

"""simple docstring"""





import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	'''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
	lowerCAmelCase__	:						Any  				=	Image.open(requests.get(A_						,     stream=A_ ).raw ).convert('''RGB''' )

	return image

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						List[Any]  				=	[]
	# fmt: off

	# vision encoder
	rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
	rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
	rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
	rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
	rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
	rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )

	for i in range(config.vision_config.num_hidden_layers ):
		rename_keys.append((f'visual_encoder.blocks.{i}.norm1.weight', f'vision_model.encoder.layers.{i}.layer_norm1.weight') )
		rename_keys.append((f'visual_encoder.blocks.{i}.norm1.bias', f'vision_model.encoder.layers.{i}.layer_norm1.bias') )
		rename_keys.append((f'visual_encoder.blocks.{i}.norm2.weight', f'vision_model.encoder.layers.{i}.layer_norm2.weight') )
		rename_keys.append((f'visual_encoder.blocks.{i}.norm2.bias', f'vision_model.encoder.layers.{i}.layer_norm2.bias') )
		rename_keys.append((f'visual_encoder.blocks.{i}.attn.qkv.weight', f'vision_model.encoder.layers.{i}.self_attn.qkv.weight') )
		rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.weight', f'vision_model.encoder.layers.{i}.self_attn.projection.weight',) )
		rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.bias', f'vision_model.encoder.layers.{i}.self_attn.projection.bias') )
		rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.weight', f'vision_model.encoder.layers.{i}.mlp.fc1.weight') )
		rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.bias', f'vision_model.encoder.layers.{i}.mlp.fc1.bias') )
		rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.weight', f'vision_model.encoder.layers.{i}.mlp.fc2.weight') )
		rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.bias', f'vision_model.encoder.layers.{i}.mlp.fc2.bias') )

	# QFormer
	rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''') )
	rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''') )

	# fmt: on
	return rename_keys

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	dct.pop(A_ )
	lowerCAmelCase__	:						int  				=	val

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	for i in range(config.vision_config.num_hidden_layers ):
		# read in original q and v biases
		lowerCAmelCase__	:						Dict  				=	state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias' )
		lowerCAmelCase__	:						Tuple  				=	state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias' )

		# next, set bias in the state dict
		lowerCAmelCase__	:						Dict  				=	torch.cat((q_bias, torch.zeros_like(A_						,     requires_grad=A_ ), v_bias) )
		lowerCAmelCase__	:						List[Any]  				=	qkv_bias

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						List[Any]  				=	3_64 if '''coco''' in model_name else 2_24
	lowerCAmelCase__	:						Any  				=	BlipaVisionConfig(image_size=A_ ).to_dict()

	# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
	# seems like flan-T5 models don't have bos_token_id properly set?
	if "opt-2.7b" in model_name:
		lowerCAmelCase__	:						Dict  				=	OPTConfig.from_pretrained('''facebook/opt-2.7b'''						,     eos_token_id=A_ ).to_dict()
	elif "opt-6.7b" in model_name:
		lowerCAmelCase__	:						Optional[int]  				=	OPTConfig.from_pretrained('''facebook/opt-6.7b'''						,     eos_token_id=A_ ).to_dict()
	elif "t5-xl" in model_name:
		lowerCAmelCase__	:						Union[str, Any]  				=	TaConfig.from_pretrained('''google/flan-t5-xl'''						,     dense_act_fn='''gelu'''						,     bos_token_id=1 ).to_dict()
	elif "t5-xxl" in model_name:
		lowerCAmelCase__	:						Optional[Any]  				=	TaConfig.from_pretrained('''google/flan-t5-xxl'''						,     dense_act_fn='''gelu'''						,     bos_token_id=1 ).to_dict()

	lowerCAmelCase__	:						Any  				=	BlipaConfig(vision_config=A_						,     text_config=A_ )

