





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_MAPPING,
         BeitForImageClassification,
         BeitForMaskedImageModeling,
         BeitForSemanticSegmentation,
         BeitModel,
     )
     from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     import PIL
     from PIL import Image

     from transformers import BeitImageProcessor

class   A							:



      def __init__(						self:    Any		,	_lowerCAmelCase:    Tuple		,	_lowerCAmelCase:    List[Any]=100		,	_lowerCAmelCase:    Optional[Any]=13		,	_lowerCAmelCase:    str=30		,	_lowerCAmelCase:    str=2		,	_lowerCAmelCase:    str=3		,	_lowerCAmelCase:    List[str]=True		,	_lowerCAmelCase:    Union[str, Any]=True		,	_lowerCAmelCase:    List[str]=32		,	_lowerCAmelCase:    str=4		,	_lowerCAmelCase:    List[str]=4		,	_lowerCAmelCase:    str=37		,	_lowerCAmelCase:    str="gelu"		,	_lowerCAmelCase:    Tuple=0.1		,	_lowerCAmelCase:    Optional[Any]=0.1		,	_lowerCAmelCase:    str=10		,	_lowerCAmelCase:    Optional[int]=0.02		,	_lowerCAmelCase:    Optional[Any]=3		,	_lowerCAmelCase:    Dict=None		,	_lowerCAmelCase:    Any=[0, 1, 2, 3]		,	)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =parent
         UpperCAmelCase_           =100
         UpperCAmelCase_           =batch_size
         UpperCAmelCase_           =image_size
         UpperCAmelCase_           =patch_size
         UpperCAmelCase_           =num_channels
         UpperCAmelCase_           =is_training
         UpperCAmelCase_           =use_labels
         UpperCAmelCase_           =hidden_size
         UpperCAmelCase_           =num_hidden_layers
         UpperCAmelCase_           =num_attention_heads
         UpperCAmelCase_           =intermediate_size
         UpperCAmelCase_           =hidden_act
         UpperCAmelCase_           =hidden_dropout_prob
         UpperCAmelCase_           =attention_probs_dropout_prob
         UpperCAmelCase_           =type_sequence_label_size
         UpperCAmelCase_           =initializer_range
         UpperCAmelCase_           =scope
         UpperCAmelCase_           =out_indices
         UpperCAmelCase_           =num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =(image_size // patch_size) ** 2
         UpperCAmelCase_           =num_patches + 1



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCAmelCase_           =None
         UpperCAmelCase_           =None
         if self.use_labels:
            UpperCAmelCase_           =ids_tensor([self.batch_size]		,	self.type_sequence_label_size							)
            UpperCAmelCase_           =ids_tensor([self.batch_size, self.image_size, self.image_size]		,	self.num_labels							)

         UpperCAmelCase_           =self.get_config()

         return config, pixel_values, labels, pixel_labels



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     List[Any]:
         '''simple docstring'''

         return BeitConfig(
             vocab_size=self.vocab_size		,	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=_lowerCAmelCase		,	initializer_range=self.initializer_range		,	out_indices=self.out_indices		,	)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    Tuple		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    Union[str, Any]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =BeitModel(config=_lowerCAmelCase							)
         model.to(_lowerCAmelCase							)
         model.eval()
         UpperCAmelCase_           =model(_lowerCAmelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)							)



      def 						lowerCAmelCase__     (						self:    List[str]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    Dict							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =BeitForMaskedImageModeling(config=_lowerCAmelCase							)
         model.to(_lowerCAmelCase							)
         model.eval()
         UpperCAmelCase_           =model(_lowerCAmelCase							)
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length - 1, self.vocab_size)							)



      def 						lowerCAmelCase__     (						self:    Optional[Any]		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    Optional[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.type_sequence_label_size
         UpperCAmelCase_           =BeitForImageClassification(_lowerCAmelCase							)
         model.to(_lowerCAmelCase							)
         model.eval()
         UpperCAmelCase_           =model(_lowerCAmelCase		,	labels=_lowerCAmelCase							)
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         UpperCAmelCase_           =1
         UpperCAmelCase_           =BeitForImageClassification(_lowerCAmelCase							)
         model.to(_lowerCAmelCase							)
         model.eval()

