

import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import YolosForObjectDetection, YolosModel
    from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor
class  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=13 ,					_SCREAMING_SNAKE_CASE=[30, 30] ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=32 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=4 ,					_SCREAMING_SNAKE_CASE=37 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=10 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=10 ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     parent
        SCREAMING_SNAKE_CASE_				:  Dict           =     batch_size
        SCREAMING_SNAKE_CASE_				:  Tuple           =     image_size
        SCREAMING_SNAKE_CASE_				:  str           =     patch_size
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_channels
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     is_training
        SCREAMING_SNAKE_CASE_				:  int           =     use_labels
        SCREAMING_SNAKE_CASE_				:  int           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  List[str]           =     intermediate_size
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     hidden_act
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_				:  Any           =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_				:  int           =     type_sequence_label_size
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  int           =     num_labels
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scope
        SCREAMING_SNAKE_CASE_				:  Dict           =     n_targets
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        SCREAMING_SNAKE_CASE_				:  Tuple           =     (image_size[1] // patch_size) * (image_size[0] // patch_size)
        SCREAMING_SNAKE_CASE_				:  int           =     num_patches + 1 + self.num_detection_tokens

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     None
        if self.use_labels:
              # labels is a list of Dict (each Dict being the labels for a given example in the batch)
              SCREAMING_SNAKE_CASE_				:  int           =     []
              for i in range(self.batch_size					):
                    SCREAMING_SNAKE_CASE_				:  Dict           =     {}
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.randint(
                        high=self.num_labels ,					size=(self.n_targets,) ,					device=_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.rand(self.n_targets ,					4 ,					device=_SCREAMING_SNAKE_CASE					)
                    labels.append(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     self.get_config()

        return config, pixel_values, labels

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return YolosConfig(
            image_size=self.image_size ,					patch_size=self.patch_size ,					num_channels=self.num_channels ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					is_decoder=_SCREAMING_SNAKE_CASE ,					initializer_range=self.initializer_range ,					num_detection_tokens=self.num_detection_tokens ,					num_labels=self.num_labels ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     YolosModel(config=_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     model(_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(
            result.last_hidden_state.shape ,					(self.batch_size, self.expected_seq_len, self.hidden_size)					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     YolosForObjectDetection(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()

        SCREAMING_SNAKE_CASE_				:  Tuple           =     model(pixel_values=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     model(_SCREAMING_SNAKE_CASE					)

        self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)					)
        self.parent.assertEqual(result.pred_boxes.shape ,					(self.batch_size, self.num_detection_tokens, 4)					)

        SCREAMING_SNAKE_CASE_				:  str           =     model(pixel_values=_SCREAMING_SNAKE_CASE ,					labels=_SCREAMING_SNAKE_CASE					)

        self.parent.assertEqual(result.loss.shape ,					()					)
        self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)					)
        self.parent.assertEqual(result.pred_boxes.shape ,					(self.batch_size, self.num_detection_tokens, 4)					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     config_and_inputs
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     {'pixel_values': pixel_values}
        return config, inputs_dict

@require_torch
class  _A		(  __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Dict       =  (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE    :      str       =  (
      {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
  )

  SCREAMING_SNAKE_CASE    :      Any       =  False
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  False
  SCREAMING_SNAKE_CASE    :      Tuple       =  False
  SCREAMING_SNAKE_CASE    :      Dict       =  False

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     super()._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					return_labels=_SCREAMING_SNAKE_CASE					)

        if return_labels:
              if model_class.__name__ == "YolosForObjectDetection":
                    SCREAMING_SNAKE_CASE_				:  int           =     []
                    for i in range(self.model_tester.batch_size					):
                          SCREAMING_SNAKE_CASE_				:  List[Any]           =     {}
                          SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.ones(
                              size=(self.model_tester.n_targets,) ,					device=_SCREAMING_SNAKE_CASE ,					dtype=torch.long					)
                          SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.ones(
                              self.model_tester.n_targets ,					4 ,					device=_SCREAMING_SNAKE_CASE ,					dtype=torch.float					)
                          labels.append(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     labels

        return inputs_dict

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     YolosModelTester(self					)
        SCREAMING_SNAKE_CASE_				:  str           =     ConfigTester(self ,					config_class=_SCREAMING_SNAKE_CASE ,					has_text_modality=_SCREAMING_SNAKE_CASE ,					hidden_size=37					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.config_tester.run_common_tests()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Dict           =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_				:  str           =     model_class(_SCREAMING_SNAKE_CASE					)
              self.assertIsInstance(model.get_input_embeddings() ,					(nn.Module)					)
              SCREAMING_SNAKE_CASE_				:  Any           =     model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE ,					nn.Linear					)					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     model_class(_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Tuple           =     inspect.signature(model.forward					)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_				:  Tuple           =     [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_				:  Any           =     ['pixel_values']
              self.assertListEqual(arg_names[:1] ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     True