	return config, image_size

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=None						,     A_=False ):
	lowerCAmelCase__	:						List[Any]  				=	(
	    AutoTokenizer.from_pretrained('''facebook/opt-2.7b''' )
	    if '''opt''' in model_name
	    else AutoTokenizer.from_pretrained('''google/flan-t5-xl''' )
	)
	lowerCAmelCase__	:						Optional[Any]  				=	tokenizer('''\n'''						,     add_special_tokens=A_ ).input_ids[0]
	lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	get_blipa_config(A_						,     eos_token_id=A_ )

	lowerCAmelCase__	:						Optional[int]  				=	BlipaForConditionalGeneration(A_ ).eval()

	lowerCAmelCase__	:						Tuple  				=	{
	    '''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
	    '''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
	    '''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
	    '''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
	    '''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
	    '''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
	    '''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
	}

	lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	model_name_to_original[model_name]

	# load original model
	print('''Loading original model...''' )
	lowerCAmelCase__	:						Tuple  				=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	load_model_and_preprocess(
	    name=A_						,     model_type=A_						,     is_eval=A_						,     device=A_ )
	original_model.eval()
	print('''Done!''' )

	# update state dict keys
	lowerCAmelCase__	:						Dict  				=	original_model.state_dict()
	lowerCAmelCase__	:						Optional[int]  				=	create_rename_keys(A_ )
	for src, dest in rename_keys:
		rename_key(A_						,     A_						,     A_ )

	# some keys can be renamed efficiently
	for key, val in state_dict.copy().items():
		lowerCAmelCase__	:						Optional[int]  				=	state_dict.pop(A_ )
		if key.startswith('''Qformer.bert''' ):
			lowerCAmelCase__	:						Optional[Any]  				=	key.replace('''Qformer.bert'''						,     '''qformer''' )
		if "attention.self" in key:
			lowerCAmelCase__	:						Optional[int]  				=	key.replace('''self'''						,     '''attention''' )
		if "opt_proj" in key:
			lowerCAmelCase__	:						List[str]  				=	key.replace('''opt_proj'''						,     '''language_projection''' )
		if "t5_proj" in key:
			lowerCAmelCase__	:						str  				=	key.replace('''t5_proj'''						,     '''language_projection''' )
		if key.startswith('''opt''' ):
			lowerCAmelCase__	:						Tuple  				=	key.replace('''opt'''						,     '''language''' )
		if key.startswith('''t5''' ):
			lowerCAmelCase__	:						Union[str, Any]  				=	key.replace('''t5'''						,     '''language''' )
		lowerCAmelCase__	:						str  				=	val

	# read in qv biases
	read_in_q_v_bias(A_						,     A_ )

	lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	hf_model.load_state_dict(A_						,     strict=A_ )
	assert len(A_ ) == 0
	assert unexpected_keys == ["qformer.embeddings.position_ids"]

	lowerCAmelCase__	:						int  				=	load_demo_image()
	lowerCAmelCase__	:						List[Any]  				=	vis_processors['''eval'''](A_ ).unsqueeze(0 ).to(A_ )
	lowerCAmelCase__	:						List[str]  				=	tokenizer(['''\n''']						,     return_tensors='''pt''' ).input_ids.to(A_ )

	# create processor
	lowerCAmelCase__	:						int  				=	BlipImageProcessor(
	    size={'''height''': image_size, '''width''': image_size}						,     image_mean=A_						,     image_std=A_ )
	lowerCAmelCase__	:						int  				=	BlipaProcessor(image_processor=A_						,     tokenizer=A_ )
	lowerCAmelCase__	:						List[str]  				=	processor(images=A_						,     return_tensors='''pt''' ).pixel_values.to(A_ )

	# make sure processor creates exact same pixel values
	assert torch.allclose(A_						,     A_ )

	original_model.to(A_ )
	hf_model.to(A_ )
	with torch.no_grad():
		if "opt" in model_name:
			lowerCAmelCase__	:						str  				=	original_model({'''image''': original_pixel_values, '''text_input''': ['''''']} ).logits
			lowerCAmelCase__	:						Union[str, Any]  				=	hf_model(A_						,     A_ ).logits
		else:
			lowerCAmelCase__	:						Optional[Any]  				=	original_model(
			    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']} ).logits
			lowerCAmelCase__	:						Union[str, Any]  				=	input_ids.masked_fill(input_ids == tokenizer.pad_token_id						,     -1_00 )
			lowerCAmelCase__	:						List[str]  				=	hf_model(A_						,     A_						,     labels=A_ ).logits

	assert original_logits.shape == logits.shape
	print('''First values of original logits:'''						,     original_logits[0, :3, :3] )
	print('''First values of HF logits:'''						,     logits[0, :3, :3] )