         UpperCAmelCase_           =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         UpperCAmelCase_           =model(_lowerCAmelCase		,	labels=_lowerCAmelCase							)
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)							)



      def 						lowerCAmelCase__     (						self:    List[str]		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Any		,	_lowerCAmelCase:    int							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =self.num_labels
         UpperCAmelCase_           =BeitForSemanticSegmentation(_lowerCAmelCase							)
         model.to(_lowerCAmelCase							)
         model.eval()
         UpperCAmelCase_           =model(_lowerCAmelCase							)
         self.parent.assertEqual(
             result.logits.shape		,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)
         UpperCAmelCase_           =model(_lowerCAmelCase		,	labels=_lowerCAmelCase							)
         self.parent.assertEqual(
             result.logits.shape		,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)






      def 						lowerCAmelCase__     (						self:    Any							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.prepare_config_and_inputs()
         UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_           =config_and_inputs
         UpperCAmelCase_           ={"pixel_values": pixel_values}
         return config, inputs_dict

@require_torch
class   A							(						__lowercase		,   __lowercase		,   unittest.TestCase    ):
      _snake_case				  =(
          (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      _snake_case				  =(
          {
              '''feature-extraction''': BeitModel,
              '''image-classification''': BeitForImageClassification,
              '''image-segmentation''': BeitForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _snake_case				  =False
      _snake_case				  =False
      _snake_case				  =False



      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =BeitModelTester(self							)
         UpperCAmelCase_           =ConfigTester(self		,	config_class=_lowerCAmelCase		,	has_text_modality=_lowerCAmelCase		,	hidden_size=37							)



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     int:
         '''simple docstring'''

         self.config_tester.run_common_tests()



      @unittest.skip(reason="BEiT does not use inputs_embeds"							)
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Any:
         '''simple docstring'''

         pass



      @require_torch_multi_gpu
      @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"							)
      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Any:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    str							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_    ,							UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs_for_common()

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



      def 						lowerCAmelCase__     (						self:    str							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_    ,							UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCAmelCase_           =["pixel_values"]
            self.assertListEqual(arg_names[:1]		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    str							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Union[str, Any]:
         '''simple docstring'''

         if not self.model_tester.is_training:
            return

         UpperCAmelCase_    ,							UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_           =True

         for model_class in self.all_model_classes:
            # we don't test BeitForMaskedImageModeling
            if model_class in [*get_values(_lowerCAmelCase							), BeitForMaskedImageModeling]:
               continue

            UpperCAmelCase_           =model_class(_lowerCAmelCase							)
            model.to(_lowerCAmelCase							)
            model.train()
            UpperCAmelCase_           =self._prepare_for_class(_lowerCAmelCase		,	_lowerCAmelCase		,	return_labels=_lowerCAmelCase							)
            UpperCAmelCase_           =model(**_lowerCAmelCase							).loss
            loss.backward()



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_    ,							UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
            return

         UpperCAmelCase_           =False
         UpperCAmelCase_           =True

         for model_class in self.all_model_classes:
            # we don't test BeitForMaskedImageModeling
            if (
                model_class in [*get_values(_lowerCAmelCase							), BeitForMaskedImageModeling]
                or not model_class.supports_gradient_checkpointing
            ):
               continue

            UpperCAmelCase_           =model_class(_lowerCAmelCase							)
            model.gradient_checkpointing_enable()
            model.to(_lowerCAmelCase							)
            model.train()
            UpperCAmelCase_           =self._prepare_for_class(_lowerCAmelCase		,	_lowerCAmelCase		,	return_labels=_lowerCAmelCase							)
            UpperCAmelCase_           =model(**_lowerCAmelCase							).loss
            loss.backward()



