





'''simple docstring'''




import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel




def 						__lowerCamelCase			(	UpperCAmelCase_=32					,				UpperCAmelCase_=10					,				UpperCAmelCase_=1_00					,				UpperCAmelCase_=10_26					,				UpperCAmelCase_=True					,				UpperCAmelCase_="data/tokenized_stories_train_wikitext103.jbl"					,				UpperCAmelCase_="igf_context_pairs.jbl"					,				)   ->Dict:
	set_seed(3							)
	# generate train_data and objective_set
	snake_case__       ,  snake_case__	       =   generate_datasets(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				number=UpperCAmelCase_					,				min_len=10_26					,				trim=UpperCAmelCase_							)
	# keeps model same across runs
	set_seed(4							)
	# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
	# can we train on GPU?
	snake_case__	       =   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'							)

	# load pretrained model
	snake_case__	       =   load_gpta('gpt2'							).to(UpperCAmelCase_							)
	print('computing perplexity on objective set'							)
	snake_case__	       =   compute_perplexity(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							).item()
	print('perplexity on objective set:'					,				UpperCAmelCase_							)

	# collect igf pairs and save to file demo.jbl
	collect_objective_set(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# clean up, delete model and data we don't need anymore
	del model, train_data, objective_set
	torch.cuda.empty_cache()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=15					,				UpperCAmelCase_=1_28					,				UpperCAmelCase_=1_00					,				UpperCAmelCase_="igf_model.pt"					,				)   ->Any:
	set_seed(42							)

	# Load pre-trained model
	snake_case__	       =   GPTaLMHeadModel.from_pretrained('gpt2'							)

	# Initialize secondary learner to use embedding weights of model
	snake_case__	       =   SecondaryLearner(UpperCAmelCase_							)

	# Train secondary learner
	snake_case__	       =   train_secondary_learner(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				max_epochs=UpperCAmelCase_					,				batch_size=UpperCAmelCase_					,				eval_freq=1_00					,				igf_model_path=UpperCAmelCase_					,				)

	del model, secondary_learner_train_data
	torch.cuda.empty_cache()

	return secondary_learner




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=32					,				UpperCAmelCase_=10_00					,				UpperCAmelCase_=16					,				UpperCAmelCase_=1.0					,				UpperCAmelCase_=recopy_gpta					,				UpperCAmelCase_=None					,				UpperCAmelCase_=10					,				UpperCAmelCase_="gpt2_finetuned.pt"					,				)   ->List[str]:
	snake_case__	       =   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'							)
	snake_case__	       =   RandomSampler(UpperCAmelCase_							)
	snake_case__	       =   DataLoader(UpperCAmelCase_					,				sampler=UpperCAmelCase_							)

	snake_case__	       =   max_steps // (len(UpperCAmelCase_							)) + 1
	snake_case__	       =   0
	snake_case__	       =   torch.zeros((1, context_len)					,				dtype=torch.long					,				device=UpperCAmelCase_							)
	snake_case__       ,  snake_case__       ,  snake_case__	       =   recopy_model(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	model.train()
	if secondary_learner is not None:
		secondary_learner.to(UpperCAmelCase_							)
		secondary_learner.eval()
	snake_case__	       =   []
	snake_case__	       =   0

	snake_case__	       =   []
	snake_case__	       =   []

	# Compute the performance of the transformer model at the beginning
	snake_case__	       =   compute_perplexity(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	test_perps.append(UpperCAmelCase_							)
	print('Test perplexity, step'					,				UpperCAmelCase_					,				':'					,				UpperCAmelCase_							)
	for epoch in range(int(UpperCAmelCase_							)							):
		for step, example in enumerate(UpperCAmelCase_							):
			torch.cuda.empty_cache()
			snake_case__	       =   random.randint(0					,				example.size(2							) - context_len - 1							)
			snake_case__	       =   example[0, 0, start : start + context_len]
			lm_optimizer.zero_grad()
			snake_case__	       =   model(UpperCAmelCase_					,				labels=UpperCAmelCase_							)
			snake_case__	       =   True

			if secondary_learner is not None:
				snake_case__	       =   secondary_learner.forward(
				    torch.tensor(UpperCAmelCase_					,				dtype=torch.long					,				device=UpperCAmelCase_							).unsqueeze(0							)							)[0].item()
				observed_qs.append(float(UpperCAmelCase_							)							)