	# assert values
	if model_name == "blip2-flan-t5-xl":
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor(
		    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]						,     device=A_ )
		assert torch.allclose(logits[0, :3, :3]						,     A_						,     atol=1e-4 )
	elif model_name == "blip2-flan-t5-xl-coco":
		lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor(
		    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]						,     device=A_ )
	else:
		# cast to same type
		lowerCAmelCase__	:						Dict  				=	logits.dtype
		assert torch.allclose(original_logits.to(A_ )						,     A_						,     atol=1e-2 )
	print('''Looks ok!''' )

	print('''Generating a caption...''' )
	lowerCAmelCase__	:						Union[str, Any]  				=	''''''
	lowerCAmelCase__	:						Optional[int]  				=	tokenizer(A_						,     return_tensors='''pt''' ).input_ids.to(A_ )

	lowerCAmelCase__	:						List[str]  				=	original_model.generate({'''image''': original_pixel_values} )
	lowerCAmelCase__	:						Union[str, Any]  				=	hf_model.generate(
	    A_						,     A_						,     do_sample=A_						,     num_beams=5						,     max_length=30						,     min_length=1						,     top_p=0.9						,     repetition_penalty=1.0						,     length_penalty=1.0						,     temperature=1						,     )
	print('''Original generation:'''						,     A_ )
	lowerCAmelCase__	:						Tuple  				=	input_ids.shape[1]
	lowerCAmelCase__	:						List[str]  				=	processor.batch_decode(outputs[:, prompt_length:]						,     skip_special_tokens=A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	[text.strip() for text in output_text]
	print('''HF generation:'''						,     A_ )

	if pytorch_dump_folder_path is not None:
		processor.save_pretrained(A_ )
		hf_model.save_pretrained(A_ )

	if push_to_hub:
		processor.push_to_hub(f'nielsr/{model_name}' )
		hf_model.push_to_hub(f'nielsr/{model_name}' )


if __name__ == "__main__":
							__UpperCamelCase  :      Optional[Any]							      =		argparse.ArgumentParser()
							__UpperCamelCase  :      List[Any]							      =		[
							    '''blip2-opt-2.7b''',
							    '''blip2-opt-6.7b''',
							    '''blip2-opt-2.7b-coco''',
							    '''blip2-opt-6.7b-coco''',
							    '''blip2-flan-t5-xl''',
							    '''blip2-flan-t5-xl-coco''',
							    '''blip2-flan-t5-xxl''',
							]
							parser.add_argument(
							    '''--model_name''',
							    default='''blip2-opt-2.7b''',
							    choices=choices,
							    type=str,
							    help='''Path to hf config.json of model to convert''',
							)
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							    help='''Whether to push the model and processor to the hub after converting''',
							)

							__UpperCamelCase  :      List[Any]							      =		parser.parse_args()

							convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__UpperCamelCase  :      int							      =		logging.getLogger(__name__)

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						Optional[Any]  				=	2
	if unlogit:
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.pow(A_						,     A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	p * torch.log(A_ )
	lowerCAmelCase__	:						List[Any]  				=	0
	return -plogp.sum(dim=-1 )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(A_ ) ) ) )
	for row in range(len(A_ ) ):
		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  (      A_						,     A_						,     A_						,     A_=True						,     A_=True						,     A_=None						,     A_=False ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	model.config.num_hidden_layers, model.config.num_attention_heads
	lowerCAmelCase__	:						Dict  				=	torch.zeros(A_						,     A_ ).to(args.device )
	lowerCAmelCase__	:						int  				=	torch.zeros(A_						,     A_ ).to(args.device )

	if head_mask is None:
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.ones(A_						,     A_ ).to(args.device )

