






"""simple docstring"""
from typing import Any

import numpy as np



class 		a_      :







      '''simple docstring'''

      def __init__(self,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       if np.any(tableau[:, -1],     where=tableau[:, -1] < 0					):
        raise ValueError('RHS must be > 0'					)

       lowerCamelCase__     :			Dict     				=						tableau
       lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						tableau.shape

       # Number of decision variables x1, x2, x3...
       lowerCamelCase__     :			Any     				=						n_vars

       # Number of artificial variables to be minimised
       lowerCamelCase__     :			int     				=						len(np.where(tableau[self.n_vars : -1] == -1					)[0]					)

       # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
       lowerCamelCase__     :			str     				=						(self.n_art_vars > 0) + 1

       # Number of slack variables added to make inequalities into equalities
       lowerCamelCase__     :			List[str]     				=						self.n_rows - self.n_stages

       # Objectives for each stage
       lowerCamelCase__     :			int     				=						['max']

       # In two stage simplex, first minimise then maximise
       if self.n_art_vars:
        self.objectives.append('min'					)

       lowerCamelCase__     :			Optional[int]     				=						['']

       # Index of current pivot row and column
       lowerCamelCase__     :			Optional[int]     				=						None
       lowerCamelCase__     :			int     				=						None

       # Does objective row only contain (non)-negative values?
       lowerCamelCase__     :			Any     				=						False




      @staticmethod
      def 			a__							(*lowerCamelCase_					):

       '''simple docstring'''



       if len(lowerCamelCase_					) != 3:
        raise ValueError('Must provide n_vars, n_slack, and n_art_vars'					)

       if not all(x >= 0 and isinstance(lowerCamelCase_,     lowerCamelCase_					) for x in args					):
        raise ValueError('All arguments must be non-negative integers'					)

       # decision | slack | artificial
       lowerCamelCase__     :			Optional[Any]     				=						['x', 's', 'a']
       lowerCamelCase__     :			List[Any]     				=						[]
       for i in range(3					):
        for j in range(args[i]					):
         titles.append(string_starts[i] + str(j + 1					)					)
       titles.append('RHS'					)
       return titles




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			str     				=						self.objectives[-1]

       # Find entries of highest magnitude in objective rows
       lowerCamelCase__     :			Optional[Any]     				=						(objective == 'min') - (objective == 'max')
       lowerCamelCase__     :			Tuple     				=						np.argmax(sign * tableau[0, : self.n_vars]					)

       # Choice is only valid if below 0 for maximise, and above for minimise
       if sign * self.tableau[0, col_idx] <= 0:
        lowerCamelCase__     :			Any     				=						True
        return 0, 0

       # Pivot row is chosen as having the lowest quotient when elements of
       # the pivot column divide the right-hand side

       # Slice excluding the objective rows
       lowerCamelCase__     :			Tuple     				=						slice(self.n_stages,     self.n_rows					)

       # RHS
       lowerCamelCase__     :			int     				=						tableau[s, -1]

       # Elements of pivot column within slice
       lowerCamelCase__     :			Optional[Any]     				=						tableau[s, col_idx]

       # Array filled with nans
       lowerCamelCase__     :			List[Any]     				=						np.full(self.n_rows - self.n_stages,     np.nan					)

       # If element in pivot column is greater than zeron_stages, return
       # quotient or nan otherwise
       lowerCamelCase__     :			List[Any]     				=						np.divide(lowerCamelCase_,     lowerCamelCase_,     out=lowerCamelCase_,     where=divisor > 0					)

       # Arg of minimum quotient excluding the nan values. n_stages is added
       # to compensate for earlier exclusion of objective columns
       lowerCamelCase__     :			Optional[Any]     				=						np.nanargmin(lowerCamelCase_					) + self.n_stages
       return row_idx, col_idx




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						tableau[row_idx].copy()

       lowerCamelCase__     :			Any     				=						piv_row[col_idx]

       # Entry becomes 1
       piv_row *= 1 / piv_val

       # Variable in pivot column becomes basic, ie the only non-zero entry
       for idx, coeff in enumerate(tableau[:, col_idx]					):
        tableau[idx] += -coeff * piv_row
       lowerCamelCase__     :			Any     				=						piv_row
       return tableau




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       self.objectives.pop()

       if not self.objectives:
        return tableau

       # Slice containing ids for artificial columns
       lowerCamelCase__     :			List[str]     				=						slice(-self.n_art_vars - 1,     -1					)

       # Delete the artificial variable columns
       lowerCamelCase__     :			int     				=						np.delete(lowerCamelCase_,     lowerCamelCase_,     axis=1					)

       # Delete the objective row of the first stage
       lowerCamelCase__     :			str     				=						np.delete(lowerCamelCase_,     0,     axis=0					)

       lowerCamelCase__     :			Union[str, Any]     				=						1
       self.n_rows -= 1
       lowerCamelCase__     :			Optional[int]     				=						0
       lowerCamelCase__     :			Optional[int]     				=						False
       return tableau




      def 			a__							(self					):

       '''simple docstring'''



       for _ in range(1_0_0					):
        # Completion of each stage removes an objective. If both stages
        # are complete, then no objectives are left
        if not self.objectives:
         lowerCamelCase__     :			Optional[Any]     				=						self.generate_col_titles(
             self.n_vars,     self.n_slack,     self.n_art_vars					)

         # Find the values of each variable at optimal solution
         return self.interpret_tableau(self.tableau,     self.col_titles					)

        lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						self.find_pivot(self.tableau					)

        # If there are no more negative values in objective row
        if self.stop_iter:
         # Delete artificial variable columns and rows. Update attributes
         lowerCamelCase__     :			Any     				=						self.change_stage(self.tableau					)
        else:
         lowerCamelCase__     :			Optional[Any]     				=						self.pivot(self.tableau,     lowerCamelCase_,     lowerCamelCase_					)
       return {}





      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						{'P': abs(tableau[0, -1]					)}

       for i in range(self.n_vars					):
        # Gives ids of nonzero entries in the ith column
        lowerCamelCase__     :			List[str]     				=						np.nonzero(tableau[:, i]					)
        lowerCamelCase__     :			Tuple     				=						len(nonzero[0]					)

        # First entry in the nonzero ids
        lowerCamelCase__     :			List[str]     				=						nonzero[0][0]
        lowerCamelCase__     :			Optional[Any]     				=						tableau[nonzero_rowidx, i]

        # If there is only one nonzero value in column, which is one
        if n_nonzero == nonzero_val == 1:
         lowerCamelCase__     :			Union[str, Any]     				=						tableau[nonzero_rowidx, -1]
         lowerCamelCase__     :			int     				=						rhs_val

        # Check for basic variables
       for title in col_titles:
        # Don't add RHS or slack variables to output dict
        if title[0] not in "R-s-a":
         output_dict.setdefault(lowerCamelCase_,     0					)
       return output_dict


if __name__ == "__main__":
      import doctest

      doctest.testmod()


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


__SCREAMING_SNAKE_CASE						:    str     = logging.getLogger(__name__)
def 					snake_case						(__lowercase						)       ->       str:

						'''simple docstring'''



						_snake_case						:     List[str]				=    p * torch.log(__lowercase						)
						_snake_case						:     List[Any]				=    0
						return -plogp.sum(dim=-1						)
def 					snake_case						(__lowercase						)       ->       int:

						'''simple docstring'''



						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 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase=True					,			__lowercase=True					,			__lowercase=None					,			__lowercase=False						)       ->       List[Any]:

						'''simple docstring'''



						_snake_case				,_snake_case						:     List[Any]				=    model.config.num_hidden_layers, model.config.num_attention_heads
						_snake_case						:     Tuple				=    torch.zeros(__lowercase					,			__lowercase						).to(args.device						)
						_snake_case						:     str				=    torch.zeros(__lowercase					,			__lowercase						).to(args.device						)

						if head_mask is None:
												_snake_case						:     Tuple				=    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:
												_snake_case						:     Optional[int]				=    None

						_snake_case						:     Optional[int]				=    None
						_snake_case						:     List[str]				=    None
						_snake_case						:     List[Any]				=    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():
																		_snake_case						:     List[str]				=    v.to(args.device						)

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												_snake_case						:     int				=    model(**__lowercase					,			head_mask=__lowercase						)
												_snake_case				,_snake_case				,_snake_case						:     Any				=    (
												    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						):
																								_snake_case						:     Union[str, Any]				=    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:
																		_snake_case						:     str				=    logits.detach().cpu().numpy()
																		_snake_case						:     Union[str, Any]				=    inputs["labels"].detach().cpu().numpy()
												else:
																		_snake_case						:     Optional[int]				=    np.append(__lowercase					,			logits.detach().cpu().numpy()					,			axis=0						)
																		_snake_case						:     Dict				=    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:
												_snake_case						:     str				=    2
												_snake_case						:     Any				=    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:
												_snake_case						:     Union[str, Any]				=    (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"						)
						_snake_case						:     int				=    torch.zeros(head_importance.numel()					,			dtype=torch.long					,			device=args.device						)
						_snake_case						:     List[str]				=    torch.arange(
						    head_importance.numel()					,			device=args.device						)
						_snake_case						:     Tuple				=    head_ranks.view_as(__lowercase						)
						print_ad_tensor(__lowercase						)

						return attn_entropy, head_importance, preds, labels
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       int:

