

"""simple docstring"""






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_   :   Optional[Any]        =       logging.getLogger(__name__)



def   lowercase_	(    _lowercase       :		Optional[Any]							,      _lowercase       :		Dict	):




					'''simple docstring'''






					if os.path.exists(_SCREAMING_SNAKE_CASE	):
										if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE							,      "config.json"	)	) and os.path.isfile(
										    os.path.join(_SCREAMING_SNAKE_CASE							,      "config.json"	)	):
															os.remove(os.path.join(_SCREAMING_SNAKE_CASE							,      "config.json"	)	)
										if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE							,      "pytorch_model.bin"	)	) and os.path.isfile(
										    os.path.join(_SCREAMING_SNAKE_CASE							,      "pytorch_model.bin"	)	):
															os.remove(os.path.join(_SCREAMING_SNAKE_CASE							,      "pytorch_model.bin"	)	)
					else:
										os.makedirs(_SCREAMING_SNAKE_CASE	)
					model.save_pretrained(_SCREAMING_SNAKE_CASE	)



def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Union[str, Any]=False	):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[int]      =				2
					if unlogit:
										UpperCAmelCase			:   str      =				torch.pow(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	)
					UpperCAmelCase			:   Optional[int]      =				p * torch.log(_SCREAMING_SNAKE_CASE	)
					UpperCAmelCase			:   Dict      =				0
					return -plogp.sum(dim=-1	)



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(_SCREAMING_SNAKE_CASE	)	)	)	)
					for row in range(len(_SCREAMING_SNAKE_CASE	)	):
										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   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		Optional[int]							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]=True							,      _lowercase       :		Optional[int]=True							,      _lowercase       :		Dict=None							,      _lowercase       :		List[Any]=False	):




					'''simple docstring'''






					UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				model.config.num_hidden_layers, model.config.num_attention_heads
					UpperCAmelCase			:   Optional[Any]      =				torch.zeros(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	).to(args.device	)
					UpperCAmelCase			:   Dict      =				torch.zeros(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	).to(args.device	)

					if head_mask is None:
										UpperCAmelCase			:   Any      =				torch.ones(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	).to(args.device	)

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

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

										# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
										UpperCAmelCase			:   List[Any]      =				model(_SCREAMING_SNAKE_CASE							,      labels=_SCREAMING_SNAKE_CASE							,      head_mask=_SCREAMING_SNAKE_CASE	)
										#  (loss), lm_logits, presents, (all hidden_states), (attentions)
										UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   int      =				(
										    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(_SCREAMING_SNAKE_CASE	):
																				UpperCAmelCase			:   List[Any]      =				entropy(attn.detach()							,      _SCREAMING_SNAKE_CASE	)
																				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(_SCREAMING_SNAKE_CASE	).float().detach().sum().data

					# Normalize
					attn_entropy /= tot_tokens
					head_importance /= tot_tokens
					# Layerwise importance normalization
					if not args.dont_normalize_importance_by_layer:
										UpperCAmelCase			:   Union[str, Any]      =				2
										UpperCAmelCase			:   Tuple      =				torch.pow(torch.pow(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	).sum(-1	)							,      1 / exponent	)
										head_importance /= norm_by_layer.unsqueeze(-1	) + 1E-20

					if not args.dont_normalize_global_importance:
										UpperCAmelCase			:   int      =				(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

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



def   lowercase_	(    _lowercase       :		Union[str, Any]							,      _lowercase       :		Tuple							,      _lowercase       :		Tuple	):




					'''simple docstring'''






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

					UpperCAmelCase			:   Union[str, Any]      =				torch.ones_like(_SCREAMING_SNAKE_CASE	)
					UpperCAmelCase			:   Dict      =				max(1							,      int(new_head_mask.numel() * args.masking_amount	)	)

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

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

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

										# Compute metric and head importance again
										UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   Dict      =				compute_heads_importance(
										    _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      compute_entropy=_SCREAMING_SNAKE_CASE							,      head_mask=_SCREAMING_SNAKE_CASE	)
										UpperCAmelCase			:   Union[str, Any]      =				1 / loss
										logger.info(
										    "Masking: current score: %f, remaining heads %d (%.1f percents)"							,      _SCREAMING_SNAKE_CASE							,      new_head_mask.sum()							,      new_head_mask.sum() / new_head_mask.numel() * 1_00							,      )

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

					return head_mask



def   lowercase_	(    _lowercase       :		Optional[Any]							,      _lowercase       :		Dict							,      _lowercase       :		List[Any]							,      _lowercase       :		List[str]	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				datetime.now()
					UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   Union[str, Any]      =				compute_heads_importance(
					    _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      compute_entropy=_SCREAMING_SNAKE_CASE							,      compute_importance=_SCREAMING_SNAKE_CASE							,      head_mask=_SCREAMING_SNAKE_CASE	)
					UpperCAmelCase			:   List[Any]      =				1 / loss
					UpperCAmelCase			:   Tuple      =				datetime.now() - before_time

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

					for k, v in heads_to_prune.items():
										if isinstance(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	):
															UpperCAmelCase			:   int      =				[
															    v,
															]

					assert sum(len(_SCREAMING_SNAKE_CASE	) for h in heads_to_prune.values()	) == (1 - head_mask.long()).sum().item()
					model.prune_heads(_SCREAMING_SNAKE_CASE	)
					UpperCAmelCase			:   Union[str, Any]      =				sum(p.numel() for p in model.parameters()	)