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

	lowerCAmelCase__	:						Optional[int]  				=	0.0
	lowerCAmelCase__	:						Optional[int]  				=	0.0
	for step, inputs in enumerate(tqdm(A_						,     desc='''Iteration'''						,     disable=args.local_rank not in [-1, 0] ) ):
		lowerCAmelCase__	:						Any  				=	tuple(t.to(args.device ) for t in inputs )
		((lowerCAmelCase__)				,)	:						List[Any]  				=	inputs

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

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		lowerCAmelCase__	:						Any  				=	2
		lowerCAmelCase__	:						Dict  				=	torch.pow(torch.pow(A_						,     A_ ).sum(-1 )						,     1 / exponent )
		head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0

	if not args.dont_normalize_global_importance:
		lowerCAmelCase__	:						List[Any]  				=	(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	compute_heads_importance(A_						,     A_						,     A_						,     compute_entropy=A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	1 / loss  # instead of downsteam score use the LM loss
	logger.info('''Pruning: original score: %f, threshold: %f'''						,     A_						,     original_score * args.masking_threshold )

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

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

		if len(A_ ) <= num_to_mask:
			print('''BREAK BY num_to_mask''' )
			break

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

		# Compute metric and head importance again
		lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	compute_heads_importance(
		    A_						,     A_						,     A_						,     compute_entropy=A_						,     head_mask=A_ )
		lowerCAmelCase__	:						Tuple  				=	1 / loss
		logger.info(
		    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''						,     A_						,     new_head_mask.sum()						,     new_head_mask.sum() / new_head_mask.numel() * 1_00						,     )

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

	return head_mask

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	datetime.now()
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	compute_heads_importance(
	    A_						,     A_						,     A_						,     compute_entropy=A_						,     compute_importance=A_						,     head_mask=A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	1 / loss
	lowerCAmelCase__	:						Tuple  				=	datetime.now() - before_time

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

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

	assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
	model.prune_heads(A_ )
	lowerCAmelCase__	:						List[Any]  				=	sum(p.numel() for p in model.parameters() )

	lowerCAmelCase__	:						Any  				=	datetime.now()
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	compute_heads_importance(
	    A_						,     A_						,     A_						,     compute_entropy=A_						,     compute_importance=A_						,     head_mask=A_						,     actually_pruned=A_						,     )

	lowerCAmelCase__	:						int  				=	1 / loss
	lowerCAmelCase__	:						Dict  				=	datetime.now() - before_time

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

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--data_dir'''						,     default=A_						,     type=A_						,     required=A_						,     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=A_						,     type=A_						,     required=A_						,     help='''Path to pretrained model or model identifier from huggingface.co/models'''						,     )
	parser.add_argument(
	    '''--output_dir'''						,     default=A_						,     type=A_						,     required=A_						,     help='''The output directory where the model predictions and checkpoints will be written.'''						,     )

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

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

	parser.add_argument('''--seed'''						,     type=A_						,     default=42 )
	parser.add_argument('''--local_rank'''						,     type=A_						,     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=A_						,     default=''''''						,     help='''Can be used for distant debugging.''' )
	parser.add_argument('''--server_port'''						,     type=A_						,     default=''''''						,     help='''Can be used for distant debugging.''' )
	lowerCAmelCase__	:						Optional[Any]  				=	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=A_ )
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		lowerCAmelCase__	:						Union[str, Any]  				=	torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
		lowerCAmelCase__	:						str  				=	0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank )
		lowerCAmelCase__	:						Dict  				=	torch.device('''cuda'''						,     args.local_rank )
		lowerCAmelCase__	:						Union[str, 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 ) ) )

	lowerCAmelCase__	:						List[str]  				=	GPTaLMHeadModel.from_pretrained(args.model_name_or_path )

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

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

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

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

	# 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:
		lowerCAmelCase__	:						Tuple  				=	mask_heads(A_						,     A_						,     A_ )
		prune_heads(A_						,     A_						,     A_						,     A_ )


if __name__ == "__main__":
							main()