						'''simple docstring'''



						_snake_case				,_snake_case				,_snake_case				,_snake_case						:     List[Any]				=    compute_heads_importance(__lowercase					,			__lowercase					,			__lowercase					,			compute_entropy=__lowercase						)
						_snake_case						:     List[Any]				=    np.argmax(__lowercase					,			axis=1						) if args.output_mode == "classification" else np.squeeze(__lowercase						)
						_snake_case						:     Tuple				=    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						)

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

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

												if len(__lowercase						) <= num_to_mask:
																		break

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

												# Compute metric and head importance again
												_snake_case				,_snake_case				,_snake_case				,_snake_case						:     Optional[Any]				=    compute_heads_importance(
												    __lowercase					,			__lowercase					,			__lowercase					,			compute_entropy=__lowercase					,			head_mask=__lowercase						)
												_snake_case						:     Tuple				=    np.argmax(__lowercase					,			axis=1						) if args.output_mode == "classification" else np.squeeze(__lowercase						)
												_snake_case						:     Optional[Any]				=    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 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Optional[int]:

						'''simple docstring'''



						_snake_case						:     Dict				=    datetime.now()
						_snake_case				,_snake_case				,_snake_case				,_snake_case						:     Any				=    compute_heads_importance(
						    __lowercase					,			__lowercase					,			__lowercase					,			compute_entropy=__lowercase					,			compute_importance=__lowercase					,			head_mask=__lowercase						)
						_snake_case						:     int				=    np.argmax(__lowercase					,			axis=1						) if args.output_mode == "classification" else np.squeeze(__lowercase						)
						_snake_case						:     Dict				=    glue_compute_metrics(args.task_name					,			__lowercase					,			__lowercase						)[args.metric_name]
						_snake_case						:     Union[str, Any]				=    datetime.now() - before_time

						_snake_case						:     Union[str, Any]				=    sum(p.numel() for p in model.parameters()						)
						_snake_case						:     str				=    {
						    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						)
						_snake_case						:     Any				=    sum(p.numel() for p in model.parameters()						)

						_snake_case						:     List[str]				=    datetime.now()
						_snake_case				,_snake_case				,_snake_case				,_snake_case						:     Union[str, Any]				=    compute_heads_importance(
						    __lowercase					,			__lowercase					,			__lowercase					,			compute_entropy=__lowercase					,			compute_importance=__lowercase					,			head_mask=__lowercase					,			actually_pruned=__lowercase					,			)
						_snake_case						:     str				=    np.argmax(__lowercase					,			axis=1						) if args.output_mode == "classification" else np.squeeze(__lowercase						)
						_snake_case						:     List[str]				=    glue_compute_metrics(args.task_name					,			__lowercase					,			__lowercase						)[args.metric_name]
						_snake_case						:     str				=    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 					snake_case						()       ->       Any:

						'''simple docstring'''



						_snake_case						:     int				=    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."						)
						_snake_case						:     List[str]				=    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:
												_snake_case						:     Any				=    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
												_snake_case						:     Tuple				=    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						:     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						)						)						)
						# 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
						_snake_case						:     List[str]				=    args.task_name.lower()
						if args.task_name not in glue_processors:
												raise ValueError("Task not found: %s" % (args.task_name)						)
						_snake_case						:     str				=    glue_processors[args.task_name]()
						_snake_case						:     Any				=    glue_output_modes[args.task_name]
						_snake_case						:     Any				=    processor.get_labels()
						_snake_case						:     Any				=    len(__lowercase						)

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

						_snake_case						:     Any				=    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					,			)
						_snake_case						:     Any				=    AutoTokenizer.from_pretrained(
						    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path					,			cache_dir=args.cache_dir					,			)
						_snake_case						:     List[str]				=    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:
												_snake_case						:     List[str]				=    nn.parallel.DistributedDataParallel(
												    __lowercase					,			device_ids=[args.local_rank]					,			output_device=args.local_rank					,			find_unused_parameters=__lowercase						)
						elif args.n_gpu > 1:
												_snake_case						:     Optional[int]				=    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
						_snake_case						:     str				=    GlueDataset(__lowercase					,			tokenizer=__lowercase					,			mode="dev"						)
						if args.data_subset > 0:
												_snake_case						:     str				=    Subset(__lowercase					,			list(range(min(args.data_subset					,			len(__lowercase						)						)						)						)						)
						_snake_case						:     Optional[Any]				=    SequentialSampler(__lowercase						) if args.local_rank == -1 else DistributedSampler(__lowercase						)
						_snake_case						:     str				=    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:
												_snake_case						:     int				=    mask_heads(__lowercase					,			__lowercase					,			__lowercase						)
												prune_heads(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)


if __name__ == "__main__":
				main()