
'''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, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A          =    logging.getLogger(__name__)





def  _A		(							lowercase__	):
						lowercase__							 =       p * torch.log(lowercase__	)
						lowercase__							 =       0
						return -plogp.sum(dim=-1	)





def  _A		(							lowercase__	):
						logger.info("""lv, h >\t""" + """\t""".join(f'''{x + 1}''' for x in range(len(lowercase__	)	)	)	)
						for row in range(len(lowercase__	)	):
												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  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=True		,		lowercase__=True		,		lowercase__=None		,		lowercase__=False	):
						lowercase__ ,						lowercase__							 =       model.config.num_hidden_layers, model.config.num_attention_heads
						lowercase__							 =       torch.zeros(lowercase__		,		lowercase__	).to(args.device	)
						lowercase__							 =       torch.zeros(lowercase__		,		lowercase__	).to(args.device	)

						if head_mask is None:
												lowercase__							 =       torch.ones(lowercase__		,		lowercase__	).to(args.device	)

						head_mask.requires_grad_(requires_grad=lowercase__	)
						# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
						if actually_pruned:
												lowercase__							 =       None

						lowercase__							 =       None
						lowercase__							 =       None
						lowercase__							 =       0.0

						for step, inputs in enumerate(tqdm(lowercase__		,		desc="""Iteration"""		,		disable=args.local_rank not in [-1, 0]	)	):
												for k, v in inputs.items():
																		lowercase__							 =       v.to(args.device	)

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												lowercase__							 =       model(**lowercase__		,		head_mask=lowercase__	)
												lowercase__ ,						lowercase__ ,						lowercase__							 =       (
												    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

												if compute_entropy:
																		for layer, attn in enumerate(lowercase__	):
																								lowercase__							 =       entropy(attn.detach()	) * inputs["""attention_mask"""].float().unsqueeze(1	)
																								attn_entropy[layer] += masked_entropy.sum(-1	).sum(0	).detach()

												if compute_importance:
																		head_importance += head_mask.grad.abs().detach()

												# Also store our logits/labels if we want to compute metrics afterwards
												if preds is None:
																		lowercase__							 =       logits.detach().cpu().numpy()
																		lowercase__							 =       inputs["""labels"""].detach().cpu().numpy()
												else:
																		lowercase__							 =       np.append(lowercase__		,		logits.detach().cpu().numpy()		,		axis=0	)
																		lowercase__							 =       np.append(lowercase__		,		inputs["""labels"""].detach().cpu().numpy()		,		axis=0	)

												tot_tokens += inputs["attention_mask"].float().detach().sum().data

						# Normalize
						attn_entropy /= tot_tokens
						head_importance /= tot_tokens
						# Layerwise importance normalization
						if not args.dont_normalize_importance_by_layer:
												lowercase__							 =       2
												lowercase__							 =       torch.pow(torch.pow(lowercase__		,		lowercase__	).sum(-1	)		,		1 / exponent	)
												head_importance /= norm_by_layer.unsqueeze(-1	) + 1e-20

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

						# Print/save matrices
						np.save(os.path.join(args.output_dir		,		"""attn_entropy.npy"""	)		,		attn_entropy.detach().cpu().numpy()	)
						np.save(os.path.join(args.output_dir		,		"""head_importance.npy"""	)		,		head_importance.detach().cpu().numpy()	)

						logger.info("""Attention entropies"""	)
						print_ad_tensor(lowercase__	)
						logger.info("""Head importance scores"""	)
						print_ad_tensor(lowercase__	)
						logger.info("""Head ranked by importance scores"""	)
						lowercase__							 =       torch.zeros(head_importance.numel()		,		dtype=torch.long		,		device=args.device	)
						lowercase__							 =       torch.arange(
						    head_importance.numel()		,		device=args.device	)
						lowercase__							 =       head_ranks.view_as(lowercase__	)
						print_ad_tensor(lowercase__	)

						return attn_entropy, head_importance, preds, labels





def  _A		(							lowercase__		,		lowercase__		,		lowercase__	):
						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       compute_heads_importance(lowercase__		,		lowercase__		,		lowercase__		,		compute_entropy=lowercase__	)
						lowercase__							 =       np.argmax(lowercase__		,		axis=1	) if args.output_mode == """classification""" else np.squeeze(lowercase__	)
						lowercase__							 =       glue_compute_metrics(args.task_name		,		lowercase__		,		lowercase__	)[args.metric_name]
						logger.info("""Pruning: original score: %f, threshold: %f"""		,		lowercase__		,		original_score * args.masking_threshold	)

						lowercase__							 =       torch.ones_like(lowercase__	)
						lowercase__							 =       max(1		,		int(new_head_mask.numel() * args.masking_amount	)	)

