



import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
	from transformers import LlamaTokenizerFast
except ImportError as e:
	warnings.warn(e)
	warnings.warn(
	    """The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
	)
	UpperCAmelCase   :							Any							 =None
UpperCAmelCase   :							Optional[Any]							 ={
    """7B""": 1_1008,
    """13B""": 1_3824,
    """30B""": 1_7920,
    """65B""": 2_2016,
    """70B""": 2_8672,
}
UpperCAmelCase   :							Tuple							 ={
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase=1   ,	_lowerCAmelCase=2_56):
					return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)



def    _lowerCAmelCase    (_lowerCAmelCase):
					with open(_lowerCAmelCase   ,	"r") as f:
										return json.load(_lowerCAmelCase)



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase):
					with open(_lowerCAmelCase   ,	"w") as f:
										json.dump(_lowerCAmelCase   ,	_lowerCAmelCase)



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase=True):
					os.makedirs(_lowerCAmelCase   ,	exist_ok=_lowerCAmelCase)
					UpperCamelCase_		  =   os.path.join(_lowerCAmelCase   ,	"tmp")
					os.makedirs(_lowerCAmelCase   ,	exist_ok=_lowerCAmelCase)

					UpperCamelCase_		  =   read_json(os.path.join(_lowerCAmelCase   ,	"params.json"))
					UpperCamelCase_		  =   NUM_SHARDS[model_size]
					UpperCamelCase_		  =   params["n_layers"]
					UpperCamelCase_		  =   params["n_heads"]
					UpperCamelCase_		  =   n_heads // num_shards
					UpperCamelCase_		  =   params["dim"]
					UpperCamelCase_		  =   dim // n_heads
					UpperCamelCase_		  =   1_0000.0
					UpperCamelCase_		  =   1.0 / (base ** (torch.arange(0   ,	_lowerCAmelCase   ,	2).float() / dims_per_head))

					if "n_kv_heads" in params:
										UpperCamelCase_		  =   params["n_kv_heads"]  # for GQA / MQA
										UpperCamelCase_		  =   n_heads_per_shard // num_key_value_heads
										UpperCamelCase_		  =   dim // num_key_value_heads
					else:  # compatibility with other checkpoints
										UpperCamelCase_		  =   n_heads
										UpperCamelCase_		  =   n_heads_per_shard
										UpperCamelCase_		  =   dim

					# permute for sliced rotary
					def permute(_lowerCAmelCase   ,	_lowerCAmelCase=n_heads   ,	_lowerCAmelCase=dim   ,	_lowerCAmelCase=dim):
										return w.view(_lowerCAmelCase   ,	dima // n_heads // 2   ,	2   ,	_lowerCAmelCase).transpose(1   ,	2).reshape(_lowerCAmelCase   ,	_lowerCAmelCase)

					print(f"""Fetching all parameters from the checkpoint at {input_base_path}.""")
					# Load weights
					if model_size == "7B":
										# Not sharded
										# (The sharded implementation would also work, but this is simpler.)
										UpperCamelCase_		  =   torch.load(os.path.join(_lowerCAmelCase   ,	"consolidated.00.pth")   ,	map_location="cpu")
					else:
										# Sharded
										UpperCamelCase_		  =   [
										    torch.load(os.path.join(_lowerCAmelCase   ,	f"""consolidated.{i:02d}.pth""")   ,	map_location="cpu")
										    for i in range(_lowerCAmelCase)
										]
					UpperCamelCase_		  =   0
					UpperCamelCase_		  =   {"weight_map": {}}
					for layer_i in range(_lowerCAmelCase):
										UpperCamelCase_		  =   f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
										if model_size == "7B":
															# Unsharded
															UpperCamelCase_		  =   {
															    f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute(
															        loaded[f"""layers.{layer_i}.attention.wq.weight"""]),
															    f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute(
															        loaded[f"""layers.{layer_i}.attention.wk.weight"""]),
															    f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""],
															    f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""],
															    f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""],
															    f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""],
															    f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""],
															    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""],
															    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""],
															}
										else:
															# Sharded
															# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
															# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
															# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