      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_    ,							UpperCAmelCase_           =self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase_           =_config_zero_init(_lowerCAmelCase							)
         for model_class in self.all_model_classes:
            UpperCAmelCase_           =model_class(config=_lowerCAmelCase							)
            for name, param in model.named_parameters():
               # we skip lambda parameters as these require special initial values
               # determined by config.layer_scale_init_value
               if "lambda" in name:
                  continue
               if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item()		,	[0.0, 1.0]		,	msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,	)






      @slow
      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     List[Any]:
         '''simple docstring'''

         for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_           =BeitModel.from_pretrained(_lowerCAmelCase							)
            self.assertIsNotNone(_lowerCAmelCase							)




def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_           =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
   return image





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



      @cached_property
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     List[Any]:
         '''simple docstring'''

         return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"							) if is_vision_available() else None



      @slow
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"							).to(_lowerCAmelCase							)

         UpperCAmelCase_           =self.default_image_processor
         UpperCAmelCase_           =prepare_img()
         UpperCAmelCase_           =image_processor(images=_lowerCAmelCase		,	return_tensors="pt"							).pixel_values.to(_lowerCAmelCase							)

         # prepare bool_masked_pos
         UpperCAmelCase_           =torch.ones((1, 196)		,	dtype=torch.bool							).to(_lowerCAmelCase							)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase_           =model(pixel_values=_lowerCAmelCase		,	bool_masked_pos=_lowerCAmelCase							)
         UpperCAmelCase_           =outputs.logits

         # verify the logits
         UpperCAmelCase_           =torch.Size((1, 196, 8192)							)
         self.assertEqual(logits.shape		,	_lowerCAmelCase							)

         UpperCAmelCase_           =torch.tensor(
             [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]							).to(_lowerCAmelCase							)

         self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]		,	_lowerCAmelCase		,	atol=1e-2							)							)



      @slow
      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"							).to(_lowerCAmelCase							)

         UpperCAmelCase_           =self.default_image_processor
         UpperCAmelCase_           =prepare_img()
         UpperCAmelCase_           =image_processor(images=_lowerCAmelCase		,	return_tensors="pt"							).to(_lowerCAmelCase							)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase_           =model(**_lowerCAmelCase							)
         UpperCAmelCase_           =outputs.logits

         # verify the logits
         UpperCAmelCase_           =torch.Size((1, 1000)							)
         self.assertEqual(logits.shape		,	_lowerCAmelCase							)

         UpperCAmelCase_           =torch.tensor([-1.23_85, -1.09_87, -1.01_08]							).to(_lowerCAmelCase							)

         self.assertTrue(torch.allclose(logits[0, :3]		,	_lowerCAmelCase		,	atol=1e-4							)							)

         UpperCAmelCase_           =281
         self.assertEqual(logits.argmax(-1							).item()		,	_lowerCAmelCase							)



      @slow
      def 						lowerCAmelCase__     (						self:    str							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"							).to(
             _lowerCAmelCase							)

         UpperCAmelCase_           =self.default_image_processor
         UpperCAmelCase_           =prepare_img()
         UpperCAmelCase_           =image_processor(images=_lowerCAmelCase		,	return_tensors="pt"							).to(_lowerCAmelCase							)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase_           =model(**_lowerCAmelCase							)
         UpperCAmelCase_           =outputs.logits

         # verify the logits
         UpperCAmelCase_           =torch.Size((1, 2_1841)							)
         self.assertEqual(logits.shape		,	_lowerCAmelCase							)

         UpperCAmelCase_           =torch.tensor([1.68_81, -0.27_87, 0.59_01]							).to(_lowerCAmelCase							)

         self.assertTrue(torch.allclose(logits[0, :3]		,	_lowerCAmelCase		,	atol=1e-4							)							)

