"""simple docstring"""





import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
			    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    LayoutLMvaConfig,
			    LayoutLMvaForQuestionAnswering,
			    LayoutLMvaForSequenceClassification,
			    LayoutLMvaForTokenClassification,
			    LayoutLMvaModel,
			)
			from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import LayoutLMvaImageProcessor




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							Optional[Any]      ,    _A							:							int      ,    _A							:							int=2      ,    _A							:							Optional[int]=3      ,    _A							:							Any=4      ,    _A							:							Optional[Any]=2      ,    _A							:							str=7      ,    _A							:							List[str]=True      ,    _A							:							Optional[Any]=True      ,    _A							:							Optional[Any]=True      ,    _A							:							List[str]=True      ,    _A							:							List[str]=9_9      ,    _A							:							Tuple=3_6      ,    _A							:							Union[str, Any]=3      ,    _A							:							Optional[int]=4      ,    _A							:							List[Any]=3_7      ,    _A							:							List[str]="gelu"      ,    _A							:							Dict=0.1      ,    _A							:							List[str]=0.1      ,    _A							:							Optional[int]=5_1_2      ,    _A							:							Optional[int]=1_6      ,    _A							:							Optional[Any]=2      ,    _A							:							int=0.02      ,    _A							:							Optional[Any]=6      ,    _A							:							Optional[int]=6      ,    _A							:							Any=3      ,    _A							:							Union[str, Any]=4      ,    _A							:							Union[str, Any]=None      ,    _A							:							Dict=1_0_0_0      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    parent
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    num_channels
											_SCREAMING_SNAKE_CASE		:						str			  =    image_size
											_SCREAMING_SNAKE_CASE		:						str			  =    patch_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    text_seq_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    is_training
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    use_input_mask
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    use_token_type_ids
											_SCREAMING_SNAKE_CASE		:						int			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Dict			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						str			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Any			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						Any			  =    type_vocab_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    type_sequence_label_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						int			  =    coordinate_size
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    shape_size
											_SCREAMING_SNAKE_CASE		:						str			  =    num_labels
											_SCREAMING_SNAKE_CASE		:						Dict			  =    num_choices
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    scope
											_SCREAMING_SNAKE_CASE		:						Dict			  =    range_bbox

											# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
											_SCREAMING_SNAKE_CASE		:						Any			  =    text_seq_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (image_size // patch_size) ** 2 + 1
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.text_seq_length + self.image_seq_length





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ids_tensor([self.batch_size, self.text_seq_length]      ,    self.vocab_size)

											_SCREAMING_SNAKE_CASE		:						int			  =    ids_tensor([self.batch_size, self.text_seq_length, 4]      ,    self.range_bbox)
											# Ensure that bbox is legal
											for i in range(bbox.shape[0]):
																		for j in range(bbox.shape[1]):
																									if bbox[i, j, 3] < bbox[i, j, 1]:
																																_SCREAMING_SNAKE_CASE		:						Dict			  =    bbox[i, j, 3]
																																_SCREAMING_SNAKE_CASE		:						int			  =    bbox[i, j, 1]
																																_SCREAMING_SNAKE_CASE		:						Dict			  =    t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																																_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    bbox[i, j, 2]
																																_SCREAMING_SNAKE_CASE		:						str			  =    bbox[i, j, 0]
																																_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    t

											_SCREAMING_SNAKE_CASE		:						Dict			  =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    None
											if self.use_input_mask:
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    random_attention_mask([self.batch_size, self.text_seq_length])

											_SCREAMING_SNAKE_CASE		:						Dict			  =    None
											if self.use_token_type_ids:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor([self.batch_size, self.text_seq_length]      ,    self.type_vocab_size)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
											if self.use_labels:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    ids_tensor([self.batch_size]      ,    self.type_sequence_label_size)
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ids_tensor([self.batch_size, self.text_seq_length]      ,    self.num_labels)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    LayoutLMvaConfig(
											    vocab_size=self.vocab_size      ,    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      ,    max_position_embeddings=self.max_position_embeddings      ,    type_vocab_size=self.type_vocab_size      ,    initializer_range=self.initializer_range      ,    coordinate_size=self.coordinate_size      ,    shape_size=self.shape_size      ,    input_size=self.image_size      ,    patch_size=self.patch_size      ,    )

											return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Tuple      ,    _A							:							str      ,    _A							:							List[str]      ,    _A							:							Any      ,    _A							:							Optional[int]      ,    _A							:							List[str]      ,    _A							:							Any      ,    _A							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    LayoutLMvaModel(config=UpperCAmelCase__)
											model.to(UpperCAmelCase__)
											model.eval()