															UpperCamelCase_		  =   {
															    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][
															        f"""layers.{layer_i}.attention_norm.weight"""
															    ].clone(),
															    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][
															        f"""layers.{layer_i}.ffn_norm.weight"""
															    ].clone(),
															}
															UpperCamelCase_		  =   permute(
															    torch.cat(
															        [
															            loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase)
															            for i in range(_lowerCAmelCase)
															        ]   ,	dim=0   ,	).reshape(_lowerCAmelCase   ,	_lowerCAmelCase))
															UpperCamelCase_		  =   permute(
															    torch.cat(
															        [
															            loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view(
															                _lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase)
															            for i in range(_lowerCAmelCase)
															        ]   ,	dim=0   ,	).reshape(_lowerCAmelCase   ,	_lowerCAmelCase)   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	)
															UpperCamelCase_		  =   torch.cat(
															    [
															        loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view(
															            _lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase)
															        for i in range(_lowerCAmelCase)
															    ]   ,	dim=0   ,	).reshape(_lowerCAmelCase   ,	_lowerCAmelCase)

															UpperCamelCase_		  =   torch.cat(
															    [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase)]   ,	dim=1)
															UpperCamelCase_		  =   torch.cat(
															    [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase)]   ,	dim=0)
															UpperCamelCase_		  =   torch.cat(
															    [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase)]   ,	dim=1)
															UpperCamelCase_		  =   torch.cat(
															    [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase)]   ,	dim=0)

										UpperCamelCase_		  =   inv_freq
										for k, v in state_dict.items():
															UpperCamelCase_		  =   filename
															param_count += v.numel()
										torch.save(_lowerCAmelCase   ,	os.path.join(_lowerCAmelCase   ,	_lowerCAmelCase))

					UpperCamelCase_		  =   f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
					if model_size == "7B":
										# Unsharded
										UpperCamelCase_		  =   {
										    "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
										    "model.norm.weight": loaded["norm.weight"],
										    "lm_head.weight": loaded["output.weight"],
										}
					else:
										UpperCamelCase_		  =   {
										    "model.norm.weight": loaded[0]["norm.weight"],
										    "model.embed_tokens.weight": torch.cat(
										        [loaded[i]["tok_embeddings.weight"] for i in range(_lowerCAmelCase)]   ,	dim=1),
										    "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(_lowerCAmelCase)]   ,	dim=0),
										}

					for k, v in state_dict.items():
										UpperCamelCase_		  =   filename
										param_count += v.numel()
					torch.save(_lowerCAmelCase   ,	os.path.join(_lowerCAmelCase   ,	_lowerCAmelCase))

					# Write configs
					UpperCamelCase_		  =   {"total_size": param_count * 2}
					write_json(_lowerCAmelCase   ,	os.path.join(_lowerCAmelCase   ,	"pytorch_model.bin.index.json"))
					UpperCamelCase_		  =   params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
					UpperCamelCase_		  =   params["multiple_of"] if "multiple_of" in params else 2_56
					UpperCamelCase_		  =   LlamaConfig(
					    hidden_size=_lowerCAmelCase   ,	intermediate_size=compute_intermediate_size(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase)   ,	num_attention_heads=params["n_heads"]   ,	num_hidden_layers=params["n_layers"]   ,	rms_norm_eps=params["norm_eps"]   ,	num_key_value_heads=_lowerCAmelCase   ,	)
					config.save_pretrained(_lowerCAmelCase)

					# Make space so we can load the model properly now.
					del state_dict
					del loaded
					gc.collect()

					print("Loading the checkpoint in a Llama model.")
					UpperCamelCase_		  =   LlamaForCausalLM.from_pretrained(_lowerCAmelCase   ,	torch_dtype=torch.floataa   ,	low_cpu_mem_usage=_lowerCAmelCase)
					# Avoid saving this as part of the config.
					del model.config._name_or_path

					print("Saving in the Transformers format.")
					model.save_pretrained(_lowerCAmelCase   ,	safe_serialization=_lowerCAmelCase)
					shutil.rmtree(_lowerCAmelCase)



