






'''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        _SCREAMING_SNAKE_CASE  (				A  :       Tuple=32  ,		A  :       Optional[int]=10  ,		A  :       Optional[int]=1_00  ,		A  :       List[str]=10_26  ,		A  :       List[str]=True  ,		A  :       List[str]="data/tokenized_stories_train_wikitext103.jbl"  ,		A  :       List[Any]="igf_context_pairs.jbl"  ,		)    -> Optional[Any]:







   """simple docstring"""
   set_seed(3  )
   # generate train_data and objective_set
   __snake_case							,__snake_case				:      str     							= generate_datasets(
       A  ,		A  ,		number=A  ,		min_len=10_26  ,		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?
   __snake_case				:      int     							= torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

   # load pretrained model
   __snake_case				:      str     							= load_gpta('gpt2'  ).to(A  )
   print('computing perplexity on objective set'  )
   __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       Optional[int]=15  ,		A  :       List[Any]=1_28  ,		A  :       Tuple=1_00  ,		A  :       Tuple="igf_model.pt"  ,		)    -> Dict:







   """simple docstring"""
   set_seed(42  )

   # Load pre-trained model
   __snake_case				:      Dict     							= GPTaLMHeadModel.from_pretrained('gpt2'  )

   # Initialize secondary learner to use embedding weights of model
   __snake_case				:      List[Any]     							= SecondaryLearner(A  )

   # Train secondary learner
   __snake_case				:      str     							= train_secondary_learner(
       A  ,		A  ,		max_epochs=A  ,		batch_size=A  ,		eval_freq=1_00  ,		igf_model_path=A  ,		)

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       List[str]  ,		A  :       Dict  ,		A  :       Any=32  ,		A  :       Dict=10_00  ,		A  :       List[Any]=16  ,		A  :       Dict=1.0  ,		A  :       Union[str, Any]=recopy_gpta  ,		A  :       int=None  ,		A  :       Optional[int]=10  ,		A  :       Tuple="gpt2_finetuned.pt"  ,		)    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
   __snake_case				:      Any     							= RandomSampler(A  )
   __snake_case				:      List[Any]     							= DataLoader(A  ,		sampler=A  )

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

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(A  )
      secondary_learner.eval()
   __snake_case				:      Dict     							= []
   __snake_case				:      Dict     							= 0

   __snake_case				:      Any     							= []
   __snake_case				:      Optional[int]     							= []

   # Compute the performance of the transformer model at the beginning
   __snake_case				:      List[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()
         __snake_case				:      Optional[int]     							= random.randint(0  ,		example.size(2  ) - context_len - 1  )
         __snake_case				:      int     							= example[0, 0, start : start + context_len]
         lm_optimizer.zero_grad()
         __snake_case				:      Any     							= model(A  ,		labels=A  )
         __snake_case				:      Tuple     							= True

         if secondary_learner is not None:
            __snake_case				:      Optional[int]     							= 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 == 10:
               __snake_case				:      Dict     							= -1
            if predicted_q < threshold:
               __snake_case				:      Tuple     							= False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()  )  )
            __snake_case				:      Optional[int]     							= 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				:      Any     							= 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				:      Tuple     							= 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 > 60:
            break
      if max_steps > 0 and global_step > 60:
         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        _SCREAMING_SNAKE_CASE  (				)    -> Dict:







   """simple docstring"""
   __snake_case				:      str     							= 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=32  ,		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_00  ,		type=A  ,		help='number of articles that are long enough to be used as our objective set'  ,		)
   parser.add_argument(
       '--eval_freq'  ,		default=1_00  ,		type=A  ,		help='secondary model evaluation is triggered at eval_freq'  )

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

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

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

   parser.add_argument(
       '--eval_interval'  ,		default=10  ,		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_00  ,		type=A  ,		help='The number of examples split to be used as objective_set/test_data'  )