											# text + image
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(UpperCAmelCase__      ,    pixel_values=UpperCAmelCase__)
											_SCREAMING_SNAKE_CASE		:						int			  =    model(
											    UpperCAmelCase__      ,    bbox=UpperCAmelCase__      ,    pixel_values=UpperCAmelCase__      ,    attention_mask=UpperCAmelCase__      ,    token_type_ids=UpperCAmelCase__)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    model(UpperCAmelCase__      ,    bbox=UpperCAmelCase__      ,    pixel_values=UpperCAmelCase__      ,    token_type_ids=UpperCAmelCase__)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(UpperCAmelCase__      ,    bbox=UpperCAmelCase__      ,    pixel_values=UpperCAmelCase__)

											self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size))

											# text only
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(UpperCAmelCase__)
											self.parent.assertEqual(
											    result.last_hidden_state.shape      ,    (self.batch_size, self.text_seq_length, self.hidden_size))

											# image only
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(pixel_values=UpperCAmelCase__)
											self.parent.assertEqual(
											    result.last_hidden_state.shape      ,    (self.batch_size, self.image_seq_length, self.hidden_size))





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							List[Any]      ,    _A							:							Optional[int]      ,    _A							:							str      ,    _A							:							Dict      ,    _A							:							Optional[int]      ,    _A							:							int      ,    _A							:							Union[str, Any]      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    LayoutLMvaForSequenceClassification(UpperCAmelCase__)
											model.to(UpperCAmelCase__)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(
											    UpperCAmelCase__      ,    bbox=UpperCAmelCase__      ,    pixel_values=UpperCAmelCase__      ,    attention_mask=UpperCAmelCase__      ,    token_type_ids=UpperCAmelCase__      ,    labels=UpperCAmelCase__      ,    )
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels))





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							Union[str, Any]      ,    _A							:							Tuple      ,    _A							:							List[str]      ,    _A							:							List[str]      ,    _A							:							Union[str, Any]      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						Dict			  =    LayoutLMvaForTokenClassification(config=UpperCAmelCase__)
											model.to(UpperCAmelCase__)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    model(
											    UpperCAmelCase__      ,    bbox=UpperCAmelCase__      ,    pixel_values=UpperCAmelCase__      ,    attention_mask=UpperCAmelCase__      ,    token_type_ids=UpperCAmelCase__      ,    labels=UpperCAmelCase__      ,    )
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.text_seq_length, self.num_labels))





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							List[str]      ,    _A							:							Optional[int]      ,    _A							:							Tuple      ,    _A							:							List[Any]      ,    _A							:							Optional[Any]      ,    _A							:							Any      ,    _A							:							List[Any]      ,    _A							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    LayoutLMvaForQuestionAnswering(config=UpperCAmelCase__)
											model.to(UpperCAmelCase__)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(
											    UpperCAmelCase__      ,    bbox=UpperCAmelCase__      ,    pixel_values=UpperCAmelCase__      ,    attention_mask=UpperCAmelCase__      ,    token_type_ids=UpperCAmelCase__      ,    start_positions=UpperCAmelCase__      ,    end_positions=UpperCAmelCase__      ,    )
											self.parent.assertEqual(result.start_logits.shape      ,    (self.batch_size, self.seq_length))
											self.parent.assertEqual(result.end_logits.shape      ,    (self.batch_size, self.seq_length))





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.prepare_config_and_inputs()
											(
											    _SCREAMING_SNAKE_CASE  
											)		:						Optional[Any]			  =    config_and_inputs
											_SCREAMING_SNAKE_CASE		:						str			  =    {
											    '''input_ids''': input_ids,
											    '''bbox''': bbox,
											    '''pixel_values''': pixel_values,
											    '''token_type_ids''': token_type_ids,
											    '''attention_mask''': input_mask,
											}
											return config, inputs_dict




