

"""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	:       Optional[int]        =						logging.getLogger(__name__)
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   p * torch.log(lowercase__ )
					A     =   0
					return -plogp.sum(dim=-1 )
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=True     ,				lowercase__=True     ,				lowercase__=None     ,				lowercase__=False ):
					"""simple docstring"""
					A     ,       A     =   model.config.num_hidden_layers, model.config.num_attention_heads
					A     =   torch.zeros(lowercase__     ,				lowercase__ ).to(args.device )
					A     =   torch.zeros(lowercase__     ,				lowercase__ ).to(args.device )

					if head_mask is None:
										A     =   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:
										A     =   None

					A     =   None
					A     =   None
					A     =   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():
															A     =   v.to(args.device )

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

					return attn_entropy, head_importance, preds, labels
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     ,       A     ,       A     ,       A     =   compute_heads_importance(lowercase__     ,				lowercase__     ,				lowercase__     ,				compute_entropy=lowercase__ )
					A     =   np.argmax(lowercase__     ,				axis=1 ) if args.output_mode == "classification" else np.squeeze(lowercase__ )
					A     =   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 )

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

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

										if len(lowercase__ ) <= num_to_mask:
															break

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

										# Compute metric and head importance again
										A     ,       A     ,       A     ,       A     =   compute_heads_importance(
										    lowercase__     ,				lowercase__     ,				lowercase__     ,				compute_entropy=lowercase__     ,				head_mask=lowercase__ )
										A     =   np.argmax(lowercase__     ,				axis=1 ) if args.output_mode == "classification" else np.squeeze(lowercase__ )
										A     =   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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   datetime.now()
					A     ,       A     ,       A     ,       A     =   compute_heads_importance(
					    lowercase__     ,				lowercase__     ,				lowercase__     ,				compute_entropy=lowercase__     ,				compute_importance=lowercase__     ,				head_mask=lowercase__ )
					A     =   np.argmax(lowercase__     ,				axis=1 ) if args.output_mode == "classification" else np.squeeze(lowercase__ )
					A     =   glue_compute_metrics(args.task_name     ,				lowercase__     ,				lowercase__ )[args.metric_name]
					A     =   datetime.now() - before_time

					A     =   sum(p.numel() for p in model.parameters() )
					A     =   {
					    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__ )
					A     =   sum(p.numel() for p in model.parameters() )

					A     =   datetime.now()
					A     ,       A     ,       A     ,       A     =   compute_heads_importance(
					    lowercase__     ,				lowercase__     ,				lowercase__     ,				compute_entropy=lowercase__     ,				compute_importance=lowercase__     ,				head_mask=lowercase__     ,				actually_pruned=lowercase__     ,				)
					A     =   np.argmax(lowercase__     ,				axis=1 ) if args.output_mode == "classification" else np.squeeze(lowercase__ )
					A     =   glue_compute_metrics(args.task_name     ,				lowercase__     ,				lowercase__ )[args.metric_name]
					A     =   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   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   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." )
					A     =   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:
										A     =   torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
										A     =   0 if args.no_cuda else torch.cuda.device_count()
					else:
										torch.cuda.set_device(args.local_rank )
										A     =   torch.device("cuda"     ,				args.local_rank )
										A     =   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
					A     =   args.task_name.lower()
					if args.task_name not in glue_processors:
										raise ValueError("Task not found: %s" % (args.task_name) )
					A     =   glue_processors[args.task_name]()
					A     =   glue_output_modes[args.task_name]
					A     =   processor.get_labels()
					A     =   len(lowercase__ )

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

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


if __name__ == "__main__":
	main()













"""simple docstring"""


# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


_UpperCamelCase							:       int       					=		'Update an existing config file with the latest defaults while maintaining the old configuration.'




def      snake_case    (A_ :str ):







  '''simple docstring'''

  a	:     int							=  args.config_file
  if config_file is None and Path(A_ ).exists():
    a	:     List[Any]							=  default_config_file
  elif not Path(A_ ).exists():
    raise ValueError(f'''The passed config file located at {config_file} doesn\'t exist.''' )
  a	:     Dict							=  load_config_from_file(A_ )

  if config_file.endswith('.json' ):
    config.to_json_file(A_ )
  else:
    config.to_yaml_file(A_ )
  return config_file




def      snake_case    (A_ :List[str]      ,     A_ :Union[str, Any] ):







  '''simple docstring'''

  a	:     int							=  parser.add_parser('update'      ,     parents=A_      ,     help=A_      ,     formatter_class=A_ )
  parser.add_argument(
      '--config_file'      ,     default=A_      ,     help=(
          'The path to the config file to update. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )      ,     )

  parser.set_defaults(func=A_ )
  return parser






def      snake_case    (A_ :List[str] ):







  '''simple docstring'''

  a	:     List[str]							=  update_config(A_ )
  print(f'''Sucessfully updated the configuration file at {config_file}.''' )