        # in YOLOS, the seq_len is different
        SCREAMING_SNAKE_CASE_				:  str           =     self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     True
              SCREAMING_SNAKE_CASE_				:  str           =     False
              SCREAMING_SNAKE_CASE_				:  Dict           =     True
              SCREAMING_SNAKE_CASE_				:  Any           =     model_class(_SCREAMING_SNAKE_CASE					)
              model.to(_SCREAMING_SNAKE_CASE					)
              model.eval()
              with torch.no_grad():
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     outputs.attentions
              self.assertEqual(len(_SCREAMING_SNAKE_CASE					) ,					self.model_tester.num_hidden_layers					)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     True
              SCREAMING_SNAKE_CASE_				:  int           =     model_class(_SCREAMING_SNAKE_CASE					)
              model.to(_SCREAMING_SNAKE_CASE					)
              model.eval()
              with torch.no_grad():
                    SCREAMING_SNAKE_CASE_				:  Dict           =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
              SCREAMING_SNAKE_CASE_				:  str           =     outputs.attentions
              self.assertEqual(len(_SCREAMING_SNAKE_CASE					) ,					self.model_tester.num_hidden_layers					)

              self.assertListEqual(
                  list(attentions[0].shape[-3:]					) ,					[self.model_tester.num_attention_heads, seq_len, seq_len] ,					)
              SCREAMING_SNAKE_CASE_				:  str           =     len(_SCREAMING_SNAKE_CASE					)

              # Check attention is always last and order is fine
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     True
              SCREAMING_SNAKE_CASE_				:  List[str]           =     True
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     model_class(_SCREAMING_SNAKE_CASE					)
              model.to(_SCREAMING_SNAKE_CASE					)
              model.eval()
              with torch.no_grad():
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)

              SCREAMING_SNAKE_CASE_				:  Dict           =     1
              self.assertEqual(out_len + added_hidden_states ,					len(_SCREAMING_SNAKE_CASE					)					)

              SCREAMING_SNAKE_CASE_				:  str           =     outputs.attentions

              self.assertEqual(len(_SCREAMING_SNAKE_CASE					) ,					self.model_tester.num_hidden_layers					)
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]					) ,					[self.model_tester.num_attention_heads, seq_len, seq_len] ,					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  Any           =     model_class(_SCREAMING_SNAKE_CASE					)
              model.to(_SCREAMING_SNAKE_CASE					)
              model.eval()

              with torch.no_grad():
                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)

              SCREAMING_SNAKE_CASE_				:  Tuple           =     outputs.hidden_states

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     getattr(
                  self.model_tester ,					'expected_num_hidden_layers' ,					self.model_tester.num_hidden_layers + 1					)
              self.assertEqual(len(_SCREAMING_SNAKE_CASE					) ,					_SCREAMING_SNAKE_CASE					)

              # YOLOS has a different seq_length
              SCREAMING_SNAKE_CASE_				:  int           =     self.model_tester.expected_seq_len

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]					) ,					[seq_length, self.model_tester.hidden_size] ,					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*_SCREAMING_SNAKE_CASE					)



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     YolosModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image



@require_torch
@require_vision
class  _A		(  unittest.TestCase):

  @cached_property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return AutoImageProcessor.from_pretrained('hustvl/yolos-small'					) if is_vision_available() else None



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     YolosForObjectDetection.from_pretrained('hustvl/yolos-small'					).to(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     self.default_image_processor
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     prepare_img()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     image_processor(images=_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).to(_SCREAMING_SNAKE_CASE					)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     model(inputs.pixel_values					)

        # verify outputs
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.Size((1, 100, 92)					)
        self.assertEqual(outputs.logits.shape ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] ,					device=_SCREAMING_SNAKE_CASE ,					)
        SCREAMING_SNAKE_CASE_				:  int           =     torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] ,					device=_SCREAMING_SNAKE_CASE					)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)

        # verify postprocessing
        SCREAMING_SNAKE_CASE_				:  Tuple           =     image_processor.post_process_object_detection(
            _SCREAMING_SNAKE_CASE ,					threshold=0.3 ,					target_sizes=[image.size[::-1]]					)[0]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [75, 75, 17, 63, 17]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]					).to(_SCREAMING_SNAKE_CASE					)

        self.assertEqual(len(results['scores']					) ,					5					)
        self.assertTrue(torch.allclose(results['scores'] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)
        self.assertSequenceEqual(results['labels'].tolist() ,					_SCREAMING_SNAKE_CASE					)
        self.assertTrue(torch.allclose(results['boxes'][0, :] ,					_SCREAMING_SNAKE_CASE					)					)









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()