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase):
					# Initialize the tokenizer based on the `spm` model
					UpperCamelCase_		  =   LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
					print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""")
					UpperCamelCase_		  =   tokenizer_class(_lowerCAmelCase)
					tokenizer.save_pretrained(_lowerCAmelCase)



def    _lowerCAmelCase    ():
					UpperCamelCase_		  =   argparse.ArgumentParser()
					parser.add_argument(
					    "--input_dir"   ,	help="Location of LLaMA weights, which contains tokenizer.model and model folders"   ,	)
					parser.add_argument(
					    "--model_size"   ,	choices=["7B", "7Bf", "13B", "13Bf", "30B", "65B", "70B", "70Bf", "tokenizer_only"]   ,	)
					parser.add_argument(
					    "--output_dir"   ,	help="Location to write HF model and tokenizer"   ,	)
					parser.add_argument("--safe_serialization"   ,	type=_lowerCAmelCase   ,	help="Whether or not to save using `safetensors`.")
					UpperCamelCase_		  =   parser.parse_args()
					if args.model_size != "tokenizer_only":
										write_model(
										    model_path=args.output_dir   ,	input_base_path=os.path.join(args.input_dir   ,	args.model_size)   ,	model_size=args.model_size   ,	safe_serialization=args.safe_serialization   ,	)
					UpperCamelCase_		  =   os.path.join(args.input_dir   ,	"tokenizer.model")
					write_tokenizer(args.output_dir   ,	_lowerCAmelCase)


if __name__ == "__main__":
	main()





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


UpperCAmelCase   :							List[Any]							 =logging.getLogger(__name__)



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



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase=False):
					UpperCamelCase_		  =   2
					if unlogit:
										UpperCamelCase_		  =   torch.pow(_lowerCAmelCase   ,	_lowerCAmelCase)
					UpperCamelCase_		  =   p * torch.log(_lowerCAmelCase)
					UpperCamelCase_		  =   0
					return -plogp.sum(dim=-1)



def    _lowerCAmelCase    (_lowerCAmelCase):
					logger.info("lv, h >\t" + "\t".join(f"""{x + 1}""" for x in range(len(_lowerCAmelCase))))
					for row in range(len(_lowerCAmelCase)):
										if tensor.dtype != torch.long:
															logger.info(f"""layer {row + 1}:\t""" + "\t".join(f"""{x:.5f}""" for x in tensor[row].cpu().data))
										else:
															logger.info(f"""layer {row + 1}:\t""" + "\t".join(f"""{x:d}""" for x in tensor[row].cpu().data))



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase=True   ,	_lowerCAmelCase=True   ,	_lowerCAmelCase=None   ,	_lowerCAmelCase=False):
					UpperCamelCase_  ,    UpperCamelCase_		  =   model.config.num_hidden_layers, model.config.num_attention_heads
					UpperCamelCase_		  =   torch.zeros(_lowerCAmelCase   ,	_lowerCAmelCase).to(args.device)
					UpperCamelCase_		  =   torch.zeros(_lowerCAmelCase   ,	_lowerCAmelCase).to(args.device)

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

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

					UpperCamelCase_		  =   0.0
					UpperCamelCase_		  =   0.0
					for step, inputs in enumerate(tqdm(_lowerCAmelCase   ,	desc="Iteration"   ,	disable=args.local_rank not in [-1, 0])):
										UpperCamelCase_		  =   tuple(t.to(args.device) for t in inputs)
										((UpperCamelCase_)  ,    )		  =   inputs

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

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

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

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

					# Print matrices
					if compute_entropy:
										logger.info("Attention entropies")
										print_ad_tensor(_lowerCAmelCase)
					if compute_importance:
										logger.info("Head importance scores")
										print_ad_tensor(_lowerCAmelCase)
					logger.info("Head ranked by importance scores")
					UpperCamelCase_		  =   torch.zeros(head_importance.numel()   ,	dtype=torch.long   ,	device=args.device)
					UpperCamelCase_		  =   torch.arange(
					    head_importance.numel()   ,	device=args.device)
					UpperCamelCase_		  =   head_ranks.view_as(_lowerCAmelCase)
					print_ad_tensor(_lowerCAmelCase)
					return attn_entropy, head_importance, total_loss