					UpperCAmelCase			:   List[Any]      =				datetime.now()
					UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   str      =				compute_heads_importance(
					    _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      compute_entropy=_SCREAMING_SNAKE_CASE							,      compute_importance=_SCREAMING_SNAKE_CASE							,      head_mask=_SCREAMING_SNAKE_CASE							,      actually_pruned=_SCREAMING_SNAKE_CASE							,      )

					UpperCAmelCase			:   List[str]      =				1 / loss
					UpperCAmelCase			:   List[str]      =				datetime.now() - before_time

					logger.info(
					    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      pruned_num_params / original_num_params * 1_00							,      )
					logger.info("Pruning: score with masking: %f score with pruning: %f"							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	)
					logger.info("Pruning: speed ratio (original timing / new timing): %f percents"							,      original_time / new_time * 1_00	)
					save_model(_SCREAMING_SNAKE_CASE							,      args.output_dir	)



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[int]      =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--data_dir"							,      default=_SCREAMING_SNAKE_CASE							,      type=_SCREAMING_SNAKE_CASE							,      required=_SCREAMING_SNAKE_CASE							,      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=_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(
					    "--output_dir"							,      default=_SCREAMING_SNAKE_CASE							,      type=_SCREAMING_SNAKE_CASE							,      required=_SCREAMING_SNAKE_CASE							,      help="The output directory where the model predictions and checkpoints will be written."							,      )

					# Other parameters
					parser.add_argument(
					    "--config_name"							,      default=""							,      type=_SCREAMING_SNAKE_CASE							,      help="Pretrained config name or path if not the same as model_name_or_path"							,      )
					parser.add_argument(
					    "--tokenizer_name"							,      default=""							,      type=_SCREAMING_SNAKE_CASE							,      help="Pretrained tokenizer name or path if not the same as model_name_or_path"							,      )
					parser.add_argument(
					    "--cache_dir"							,      default=_SCREAMING_SNAKE_CASE							,      type=_SCREAMING_SNAKE_CASE							,      help="Where do you want to store the pre-trained models downloaded from s3"							,      )
					parser.add_argument(
					    "--data_subset"							,      type=_SCREAMING_SNAKE_CASE							,      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=_SCREAMING_SNAKE_CASE							,      help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."							,      )
					parser.add_argument(
					    "--masking_amount"							,      default=0.1							,      type=_SCREAMING_SNAKE_CASE							,      help="Amount to heads to masking at each masking step."	)
					parser.add_argument("--metric_name"							,      default="acc"							,      type=_SCREAMING_SNAKE_CASE							,      help="Metric to use for head masking."	)

					parser.add_argument(
					    "--max_seq_length"							,      default=1_28							,      type=_SCREAMING_SNAKE_CASE							,      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=_SCREAMING_SNAKE_CASE							,      help="Batch size."	)

					parser.add_argument("--seed"							,      type=_SCREAMING_SNAKE_CASE							,      default=42	)
					parser.add_argument("--local_rank"							,      type=_SCREAMING_SNAKE_CASE							,      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=_SCREAMING_SNAKE_CASE							,      default=""							,      help="Can be used for distant debugging."	)
					parser.add_argument("--server_port"							,      type=_SCREAMING_SNAKE_CASE							,      default=""							,      help="Can be used for distant debugging."	)
					UpperCAmelCase			:   Optional[int]      =				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=_SCREAMING_SNAKE_CASE	)
										ptvsd.wait_for_attach()

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

					UpperCAmelCase			:   Union[str, Any]      =				GPTaLMHeadModel.from_pretrained(args.model_name_or_path	)

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

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

					# Prepare dataset
					UpperCAmelCase			:   Tuple      =				np.concatenate(
					    [
					        np.loadtxt(args.data_dir							,      dtype=np.intaa	),
					    ]	)
					UpperCAmelCase			:   Optional[Any]      =				(torch.from_numpy(_SCREAMING_SNAKE_CASE	),)
					UpperCAmelCase			:   Tuple      =				TensorDataset(*_SCREAMING_SNAKE_CASE	)
					UpperCAmelCase			:   Optional[Any]      =				RandomSampler(_SCREAMING_SNAKE_CASE	)
					UpperCAmelCase			:   Dict      =				DataLoader(_SCREAMING_SNAKE_CASE							,      sampler=_SCREAMING_SNAKE_CASE							,      batch_size=args.batch_size	)

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

					# 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:
										UpperCAmelCase			:   List[Any]      =				mask_heads(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	)
										prune_heads(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
						main()





"""simple docstring"""






from __future__ import annotations

from collections import Counter
from random import random







class       snake_case__  :







			def __init__(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{}







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{}







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: str			,  lowercase			: str			,  lowercase			: float  ):





								'''simple docstring'''



								if nodea not in self.connections:
													self.add_node(lowercase  )
								if nodea not in self.connections:
													self.add_node(lowercase  )
								UpperCAmelCase			:   int      =				probability







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								return list(self.connections  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				0
								UpperCAmelCase			:   Union[str, Any]      =				random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		list[tuple[str, str, float]]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(_lowercase							,      _lowercase							,      _lowercase	)

					UpperCAmelCase			:   List[Any]      =				Counter(graph.get_nodes()	)
					UpperCAmelCase			:   List[Any]      =				start

					for _ in range(_lowercase	):
										UpperCAmelCase			:   Optional[Any]      =				graph.transition(_lowercase	)
										visited[node] += 1

					return visited


if __name__ == "__main__":
	import doctest

	doctest.testmod()


