			help="Amount to heads to masking at each masking step."			)
							parser.add_argument("--metric_name"							,			default="acc"							,			type=__lowerCamelCase							,			help="Metric to use for head masking."			)

							parser.add_argument(
							    "--max_seq_length"							,			default=1_2_8							,			type=__lowerCamelCase							,			help=(
							        "The maximum total input sequence length after WordPiece tokenization. \n"
							        "Sequences longer than this will be truncated, sequences shorter padded."
							    )							,			)
							parser.add_argument("--batch_size"							,			default=1							,			type=__lowerCamelCase							,			help="Batch size."			)

							parser.add_argument("--seed"							,			type=__lowerCamelCase							,			default=4_2			)
							parser.add_argument("--local_rank"							,			type=__lowerCamelCase							,			default=-1							,			help="local_rank for distributed training on gpus"			)
							parser.add_argument("--no_cuda"							,			action="store_true"							,			help="Whether not to use CUDA when available"			)
							parser.add_argument("--server_ip"							,			type=__lowerCamelCase							,			default=""							,			help="Can be used for distant debugging."			)
							parser.add_argument("--server_port"							,			type=__lowerCamelCase							,			default=""							,			help="Can be used for distant debugging."			)
							__snake_case	:    Union[str, Any]											=     parser.parse_args()

							if args.server_ip and args.server_port:
														# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
														import ptvsd

														print("Waiting for debugger attach"			)
														ptvsd.enable_attach(address=(args.server_ip, args.server_port)							,			redirect_output=__lowerCamelCase			)
														ptvsd.wait_for_attach()

							# Setup devices and distributed training
							if args.local_rank == -1 or args.no_cuda:
														__snake_case	:    str											=     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"			)
														__snake_case	:    Union[str, Any]											=     0 if args.no_cuda else torch.cuda.device_count()
							else:
														torch.cuda.set_device(args.local_rank			)
														__snake_case	:    Tuple											=     torch.device("cuda"							,			args.local_rank			)
														__snake_case	:    int											=     1
														torch.distributed.init_process_group(backend="nccl"			)  # Initializes the distributed backend

							# Setup logging
							logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
							logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device							,			args.n_gpu							,			bool(args.local_rank != -1			)			)			)

							__snake_case	:    Tuple											=     GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

							# Distributed and parallel training
							model.to(args.device			)
							if args.local_rank != -1:
														__snake_case	:    int											=     nn.parallel.DistributedDataParallel(
														    __lowerCamelCase							,			device_ids=[args.local_rank]							,			output_device=args.local_rank							,			find_unused_parameters=__lowerCamelCase			)
							elif args.n_gpu > 1:
														__snake_case	:    str											=     nn.DataParallel(__lowerCamelCase			)

							# Print/save training arguments
							os.makedirs(args.output_dir							,			exist_ok=__lowerCamelCase			)
							torch.save(__lowerCamelCase							,			os.path.join(args.output_dir							,			"run_args.bin"			)			)
							logger.info("Training/evaluation parameters %s"							,			__lowerCamelCase			)

							# Prepare dataset
							__snake_case	:    Optional[Any]											=     np.concatenate(
							    [
							        np.loadtxt(args.data_dir							,			dtype=np.intaa			),
							    ]			)
							__snake_case	:    Any											=     (torch.from_numpy(__lowerCamelCase			),)
							__snake_case	:    str											=     TensorDataset(*__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     RandomSampler(__lowerCamelCase			)
							__snake_case	:    Optional[int]											=     DataLoader(__lowerCamelCase							,			sampler=__lowerCamelCase							,			batch_size=args.batch_size			)

							# Compute head entropy and importance score
							compute_heads_importance(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)

							# Try head masking (set heads to zero until the score goes under a threshole)
							# and head pruning (remove masked heads and see the effect on the network)
							if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
														__snake_case	:    List[Any]											=     mask_heads(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)
														prune_heads(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)


if __name__ == "__main__":
			main()





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_snake_case  :	int			      =       {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_snake_case  :	Dict			      =       logging.WARNING







def        lowerCAmelCase_  (      ):
							__snake_case	:    Union[str, Any]											=     os.getenv("DATASETS_VERBOSITY"							,			__lowerCamelCase			)
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    F'Unknown option DATASETS_VERBOSITY={env_level_str}, '
																					    F'has to be one of: { ", ".join(log_levels.keys()			) }'			)
							return _default_log_level







def        lowerCAmelCase_  (      ):
							return __name__.split("."			)[0]







def        lowerCAmelCase_  (      ):
							return logging.getLogger(_get_library_name()			)







def        lowerCAmelCase_  (      ):
							# Apply our default configuration to the library root logger.
							__snake_case	:    str											=     _get_library_root_logger()
							library_root_logger.setLevel(_get_default_logging_level()			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    Dict											=     _get_library_root_logger()
							library_root_logger.setLevel(logging.NOTSET			)







def        lowerCAmelCase_  (      __lowerCamelCase = None			):
							if name is None:
														__snake_case	:    Tuple											=     _get_library_name()
							return logging.getLogger(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return _get_library_root_logger().getEffectiveLevel()







def        lowerCAmelCase_  (      __lowerCamelCase			):
							_get_library_root_logger().setLevel(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    List[str]											=     False







def        lowerCAmelCase_  (      ):
							__snake_case	:    Tuple											=     True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()

class        a	:




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Optional[Any]					,			**lowerCamelCase :						List[str]       )			->					Optional[int]:  # pylint: disable=unused-argument
														__snake_case	:    int											=     args[0] if args else None

							def __iter__(							self :						Dict       )			->					Optional[int]:
														return iter(self._iterator       )

							def __getattr__(							self :						int					,			lowerCamelCase :						Optional[Any]       )			->					List[Any]:

														def empty_fn(*lowerCamelCase :						Optional[int]					,			**lowerCamelCase :						List[Any]       ):  # pylint: disable=unused-argument
																					return

														return empty_fn

							def __enter__(							self :						List[str]       )			->					int:
														return self






							def __exit__(							self :						Dict					,			lowerCamelCase :						Any					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						str       )			->					Dict:
														return
_snake_case  :	Optional[Any]			      =       True

class        a	:




							"""simple docstring"""


							def __call__(							self :						str					,			*lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple=False					,			**lowerCamelCase :						Union[str, Any]       )			->					Optional[Any]:
														if _tqdm_active and not disable:
																					return tqdm_lib.tqdm(*lowerCamelCase					,			**lowerCamelCase       )
														else:
																					return EmptyTqdm(*lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						str					,			*lowerCamelCase :						str					,			**lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Optional[Any]											=     None
														if _tqdm_active:
																					return tqdm_lib.tqdm.set_lock(*lowerCamelCase					,			**lowerCamelCase       )






							def 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														if _tqdm_active:
																					return tqdm_lib.tqdm.get_lock()
_snake_case  :	int			      =       _tqdm_cls()







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							return bool(_tqdm_active			)







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    Tuple											=     True







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    List[Any]											=     False