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

   parser.add_argument(
       '--secondary_learner_max_epochs'  ,		default=15  ,		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=32  ,		max_steps=10  ,		size_objective_set=1_00  ,		min_len=10_26  ,		trim=A  ,		data_file='data/tokenized_stories_train_wikitext103.jbl'  ,		igf_data_file='igf_context_pairs.jbl'  ,		)

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

   # Train secondary learner
   __snake_case				:      Tuple     							= training_secondary_learner(
       A  ,		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				:      List[Any]     							= GPTaLMHeadModel.from_pretrained('gpt2'  )
   set_seed(42  )

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

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


if __name__ == "__main__":
      main()







'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor




class a_    (    unittest.TestCase							):







   def __init__(self					,   __a					,   __a=7					,   __a=3					,   __a=1_8					,   __a=3_0					,   __a=4_0_0					,   __a=True					,   __a=None					,   __a=True					,   __a=None					,   __a=True					,   __a=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,   __a=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,   __a=True					,   )			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Tuple     							= size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
      __snake_case				:      Any     							= crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      __snake_case				:      Optional[int]     							= parent
      __snake_case				:      Dict     							= batch_size
      __snake_case				:      str     							= num_channels
      __snake_case				:      Optional[Any]     							= image_size
      __snake_case				:      Optional[int]     							= min_resolution
      __snake_case				:      Tuple     							= max_resolution
      __snake_case				:      Optional[int]     							= do_resize
      __snake_case				:      Optional[int]     							= size
      __snake_case				:      Union[str, Any]     							= do_center_crop
      __snake_case				:      List[Any]     							= crop_size
      __snake_case				:      int     							= do_normalize
      __snake_case				:      Optional[Any]     							= image_mean
      __snake_case				:      str     							= image_std
      __snake_case				:      Optional[Any]     							= do_convert_rgb







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=False					,   __a=False					,   __a=False)			->      List[str]:
      """simple docstring"""






      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         __snake_case				:      Optional[int]     							= []
         for i in range(self.batch_size):
            image_inputs.append(
                np.random.randint(
                    2_5_5					,   size=(self.num_channels, self.max_resolution, self.max_resolution)					,   dtype=np.uinta))
      else:
         __snake_case				:      Dict     							= []
         for i in range(self.batch_size):
            __snake_case							,__snake_case				:      Optional[Any]     							= np.random.choice(np.arange(self.min_resolution					,   self.max_resolution)					,   2)
            image_inputs.append(np.random.randint(2_5_5					,   size=(self.num_channels, width, height)					,   dtype=np.uinta))

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         __snake_case				:      int     							= [Image.fromarray(np.moveaxis(__a					,   0					,   -1)) for x in image_inputs]

      if torchify:
         __snake_case				:      List[Any]     							= [torch.from_numpy(__a) for x in image_inputs]

      return image_inputs




@require_torch
@require_vision
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= ChineseCLIPImageProcessingTester(self					,   do_center_crop=__a)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      int     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size					,   {'height': 2_2_4, 'width': 2_2_4})
      self.assertEqual(image_processor.crop_size					,   {'height': 1_8, 'width': 1_8})

      __snake_case				:      List[str]     							= self.image_processing_class.from_dict(self.image_processor_dict					,   size=4_2					,   crop_size=8_4)
      self.assertEqual(image_processor.size					,   {'shortest_edge': 4_2})
      self.assertEqual(image_processor.crop_size					,   {'height': 8_4, 'width': 8_4})







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Optional[Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

      # Test not batched input
      __snake_case				:      int     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      List[Any]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      __snake_case				:      Optional[int]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   numpify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   np.ndarray)

      # Test not batched input
      __snake_case				:      List[Any]     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      int     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      __snake_case				:      Tuple     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   torchify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   torch.Tensor)

      # Test not batched input
      __snake_case				:      Any     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      Union[str, Any]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )




@require_torch
@require_vision
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= ChineseCLIPImageProcessingTester(self					,   num_channels=4					,   do_center_crop=__a)
      __snake_case				:      List[Any]     							= 3







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Union[str, Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

      # Test not batched input
      __snake_case				:      Optional[int]     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      Optional[int]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )