

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)

lowerCAmelCase     :   Optional[int]						     =  torch.device('cpu')

def    A_  (							):

						"""simple docstring"""



						SCREAMING_SNAKE_CASE_				:  Dict           =     'http://images.cocodataset.org/val2017/000000039769.jpg'
						SCREAMING_SNAKE_CASE_				:  Dict           =     Image.open(requests.get(a					,   stream=a  ).raw  )
						return im

def    A_  (							a  ):

						"""simple docstring"""



						if swiftformer_name == "swiftformer_xs":
												return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]  )

						elif swiftformer_name == "swiftformer_s":
												return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]  )

						elif swiftformer_name == "swiftformer_l1":
												return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]  )

						elif swiftformer_name == "swiftformer_l3":
												return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]  )

def    A_  (							a					,   a					,   a  ):

						"""simple docstring"""



						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     dct.pop(a  )
						SCREAMING_SNAKE_CASE_				:  Tuple           =     val

def    A_  (							a  ):

						"""simple docstring"""



						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     []
						for k in state_dict.keys():
												SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     k
												if ".pwconv" in k:
																		SCREAMING_SNAKE_CASE_				:  Tuple           =     k_new.replace('.pwconv'					,   '.point_wise_conv'  )
												if ".dwconv" in k:
																		SCREAMING_SNAKE_CASE_				:  List[str]           =     k_new.replace('.dwconv'					,   '.depth_wise_conv'  )
												if ".Proj." in k:
																		SCREAMING_SNAKE_CASE_				:  Dict           =     k_new.replace('.Proj.'					,   '.proj.'  )
												if "patch_embed" in k_new:
																		SCREAMING_SNAKE_CASE_				:  Optional[int]           =     k_new.replace('patch_embed'					,   'swiftformer.patch_embed.patch_embedding'  )
												if "network" in k_new:
																		SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     k_new.split('.'  )
																		if ls[2].isdigit():
																								SCREAMING_SNAKE_CASE_				:  Dict           =     'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]  )
																		else:
																								SCREAMING_SNAKE_CASE_				:  Dict           =     k_new.replace('network'					,   'swiftformer.encoder.network'  )
												rename_keys.append((k, k_new)  )
						return rename_keys

@torch.no_grad()
def    A_  (							a					,   a					,   a  ):

						"""simple docstring"""



						SCREAMING_SNAKE_CASE_				:  str           =     SwiftFormerConfig()

						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						SCREAMING_SNAKE_CASE_				:  str           =     1_0_0_0
						SCREAMING_SNAKE_CASE_				:  List[Any]           =     'huggingface/label-files'
						SCREAMING_SNAKE_CASE_				:  Any           =     'imagenet-1k-id2label.json'
						SCREAMING_SNAKE_CASE_				:  int           =     json.load(open(hf_hub_download(a					,   a					,   repo_type='dataset'  )					,   'r'  )  )
						SCREAMING_SNAKE_CASE_				:  Dict           =     {int(a  ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_				:  List[Any]           =     idalabel
						SCREAMING_SNAKE_CASE_				:  List[str]           =     {v: k for k, v in idalabel.items()}

						# size of the architecture
						if swiftformer_name == "swiftformer_xs":
												SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [3, 3, 6, 4]
												SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [4_8, 5_6, 1_1_2, 2_2_0]

						elif swiftformer_name == "swiftformer_s":
												SCREAMING_SNAKE_CASE_				:  Any           =     [3, 3, 9, 6]
												SCREAMING_SNAKE_CASE_				:  Dict           =     [4_8, 6_4, 1_6_8, 2_2_4]

						elif swiftformer_name == "swiftformer_l1":
												SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [4, 3, 1_0, 5]
												SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [4_8, 9_6, 1_9_2, 3_8_4]

						elif swiftformer_name == "swiftformer_l3":
												SCREAMING_SNAKE_CASE_				:  Any           =     [4, 4, 1_2, 6]
												SCREAMING_SNAKE_CASE_				:  Dict           =     [6_4, 1_2_8, 3_2_0, 5_1_2]

						# load state_dict of original model, remove and rename some keys
						if original_ckpt:
												if original_ckpt.startswith('https'  ):
																		SCREAMING_SNAKE_CASE_				:  Dict           =     torch.hub.load_state_dict_from_url(a					,   map_location='cpu'					,   check_hash=a  )
												else:
																		SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.load(a					,   map_location='cpu'  )
						SCREAMING_SNAKE_CASE_				:  Any           =     checkpoint

						SCREAMING_SNAKE_CASE_				:  List[str]           =     create_rename_keys(a  )
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(a					,   a					,   a  )

						# load HuggingFace model
						SCREAMING_SNAKE_CASE_				:  str           =     SwiftFormerForImageClassification(a  ).eval()
						hf_model.load_state_dict(a  )

						# prepare test inputs
						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     prepare_img()
						SCREAMING_SNAKE_CASE_				:  int           =     ViTImageProcessor.from_pretrained('preprocessor_config'  )
						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     processor(images=a					,   return_tensors='pt'  )

						# compare outputs from both models
						SCREAMING_SNAKE_CASE_				:  Dict           =     get_expected_output(a  )
						SCREAMING_SNAKE_CASE_				:  Dict           =     hf_model(inputs['pixel_values']  ).logits

						assert hf_logits.shape == torch.Size([1, 1_0_0_0]  )
						assert torch.allclose(hf_logits[0, 0:5]					,   a					,   atol=1e-3  )

						Path(a  ).mkdir(exist_ok=a  )
						print(f"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"  )
						hf_model.save_pretrained(a  )


if __name__ == "__main__":
				lowerCAmelCase     :   Union[str, Any]						     =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--swiftformer_name',
				    default='swiftformer_xs',
				    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
				    type=str,
				    help='Name of the SwiftFormer model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='./converted_outputs/',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

				lowerCAmelCase     :   Union[str, Any]						     =  parser.parse_args()
				convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