         UpperCAmelCase_           =2396
         self.assertEqual(logits.argmax(-1							).item()		,	_lowerCAmelCase							)



      @slow
      def 						lowerCAmelCase__     (						self:    str							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"							)
         UpperCAmelCase_           =model.to(_lowerCAmelCase							)

         UpperCAmelCase_           =BeitImageProcessor(do_resize=_lowerCAmelCase		,	size=640		,	do_center_crop=_lowerCAmelCase							)

         UpperCAmelCase_           =load_dataset("hf-internal-testing/fixtures_ade20k"		,	split="test"							)
         UpperCAmelCase_           =Image.open(ds[0]["file"]							)
         UpperCAmelCase_           =image_processor(images=_lowerCAmelCase		,	return_tensors="pt"							).to(_lowerCAmelCase							)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase_           =model(**_lowerCAmelCase							)
         UpperCAmelCase_           =outputs.logits

         # verify the logits
         UpperCAmelCase_           =torch.Size((1, 150, 160, 160)							)
         self.assertEqual(logits.shape		,	_lowerCAmelCase							)

         UpperCAmelCase_           =version.parse(PIL.__version__							) < version.parse("9.0.0"							)

         if is_pillow_less_than_a:
            UpperCAmelCase_           =torch.tensor(
                [
                    [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                    [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                    [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                ]		,	device=_lowerCAmelCase		,	)
         else:
            UpperCAmelCase_           =torch.tensor(
                [
                    [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
                    [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
                    [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
                ]		,	device=_lowerCAmelCase		,	)

         self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,	_lowerCAmelCase		,	atol=1e-4							)							)






      @slow
      def 						lowerCAmelCase__     (						self:    int							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"							)
         UpperCAmelCase_           =model.to(_lowerCAmelCase							)

         UpperCAmelCase_           =BeitImageProcessor(do_resize=_lowerCAmelCase		,	size=640		,	do_center_crop=_lowerCAmelCase							)

         UpperCAmelCase_           =load_dataset("hf-internal-testing/fixtures_ade20k"		,	split="test"							)
         UpperCAmelCase_           =Image.open(ds[0]["file"]							)
         UpperCAmelCase_           =image_processor(images=_lowerCAmelCase		,	return_tensors="pt"							).to(_lowerCAmelCase							)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase_           =model(**_lowerCAmelCase							)

         UpperCAmelCase_           =outputs.logits.detach().cpu()

         UpperCAmelCase_           =image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase		,	target_sizes=[(500, 300)]							)
         UpperCAmelCase_           =torch.Size((500, 300)							)
         self.assertEqual(segmentation[0].shape		,	_lowerCAmelCase							)

         UpperCAmelCase_           =image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase							)
         UpperCAmelCase_           =torch.Size((160, 160)							)
         self.assertEqual(segmentation[0].shape		,	_lowerCAmelCase							)












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      _snake_case     (__lowercase=32     ,   __lowercase=10     ,   __lowercase=100     ,   __lowercase=1026     ,   __lowercase=True     ,   __lowercase="data/tokenized_stories_train_wikitext103.jbl"     ,   __lowercase="igf_context_pairs.jbl"     ,   ):
   set_seed(3)
   # generate train_data and objective_set
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       __lowercase     ,   __lowercase     ,   number=__lowercase     ,   min_len=1026     ,   trim=__lowercase)
   # 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?
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

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

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

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






def      _snake_case     (__lowercase     ,   __lowercase=15     ,   __lowercase=128     ,   __lowercase=100     ,   __lowercase="igf_model.pt"     ,   ):
   set_seed(42)

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

   # Initialize secondary learner to use embedding weights of model
   UpperCamelCase_						=  SecondaryLearner(__lowercase)

   # Train secondary learner
   UpperCamelCase_						=  train_secondary_learner(
       __lowercase     ,   __lowercase     ,   max_epochs=__lowercase     ,   batch_size=__lowercase     ,   eval_freq=100     ,   igf_model_path=__lowercase     ,   )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=32     ,   __lowercase=1000     ,   __lowercase=16     ,   __lowercase=1.0     ,   __lowercase=recopy_gpta     ,   __lowercase=None     ,   __lowercase=10     ,   __lowercase="gpt2_finetuned.pt"     ,   ):
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
   UpperCamelCase_						=  RandomSampler(__lowercase)
   UpperCamelCase_						=  DataLoader(__lowercase     ,   sampler=__lowercase)

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

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

   UpperCamelCase_						=  []
   UpperCamelCase_						=  []

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

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

            # 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:
               UpperCamelCase_						=  -1
            if predicted_q < threshold:
               UpperCamelCase_						=  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()))
            UpperCamelCase_						=  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()
            UpperCamelCase_						=  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:
               UpperCamelCase_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase)
               test_perps.append(__lowercase)

               print('Test perplexity, step'     ,   __lowercase     ,   ':'     ,   __lowercase)
            # 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()     ,   __lowercase)
   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      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task')

   # Required parameters
   parser.add_argument(
       '--data_dir'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='The input data dir. Should contain data files for WikiText.'     ,   )
   parser.add_argument(
       '--model_name_or_path'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
   parser.add_argument(
       '--data_file'     ,   type=__lowercase     ,   default=__lowercase     ,   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=__lowercase     ,   default=__lowercase     ,   help='A jbl file containing the context and information gain pairs to train secondary learner.'     ,   )

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

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

   parser.add_argument(
       '--context_len'     ,   default=32     ,   type=__lowercase     ,   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=100     ,   type=__lowercase     ,   help='number of articles that are long enough to be used as our objective set'     ,   )
   parser.add_argument(
       '--eval_freq'     ,   default=100     ,   type=__lowercase     ,   help='secondary model evaluation is triggered at eval_freq')

   parser.add_argument('--max_steps'     ,   default=1000     ,   type=__lowercase     ,   help='To calculate training epochs')

   parser.add_argument(
       '--secondary_learner_batch_size'     ,   default=128     ,   type=__lowercase     ,   help='batch size of training data for secondary learner'     ,   )

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

   parser.add_argument(
       '--eval_interval'     ,   default=10     ,   type=__lowercase     ,   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=100     ,   type=__lowercase     ,   help='The number of examples split to be used as objective_set/test_data')

   parser.add_argument(
       '--min_len'     ,   default=1026     ,   type=__lowercase     ,   help='The minimum length of the article to be used as objective set')

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

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

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

   parser.add_argument(
       '--recopy_model'     ,   default=__lowercase     ,   type=__lowercase     ,   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=100     ,   min_len=1026     ,   trim=__lowercase     ,   data_file='data/tokenized_stories_train_wikitext103.jbl'     ,   igf_data_file='igf_context_pairs.jbl'     ,   )

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

   # Train secondary learner
   UpperCamelCase_						=  training_secondary_learner(
       __lowercase     ,   secondary_learner_max_epochs=15     ,   secondary_learner_batch_size=128     ,   eval_freq=100     ,   igf_model_path='igf_model.pt'     ,   )

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

   # Generate train and test data to train and evaluate gpt2 model
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       context_len=32     ,   file='data/tokenized_stories_train_wikitext103.jbl'     ,   number=100     ,   min_len=1026     ,   trim=__lowercase)

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       __lowercase     ,   __lowercase     ,   __lowercase     ,   context_len=32     ,   max_steps=1000     ,   batch_size=16     ,   threshold=1.0     ,   recopy_model=__lowercase     ,   secondary_learner=__lowercase     ,   eval_interval=10     ,   finetuned_model_name='gpt2_finetuned.pt'     ,   )


if __name__ == "__main__":
 main()