@require_torch
class  _snake_case		(    lowercase__   ,       lowercase__   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						False
				a			=						False
				a			=						False

				a			=						(
				    (
				        LayoutLMvaModel,
				        LayoutLMvaForSequenceClassification,
				        LayoutLMvaForTokenClassification,
				        LayoutLMvaForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				a			=						(
				    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
				    if is_torch_available()
				    else {}
				)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int      ,    _A							:							Optional[int]      ,    _A							:							List[Any]      ,    _A							:							List[str]      ,    _A							:							Tuple):



											"""simple docstring"""







											return True





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    LayoutLMvaModelTester(self)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ConfigTester(self      ,    config_class=UpperCAmelCase__      ,    hidden_size=3_7)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							List[Any]      ,    _A							:							Optional[int]      ,    _A							:							Dict=False):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    copy.deepcopy(UpperCAmelCase__)
											if model_class in get_values(UpperCAmelCase__):
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {
																		    k: v.unsqueeze(1).expand(-1      ,    self.model_tester.num_choices      ,    -1).contiguous()
																		    if isinstance(UpperCAmelCase__      ,    torch.Tensor) and v.ndim > 1
																		    else v
																		    for k, v in inputs_dict.items()
																		}
											if return_labels:
																		if model_class in get_values(UpperCAmelCase__):
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.ones(self.model_tester.batch_size      ,    dtype=torch.long      ,    device=UpperCAmelCase__)
																		elif model_class in get_values(UpperCAmelCase__):
																									_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.zeros(
																									    self.model_tester.batch_size      ,    dtype=torch.long      ,    device=UpperCAmelCase__)
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.zeros(
																									    self.model_tester.batch_size      ,    dtype=torch.long      ,    device=UpperCAmelCase__)
																		elif model_class in [
																		    *get_values(UpperCAmelCase__),
																		]:
																									_SCREAMING_SNAKE_CASE		:						Any			  =    torch.zeros(
																									    self.model_tester.batch_size      ,    dtype=torch.long      ,    device=UpperCAmelCase__)
																		elif model_class in [
																		    *get_values(UpperCAmelCase__),
																		]:
																									_SCREAMING_SNAKE_CASE		:						Any			  =    torch.zeros(
																									    (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,    dtype=torch.long      ,    device=UpperCAmelCase__      ,    )

											return inputs_dict





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											self.config_tester.run_common_tests()





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase__)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    type
																		self.model_tester.create_and_check_model(*UpperCAmelCase__)





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__)





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







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





				@slow
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    LayoutLMvaModel.from_pretrained(UpperCAmelCase__)
																		self.assertIsNotNone(UpperCAmelCase__)



def      lowerCamelCase_()->    List[str]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
							return image




@require_torch
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@cached_property
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__) if is_vision_available() else None





				@slow
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""").to(UpperCAmelCase__)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.default_image_processor
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    prepare_img()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_processor(images=UpperCAmelCase__      ,    return_tensors="""pt""").pixel_values.to(UpperCAmelCase__)

											_SCREAMING_SNAKE_CASE		:						Any			  =    torch.tensor([[1, 2]])
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

											# forward pass
											_SCREAMING_SNAKE_CASE		:						int			  =    model(
											    input_ids=input_ids.to(UpperCAmelCase__)      ,    bbox=bbox.to(UpperCAmelCase__)      ,    pixel_values=pixel_values.to(UpperCAmelCase__)      ,    )

											# verify the logits
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.Size((1, 1_9_9, 7_6_8))
											self.assertEqual(outputs.last_hidden_state.shape      ,    UpperCAmelCase__)

											_SCREAMING_SNAKE_CASE		:						str			  =    torch.tensor(
											    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]).to(UpperCAmelCase__)

											self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,    UpperCAmelCase__      ,    atol=1e-4))
"""simple docstring"""





import argparse
import random

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

from transformers import GPTaLMHeadModel



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE=1_026     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"     ,	__SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"     ,	)->    Union[str, Any]:
							set_seed(3  )
							# generate train_data and objective_set
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    generate_datasets(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	number=__SCREAMING_SNAKE_CASE     ,	min_len=1_026     ,	trim=__SCREAMING_SNAKE_CASE  )
							# 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		:						Dict			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )

							# load pretrained model
							_SCREAMING_SNAKE_CASE		:						Any			  =    load_gpta("""gpt2"""  ).to(__SCREAMING_SNAKE_CASE  )
							print("""computing perplexity on objective set"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    compute_perplexity(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).item()
							print("""perplexity on objective set:"""     ,	__SCREAMING_SNAKE_CASE  )

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

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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=15     ,	__SCREAMING_SNAKE_CASE=128     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE="igf_model.pt"     ,	)->    Optional[int]:
							set_seed(42  )

							# Load pre-trained model
							_SCREAMING_SNAKE_CASE		:						Any			  =    GPTaLMHeadModel.from_pretrained("""gpt2"""  )

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

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						Any			  =    train_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	max_epochs=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE     ,	eval_freq=100     ,	igf_model_path=__SCREAMING_SNAKE_CASE     ,	)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=1_000     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=1.0     ,	__SCREAMING_SNAKE_CASE=recopy_gpta     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"     ,	)->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RandomSampler(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    DataLoader(__SCREAMING_SNAKE_CASE     ,	sampler=__SCREAMING_SNAKE_CASE  )

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

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(__SCREAMING_SNAKE_CASE  )
														secondary_learner.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							_SCREAMING_SNAKE_CASE		:						int			  =    []

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

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

																												# 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:
																																			_SCREAMING_SNAKE_CASE		:						Dict			  =    -1
																												if predicted_q < threshold:
																																			_SCREAMING_SNAKE_CASE		:						List[str]			  =    False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()  )  )
																												_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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		:						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:
																																			_SCREAMING_SNAKE_CASE		:						Tuple			  =    compute_perplexity(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																																			test_perps.append(__SCREAMING_SNAKE_CASE  )

																																			print("""Test perplexity, step"""     ,	__SCREAMING_SNAKE_CASE     ,	""":"""     ,	__SCREAMING_SNAKE_CASE  )
            # 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()     ,	__SCREAMING_SNAKE_CASE  )
							torch.cuda.empty_cache()
							# Do some cleaning up so we can reinitialize for the next run of this function
							del lm_optimizer
							del lm_scheduler
							return model



def      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""  )

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

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

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

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

							parser.add_argument("""--max_steps"""     ,	default=1_000     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""To calculate training epochs"""  )

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

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

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

							parser.add_argument(
							    """--min_len"""     ,	default=1_026     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""The minimum length of the article to be used as objective set"""  )

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

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

							parser.add_argument(
							    """--threshold"""     ,	default=1.0     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	help="""finetuned_model_name"""  )

							parser.add_argument(
							    """--recopy_model"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	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=1_026     ,	trim=__SCREAMING_SNAKE_CASE     ,	data_file="""data/tokenized_stories_train_wikitext103.jbl"""     ,	igf_data_file="""igf_context_pairs.jbl"""     ,	)

							# Load train data for secondary learner
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    joblib.load("""data/IGF_values.jbl"""  )

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						int			  =    training_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	secondary_learner_max_epochs=15     ,	secondary_learner_batch_size=128     ,	eval_freq=100     ,	igf_model_path="""igf_model.pt"""     ,	)

							# load pretrained gpt2 model
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    GPTaLMHeadModel.from_pretrained("""gpt2"""  )
							set_seed(42  )

							# Generate train and test data to train and evaluate gpt2 model
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    generate_datasets(
							    context_len=32     ,	file="""data/tokenized_stories_train_wikitext103.jbl"""     ,	number=100     ,	min_len=1_026     ,	trim=__SCREAMING_SNAKE_CASE  )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	context_len=32     ,	max_steps=1_000     ,	batch_size=16     ,	threshold=1.0     ,	recopy_model=__SCREAMING_SNAKE_CASE     ,	secondary_learner=__SCREAMING_SNAKE_CASE     ,	eval_interval=10     ,	finetuned_model_name="""gpt2_finetuned.pt"""     ,	)


if __name__ == "__main__":
			main()