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    A_  (							a=3_2					,   a=1_0					,   a=1_0_0					,   a=1_0_2_6					,   a=True					,   a="data/tokenized_stories_train_wikitext103.jbl"					,   a="igf_context_pairs.jbl"					,   ):

      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     generate_datasets(
          a					,   a					,   number=a					,   min_len=1_0_2_6					,   trim=a  )
      # 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?
      SCREAMING_SNAKE_CASE_				:  str           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

      # load pretrained model
      SCREAMING_SNAKE_CASE_				:  List[str]           =     load_gpta('gpt2'  ).to(a  )
      print('computing perplexity on objective set'  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     compute_perplexity(a					,   a					,   a  ).item()
      print('perplexity on objective set:'					,   a  )

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

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

def    A_  (							a					,   a=1_5					,   a=1_2_8					,   a=1_0_0					,   a="igf_model.pt"					,   ):

      """simple docstring"""



      set_seed(4_2  )

      # Load pre-trained model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )

      # Initialize secondary learner to use embedding weights of model
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     SecondaryLearner(a  )

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Any           =     train_secondary_learner(
          a					,   a					,   max_epochs=a					,   batch_size=a					,   eval_freq=1_0_0					,   igf_model_path=a					,   )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner

def    A_  (							a					,   a					,   a					,   a=3_2					,   a=1_0_0_0					,   a=1_6					,   a=1.0					,   a=recopy_gpta					,   a=None					,   a=1_0					,   a="gpt2_finetuned.pt"					,   ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     RandomSampler(a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(a					,   sampler=a  )

      SCREAMING_SNAKE_CASE_				:  int           =     max_steps // (len(a  )) + 1
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     0
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.zeros((1, context_len)					,   dtype=torch.long					,   device=a  )
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     recopy_model(a					,   a					,   a  )

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(a  )
            secondary_learner.eval()
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0

      SCREAMING_SNAKE_CASE_				:  List[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  Dict           =     []

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

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

                        # 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 == 1_0:
                              SCREAMING_SNAKE_CASE_				:  Any           =     -1
                        if predicted_q < threshold:
                              SCREAMING_SNAKE_CASE_				:  Tuple           =     False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()  )  )
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     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()
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     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:
                              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     compute_perplexity(a					,   a					,   a  )
                              test_perps.append(a  )

                              print('Test perplexity, step'					,   a					,   ':'					,   a  )
            # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 6_0:
                        break
            if max_steps > 0 and global_step > 6_0:
                  break

    # save finetuned transformer model
      torch.save(model.state_dict()					,   a  )
      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    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'  )

      # Required parameters
      parser.add_argument(
          '--data_dir'					,   default=a					,   type=a					,   required=a					,   help='The input data dir. Should contain data files for WikiText.'					,   )
      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(
          '--data_file'					,   type=a					,   default=a					,   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=a					,   default=a					,   help='A jbl file containing the context and information gain pairs to train secondary learner.'					,   )

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

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

      parser.add_argument(
          '--context_len'					,   default=3_2					,   type=a					,   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_0_0					,   type=a					,   help='number of articles that are long enough to be used as our objective set'					,   )
      parser.add_argument(
          '--eval_freq'					,   default=1_0_0					,   type=a					,   help='secondary model evaluation is triggered at eval_freq'  )

      parser.add_argument('--max_steps'					,   default=1_0_0_0					,   type=a					,   help='To calculate training epochs'  )

      parser.add_argument(
          '--secondary_learner_batch_size'					,   default=1_2_8					,   type=a					,   help='batch size of training data for secondary learner'					,   )

      parser.add_argument(
          '--batch_size'					,   default=1_6					,   type=a					,   help='batch size of training data of language model(gpt2) '  )

      parser.add_argument(
          '--eval_interval'					,   default=1_0					,   type=a					,   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_0_0					,   type=a					,   help='The number of examples split to be used as objective_set/test_data'  )

      parser.add_argument(
          '--min_len'					,   default=1_0_2_6					,   type=a					,   help='The minimum length of the article to be used as objective set'  )

      parser.add_argument(
          '--secondary_learner_max_epochs'					,   default=1_5					,   type=a					,   help='number of epochs to train secondary learner'  )

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

      parser.add_argument(
          '--threshold'					,   default=1.0					,   type=a					,   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=a					,   help='finetuned_model_name'  )

      parser.add_argument(
          '--recopy_model'					,   default=a					,   type=a					,   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=3_2					,   max_steps=1_0					,   size_objective_set=1_0_0					,   min_len=1_0_2_6					,   trim=a					,   data_file='data/tokenized_stories_train_wikitext103.jbl'					,   igf_data_file='igf_context_pairs.jbl'					,   )

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

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     training_secondary_learner(
          a					,   secondary_learner_max_epochs=1_5					,   secondary_learner_batch_size=1_2_8					,   eval_freq=1_0_0					,   igf_model_path='igf_model.pt'					,   )

      # load pretrained gpt2 model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )
      set_seed(4_2  )

      # Generate train and test data to train and evaluate gpt2 model
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[str]           =     generate_datasets(
          context_len=3_2					,   file='data/tokenized_stories_train_wikitext103.jbl'					,   number=1_0_0					,   min_len=1_0_2_6					,   trim=a  )

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          a					,   a					,   a					,   context_len=3_2					,   max_steps=1_0_0_0					,   batch_size=1_6					,   threshold=1.0					,   recopy_model=a					,   secondary_learner=a					,   eval_interval=1_0					,   finetuned_model_name='gpt2_finetuned.pt'					,   )


if __name__ == "__main__":
    main()