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

												if len(lowercase__	) <= num_to_mask:
																		break

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

												# Compute metric and head importance again
												lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       compute_heads_importance(
												    lowercase__		,		lowercase__		,		lowercase__		,		compute_entropy=lowercase__		,		head_mask=lowercase__	)
												lowercase__							 =       np.argmax(lowercase__		,		axis=1	) if args.output_mode == """classification""" else np.squeeze(lowercase__	)
												lowercase__							 =       glue_compute_metrics(args.task_name		,		lowercase__		,		lowercase__	)[args.metric_name]
												logger.info(
												    """Masking: current score: %f, remaining heads %d (%.1f percents)"""		,		lowercase__		,		new_head_mask.sum()		,		new_head_mask.sum() / new_head_mask.numel() * 100		,		)

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

						return head_mask





def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	):
						lowercase__							 =       datetime.now()
						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       compute_heads_importance(
						    lowercase__		,		lowercase__		,		lowercase__		,		compute_entropy=lowercase__		,		compute_importance=lowercase__		,		head_mask=lowercase__	)
						lowercase__							 =       np.argmax(lowercase__		,		axis=1	) if args.output_mode == """classification""" else np.squeeze(lowercase__	)
						lowercase__							 =       glue_compute_metrics(args.task_name		,		lowercase__		,		lowercase__	)[args.metric_name]
						lowercase__							 =       datetime.now() - before_time

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

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

						lowercase__							 =       datetime.now()
						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       compute_heads_importance(
						    lowercase__		,		lowercase__		,		lowercase__		,		compute_entropy=lowercase__		,		compute_importance=lowercase__		,		head_mask=lowercase__		,		actually_pruned=lowercase__		,		)
						lowercase__							 =       np.argmax(lowercase__		,		axis=1	) if args.output_mode == """classification""" else np.squeeze(lowercase__	)
						lowercase__							 =       glue_compute_metrics(args.task_name		,		lowercase__		,		lowercase__	)[args.metric_name]
						lowercase__							 =       datetime.now() - before_time

						logger.info(
						    """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""		,		lowercase__		,		lowercase__		,		pruned_num_params / original_num_params * 100		,		)
						logger.info("""Pruning: score with masking: %f score with pruning: %f"""		,		lowercase__		,		lowercase__	)
						logger.info("""Pruning: speed ratio (new timing / original timing): %f percents"""		,		original_time / new_time * 100	)





def  _A		(							):
						lowercase__							 =       argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--data_dir"""		,		default=lowercase__		,		type=lowercase__		,		required=lowercase__		,		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=lowercase__		,		type=lowercase__		,		required=lowercase__		,		help="""Path to pretrained model or model identifier from huggingface.co/models"""		,		)
						parser.add_argument(
						    """--task_name"""		,		default=lowercase__		,		type=lowercase__		,		required=lowercase__		,		help="""The name of the task to train selected in the list: """ + """, """.join(glue_processors.keys()	)		,		)
						parser.add_argument(
						    """--output_dir"""		,		default=lowercase__		,		type=lowercase__		,		required=lowercase__		,		help="""The output directory where the model predictions and checkpoints will be written."""		,		)

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

						parser.add_argument(
						    """--max_seq_length"""		,		default=128		,		type=lowercase__		,		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=lowercase__		,		help="""Batch size."""	)

						parser.add_argument("""--seed"""		,		type=lowercase__		,		default=42	)
						parser.add_argument("""--local_rank"""		,		type=lowercase__		,		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=lowercase__		,		default=""""""		,		help="""Can be used for distant debugging."""	)
						parser.add_argument("""--server_port"""		,		type=lowercase__		,		default=""""""		,		help="""Can be used for distant debugging."""	)
						lowercase__							 =       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=lowercase__	)
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												lowercase__							 =       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""	)
												lowercase__							 =       0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank	)
												lowercase__							 =       torch.device("""cuda"""		,		args.local_rank	)
												lowercase__							 =       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	)	)	)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(args.local_rank	):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()

						# Set seeds
						set_seed(args.seed	)

						# Prepare GLUE task
						lowercase__							 =       args.task_name.lower()
						if args.task_name not in glue_processors:
												raise ValueError("""Task not found: %s""" % (args.task_name)	)
						lowercase__							 =       glue_processors[args.task_name]()
						lowercase__							 =       glue_output_modes[args.task_name]
						lowercase__							 =       processor.get_labels()
						lowercase__							 =       len(lowercase__	)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowercase__							 =       AutoConfig.from_pretrained(
						    args.config_name if args.config_name else args.model_name_or_path		,		num_labels=lowercase__		,		finetuning_task=args.task_name		,		output_attentions=lowercase__		,		cache_dir=args.cache_dir		,		)
						lowercase__							 =       AutoTokenizer.from_pretrained(
						    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path		,		cache_dir=args.cache_dir		,		)
						lowercase__							 =       AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path		,		from_tf=bool(""".ckpt""" in args.model_name_or_path	)		,		config=lowercase__		,		cache_dir=args.cache_dir		,		)

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

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

						# Prepare dataset for the GLUE task
						lowercase__							 =       GlueDataset(lowercase__		,		tokenizer=lowercase__		,		mode="""dev"""	)
						if args.data_subset > 0:
												lowercase__							 =       Subset(lowercase__		,		list(range(min(args.data_subset		,		len(lowercase__	)	)	)	)	)
						lowercase__							 =       SequentialSampler(lowercase__	) if args.local_rank == -1 else DistributedSampler(lowercase__	)
						lowercase__							 =       DataLoader(
						    lowercase__		,		sampler=lowercase__		,		batch_size=args.batch_size		,		collate_fn=lowercase__	)

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

						# 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:
												lowercase__							 =       mask_heads(lowercase__		,		lowercase__		,		lowercase__	)
												prune_heads(lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	)


if __name__ == "__main__":
				main()








'''simple docstring'''



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       copy.deepcopy(lowercase__	)
						lowercase__							 =       sorted(lowercase__	)
						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
						return quantized





def  _A		(							lowercase__		,		lowercase__=0	):
						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
						# count number of elements per group
						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
						lowercase__							 =       [0] + bins + [np.inf]
						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
						return groups







class 			A							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
									lowercase__							 =       sampler
									lowercase__							 =       group_ids
									lowercase__							 =       batch_size



			def __iter__(					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       defaultdict(lowerCamelCase__       )
									lowercase__							 =       defaultdict(lowerCamelCase__       )

									lowercase__							 =       0
									for idx in self.sampler:
															lowercase__							 =       self.group_ids[idx]
															buffer_per_group[group_id].append(lowerCamelCase__       )
															samples_per_group[group_id].append(lowerCamelCase__       )
															if len(buffer_per_group[group_id]       ) == self.batch_size:
																					yield buffer_per_group[group_id]  # TODO
																					num_batches += 1
																					del buffer_per_group[group_id]
															assert len(buffer_per_group[group_id]       ) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									lowercase__							 =       len(self       )
									lowercase__							 =       expected_num_batches - num_batches
									if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															lowercase__							 =       []
															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
																					batch_idx.extend(lowerCamelCase__       )
																					if len(lowerCamelCase__       ) >= self.batch_size:
																											yield batch_idx[: self.batch_size]
																											lowercase__							 =       batch_idx[self.batch_size :]
																											num_remaining -= 1
															if len(lowerCamelCase__       ) > 0:
																					yield batch_idx
																					num_remaining -= 1
									assert num_remaining == 0



			def __len__(					self       )			->		Any:




									'''simple docstring'''



									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size