				# Here we implement the simple non-constant threshold for the predicted IG(X) value
				# We will decay the selectivity of our secondary learner filter from
				# 1 standard deviation above average to 1 below average after 10 batches.

				if global_step == 10:
					snake_case__	       =   -1
				if predicted_q < threshold:
					snake_case__	       =   False

            # If we passed the filter, add the context to the batch!
			if do_backprop:
				contexts.append(np.array(context.cpu()							)							)
				snake_case__	       =   outputs[0]
				lm_loss.backward()
				examples += 1

			del outputs

			# Once the batch is filled with enough contexts, backprop on the batch.
			if examples == batch_size:
				torch.cuda.empty_cache()
				snake_case__	       =   0
				# Do LM backprop
				torch.nn.utils.clip_grad_norm_(model.parameters()					,				3.0							)
				lm_optimizer.step()
				lm_scheduler.step()  # Update learning rate schedule
				global_step += 1
				# Compute the performance of the transformer model at this batch
				if global_step % eval_interval == 0:
					snake_case__	       =   compute_perplexity(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
					test_perps.append(UpperCAmelCase_							)

					print('Test perplexity, step'					,				UpperCAmelCase_					,				':'					,				UpperCAmelCase_							)
            # Break out of the loop after 60 batches
			if max_steps > 0 and global_step > 60:
				break
		if max_steps > 0 and global_step > 60:
			break

    # save finetuned transformer model
	torch.save(model.state_dict()					,				UpperCAmelCase_							)
	torch.cuda.empty_cache()
	# Do some cleaning up so we can reinitialize for the next run of this function
	del lm_optimizer
	del lm_scheduler
	return model




def 						__lowerCamelCase			(	)   ->List[Any]:
	snake_case__	       =   argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'							)

	# Required parameters
	parser.add_argument(
	    '--data_dir'					,				default=UpperCAmelCase_					,				type=UpperCAmelCase_					,				required=UpperCAmelCase_					,				help='The input data dir. Should contain data files for WikiText.'					,				)
	parser.add_argument(
	    '--model_name_or_path'					,				default=UpperCAmelCase_					,				type=UpperCAmelCase_					,				required=UpperCAmelCase_					,				help='Path to pretrained model or model identifier from huggingface.co/models'					,				)
	parser.add_argument(
	    '--data_file'					,				type=UpperCAmelCase_					,				default=UpperCAmelCase_					,				help=(
	        'A jbl file containing tokenized data which can be split as objective dataset, '
	        'train_dataset and test_dataset.'
	    )					,				)

	parser.add_argument(
	    '--igf_data_file'					,				type=UpperCAmelCase_					,				default=UpperCAmelCase_					,				help='A jbl file containing the context and information gain pairs to train secondary learner.'					,				)

	parser.add_argument(
	    '--output_dir'					,				default=UpperCAmelCase_					,				type=UpperCAmelCase_					,				required=UpperCAmelCase_					,				help='The output directory where the final fine-tuned model is stored.'					,				)

	parser.add_argument(
	    '--tokenizer_name'					,				default=UpperCAmelCase_					,				type=UpperCAmelCase_					,				help='Pretrained tokenizer name or path if not the same as model_name'					,				)
	parser.add_argument('--seed'					,				type=UpperCAmelCase_					,				default=UpperCAmelCase_					,				help='A seed for reproducible training.'							)

	parser.add_argument(
	    '--context_len'					,				default=32					,				type=UpperCAmelCase_					,				help=(
	        'The maximum total input sequence length after tokenization. Sequences longer '
	        'than this will be truncated, sequences shorter will be padded.'
	    )					,				)

	parser.add_argument(
	    '--size_objective_set'					,				default=1_00					,				type=UpperCAmelCase_					,				help='number of articles that are long enough to be used as our objective set'					,				)
	parser.add_argument(
	    '--eval_freq'					,				default=1_00					,				type=UpperCAmelCase_					,				help='secondary model evaluation is triggered at eval_freq'							)

	parser.add_argument('--max_steps'					,				default=10_00					,				type=UpperCAmelCase_					,				help='To calculate training epochs'							)

	parser.add_argument(
	    '--secondary_learner_batch_size'					,				default=1_28					,				type=UpperCAmelCase_					,				help='batch size of training data for secondary learner'					,				)

	parser.add_argument(
	    '--batch_size'					,				default=16					,				type=UpperCAmelCase_					,				help='batch size of training data of language model(gpt2) '							)

	parser.add_argument(
	    '--eval_interval'					,				default=10					,				type=UpperCAmelCase_					,				help=(
	        'decay the selectivity of our secondary learner filter from'
	        '1 standard deviation above average to 1 below average after 10 batches'
	    )					,				)

	parser.add_argument(
	    '--number'					,				default=1_00					,				type=UpperCAmelCase_					,				help='The number of examples split to be used as objective_set/test_data'							)

	parser.add_argument(
	    '--min_len'					,				default=10_26					,				type=UpperCAmelCase_					,				help='The minimum length of the article to be used as objective set'							)

	parser.add_argument(
	    '--secondary_learner_max_epochs'					,				default=15					,				type=UpperCAmelCase_					,				help='number of epochs to train secondary learner'							)

	parser.add_argument('--trim'					,				default=UpperCAmelCase_					,				type=UpperCAmelCase_					,				help='truncate the example if it exceeds context length'							)

	parser.add_argument(
	    '--threshold'					,				default=1.0					,				type=UpperCAmelCase_					,				help=(
	        'The threshold value used by secondary learner to filter the train_data and allow only'
	        ' informative data as input to the model'
	    )					,				)

	parser.add_argument('--finetuned_model_name'					,				default='gpt2_finetuned.pt'					,				type=UpperCAmelCase_					,				help='finetuned_model_name'							)

	parser.add_argument(
	    '--recopy_model'					,				default=UpperCAmelCase_					,				type=UpperCAmelCase_					,				help='Reset the model to the original pretrained GPT-2 weights after each iteration'					,				)

	# function calls
	# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
	generate_n_pairs(
	    context_len=32					,				max_steps=10					,				size_objective_set=1_00					,				min_len=10_26					,				trim=UpperCAmelCase_					,				data_file='data/tokenized_stories_train_wikitext103.jbl'					,				igf_data_file='igf_context_pairs.jbl'					,				)

	# Load train data for secondary learner
	snake_case__	       =   joblib.load('data/IGF_values.jbl'							)

	# Train secondary learner
	snake_case__	       =   training_secondary_learner(
	    UpperCAmelCase_					,				secondary_learner_max_epochs=15					,				secondary_learner_batch_size=1_28					,				eval_freq=1_00					,				igf_model_path='igf_model.pt'					,				)

	# load pretrained gpt2 model
	snake_case__	       =   GPTaLMHeadModel.from_pretrained('gpt2'							)
	set_seed(42							)

	# Generate train and test data to train and evaluate gpt2 model
	snake_case__       ,  snake_case__	       =   generate_datasets(
	    context_len=32					,				file='data/tokenized_stories_train_wikitext103.jbl'					,				number=1_00					,				min_len=10_26					,				trim=UpperCAmelCase_							)

	# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
	finetune(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				context_len=32					,				max_steps=10_00					,				batch_size=16					,				threshold=1.0					,				recopy_model=UpperCAmelCase_					,				secondary_learner=UpperCAmelCase_					,				eval_interval=10					,				finetuned_model_name='gpt2_finetuned.pt'					,				)


if __name__ == "__main__":
			main()










'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__					:			Any          =     logging.get_logger()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = True							)   ->Dict:
	print(f'''Converting {name}...'''							)

	with torch.no_grad():
		if hidden_sizes == 1_28:
			if name[-1] == "S":
				snake_case__	       =   timm.create_model('levit_128s'					,				pretrained=UpperCAmelCase_							)
			else:
				snake_case__	       =   timm.create_model('levit_128'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 1_92:
			snake_case__	       =   timm.create_model('levit_192'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 2_56:
			snake_case__	       =   timm.create_model('levit_256'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 3_84:
			snake_case__	       =   timm.create_model('levit_384'					,				pretrained=UpperCAmelCase_							)

		from_model.eval()
		snake_case__	       =   LevitForImageClassificationWithTeacher(UpperCAmelCase_							).eval()
		snake_case__	       =   OrderedDict()

		snake_case__	       =   from_model.state_dict()
		snake_case__	       =   list(from_model.state_dict().keys()							)
		snake_case__	       =   list(our_model.state_dict().keys()							)
		print(len(UpperCAmelCase_							)					,				len(UpperCAmelCase_							)							)
		for i in range(len(UpperCAmelCase_							)							):
			snake_case__	       =   weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_							)

		snake_case__	       =   torch.randn((2, 3, 2_24, 2_24)							)
		snake_case__	       =   from_model(UpperCAmelCase_							)
		snake_case__	       =   our_model(UpperCAmelCase_							).logits

	assert torch.allclose(UpperCAmelCase_					,				UpperCAmelCase_							), "The model logits don't match the original one."

	snake_case__	       =   name
	print(UpperCAmelCase_							)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name							)
		snake_case__	       =   LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name							)

		print(f'''Pushed {checkpoint_name}'''							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = True							)   ->Any:
	snake_case__	       =   'imagenet-1k-id2label.json'
	snake_case__	       =   10_00
	snake_case__	       =   (1, num_labels)

	snake_case__	       =   'huggingface/label-files'
	snake_case__	       =   num_labels
	snake_case__	       =   json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'							)					,				'r'							)							)
	snake_case__	       =   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}

	snake_case__	       =   idalabel
	snake_case__	       =   {v: k for k, v in idalabel.items()}

	snake_case__	       =   partial(UpperCAmelCase_					,				num_labels=UpperCAmelCase_					,				idalabel=UpperCAmelCase_					,				labelaid=UpperCAmelCase_							)

	snake_case__	       =   {
	    'levit-128S': 1_28,
	    'levit-128': 1_28,
	    'levit-192': 1_92,
	    'levit-256': 2_56,
	    'levit-384': 3_84,
	}

	snake_case__	       =   {
	    'levit-128S': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 6, 8]					,				depths=[2, 3, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-128': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 8, 12]					,				depths=[4, 4, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-192': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_92, 2_88, 3_84]					,				num_attention_heads=[3, 5, 6]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-256': ImageNetPreTrainedConfig(
	        hidden_sizes=[2_56, 3_84, 5_12]					,				num_attention_heads=[4, 6, 8]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-384': ImageNetPreTrainedConfig(
	        hidden_sizes=[3_84, 5_12, 7_68]					,				num_attention_heads=[6, 9, 12]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0.1					,				),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name]					,				UpperCAmelCase_					,				names_to_config[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	return config, expected_shape


if __name__ == "__main__":
			a__					:			Optional[int]          =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default=None,
			    type=str,
			    help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default='''levit-dump-folder/''',
			    type=Path,
			    required=False,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
			parser.add_argument(
			    '''--no-push_to_hub''',
			    dest='''push_to_hub''',
			    action='''store_false''',
			    help='''Do not push model and image processor to the hub''',
			)

			a__					:			Optional[Any]          =     parser.parse_args()
			a__					:			Path          =     args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