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_		  =   compute_heads_importance(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	compute_entropy=_lowerCAmelCase)
					UpperCamelCase_		  =   1 / loss  # instead of downsteam score use the LM loss
					logger.info("Pruning: original score: %f, threshold: %f"   ,	_lowerCAmelCase   ,	original_score * args.masking_threshold)

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

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

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

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

										# Compute metric and head importance again
										UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_		  =   compute_heads_importance(
										    _lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	compute_entropy=_lowerCAmelCase   ,	head_mask=_lowerCAmelCase)
										UpperCamelCase_		  =   1 / loss
										logger.info(
										    "Masking: current score: %f, remaining heads %d (%.1f percents)"   ,	_lowerCAmelCase   ,	new_head_mask.sum()   ,	new_head_mask.sum() / new_head_mask.numel() * 1_00   ,	)

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

					return head_mask



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					UpperCamelCase_		  =   datetime.now()
					UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_		  =   compute_heads_importance(
					    _lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	compute_entropy=_lowerCAmelCase   ,	compute_importance=_lowerCAmelCase   ,	head_mask=_lowerCAmelCase)
					UpperCamelCase_		  =   1 / loss
					UpperCamelCase_		  =   datetime.now() - before_time

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

					for k, v in heads_to_prune.items():
										if isinstance(_lowerCAmelCase   ,	_lowerCAmelCase):
															UpperCamelCase_		  =   [
															    v,
															]

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

					UpperCamelCase_		  =   datetime.now()
					UpperCamelCase_  ,    UpperCamelCase_  ,    UpperCamelCase_		  =   compute_heads_importance(
					    _lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	compute_entropy=_lowerCAmelCase   ,	compute_importance=_lowerCAmelCase   ,	head_mask=_lowerCAmelCase   ,	actually_pruned=_lowerCAmelCase   ,	)

					UpperCamelCase_		  =   1 / loss
					UpperCamelCase_		  =   datetime.now() - before_time

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



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

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

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

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

					parser.add_argument(
					    "--max_seq_length"   ,	default=1_28   ,	type=_lowerCAmelCase   ,	help=(
					        "The maximum total input sequence length after WordPiece tokenization. \n"
					        "Sequences longer than this will be truncated, sequences shorter padded."
					    )   ,	)
					parser.add_argument("--batch_size"   ,	default=1   ,	type=_lowerCAmelCase   ,	help="Batch size.")

					parser.add_argument("--seed"   ,	type=_lowerCAmelCase   ,	default=42)
					parser.add_argument("--local_rank"   ,	type=_lowerCAmelCase   ,	default=-1   ,	help="local_rank for distributed training on gpus")
					parser.add_argument("--no_cuda"   ,	action="store_true"   ,	help="Whether not to use CUDA when available")
					parser.add_argument("--server_ip"   ,	type=_lowerCAmelCase   ,	default=""   ,	help="Can be used for distant debugging.")
					parser.add_argument("--server_port"   ,	type=_lowerCAmelCase   ,	default=""   ,	help="Can be used for distant debugging.")
					UpperCamelCase_		  =   parser.parse_args()

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

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

					# Setup devices and distributed training
					if args.local_rank == -1 or args.no_cuda:
										UpperCamelCase_		  =   torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
										UpperCamelCase_		  =   0 if args.no_cuda else torch.cuda.device_count()
					else:
										torch.cuda.set_device(args.local_rank)
										UpperCamelCase_		  =   torch.device("cuda"   ,	args.local_rank)
										UpperCamelCase_		  =   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_		  =   GPTaLMHeadModel.from_pretrained(args.model_name_or_path)

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

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

					# Prepare dataset
					UpperCamelCase_		  =   np.concatenate(
					    [
					        np.loadtxt(args.data_dir   ,	dtype=np.intaa),
					    ])
					UpperCamelCase_		  =   (torch.from_numpy(_lowerCAmelCase),)
					UpperCamelCase_		  =   TensorDataset(*_lowerCAmelCase)
					UpperCamelCase_		  =   RandomSampler(_lowerCAmelCase)
					UpperCamelCase_		  =   DataLoader(_lowerCAmelCase   ,	sampler=_lowerCAmelCase   ,	batch_size=args.batch_size)

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

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


if __name__ == "__main__":
	main()
