






import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_pytesseract_available():
	from PIL import Image

	from transformers import LayoutLMvaImageProcessor


class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    Optional[int]			,		UpperCamelCase__    :    Optional[Any]=7			,		UpperCamelCase__    :    Any=3			,		UpperCamelCase__    :    Dict=1_8			,		UpperCamelCase__    :    List[Any]=3_0			,		UpperCamelCase__    :    Union[str, Any]=4_0_0			,		UpperCamelCase__    :    Tuple=True			,		UpperCamelCase__    :    Dict=None			,		UpperCamelCase__    :    Optional[int]=True			,		):






											'''simple docstring'''







											snake_case__	  =       size if size is not None else {"""height""": 1_8, """width""": 1_8}
											snake_case__	  =       parent
											snake_case__	  =       batch_size
											snake_case__	  =       num_channels
											snake_case__	  =       image_size
											snake_case__	  =       min_resolution
											snake_case__	  =       max_resolution
											snake_case__	  =       do_resize
											snake_case__	  =       size
											snake_case__	  =       apply_ocr



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}


@require_torch
@require_pytesseract
class    _lowerCAmelCase			(		lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Tuple          =  LayoutLMvaImageProcessor if is_pytesseract_available() else None



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       LayoutLMvaImageProcessingTester(self)



				@property
				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											return self.image_processor_tester.prepare_image_processor_dict()



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(__A			,		"""do_resize"""))
											self.assertTrue(hasattr(__A			,		"""size"""))
											self.assertTrue(hasattr(__A			,		"""apply_ocr"""))



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size			,		{"""height""": 1_8, """width""": 1_8})

											snake_case__	  =       self.image_processing_class.from_dict(self.image_processor_dict			,		size=4_2)
											self.assertEqual(image_processor.size			,		{"""height""": 4_2, """width""": 4_2})



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											snake_case__	  =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=__A)
											for image in image_inputs:
																		self.assertIsInstance(__A			,		Image.Image)

											# Test not batched input
											snake_case__	  =       image_processing(image_inputs[0]			,		return_tensors="""pt""")
											self.assertEqual(
											    encoding.pixel_values.shape			,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )			,		)

											self.assertIsInstance(encoding.words			,		__A)
											self.assertIsInstance(encoding.boxes			,		__A)

											# Test batched
											snake_case__	  =       image_processing(__A			,		return_tensors="""pt""").pixel_values
											self.assertEqual(
											    encoded_images.shape			,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )			,		)



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											snake_case__	  =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=__A			,		numpify=__A)
											for image in image_inputs:
																		self.assertIsInstance(__A			,		np.ndarray)

											# Test not batched input
											snake_case__	  =       image_processing(image_inputs[0]			,		return_tensors="""pt""").pixel_values
											self.assertEqual(
											    encoded_images.shape			,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )			,		)

											# Test batched
											snake_case__	  =       image_processing(__A			,		return_tensors="""pt""").pixel_values
											self.assertEqual(
											    encoded_images.shape			,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )			,		)



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											snake_case__	  =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=__A			,		torchify=__A)
											for image in image_inputs:
																		self.assertIsInstance(__A			,		torch.Tensor)

											# Test not batched input
											snake_case__	  =       image_processing(image_inputs[0]			,		return_tensors="""pt""").pixel_values
											self.assertEqual(
											    encoded_images.shape			,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )			,		)

											# Test batched
											snake_case__	  =       image_processing(__A			,		return_tensors="""pt""").pixel_values
											self.assertEqual(
											    encoded_images.shape			,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )			,		)



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       LayoutLMvaImageProcessor()

											from datasets import load_dataset

											snake_case__	  =       load_dataset("""hf-internal-testing/fixtures_docvqa"""			,		split="""test""")

											snake_case__	  =       Image.open(ds[0]["""file"""]).convert("""RGB""")

											snake_case__	  =       image_processing(__A			,		return_tensors="""pt""")

											self.assertEqual(encoding.pixel_values.shape			,		(1, 3, 2_2_4, 2_2_4))
											self.assertEqual(len(encoding.words)			,		len(encoding.boxes))

											# fmt: off
											# the words and boxes were obtained with Tesseract 4.1.1
											snake_case__	  =       [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
											snake_case__	  =       [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
											# fmt: on

											self.assertListEqual(encoding.words			,		__A)
											self.assertListEqual(encoding.boxes			,		__A)

											# with apply_OCR = False
											snake_case__	  =       LayoutLMvaImageProcessor(apply_ocr=__A)

											snake_case__	  =       image_processing(__A			,		return_tensors="""pt""")

											self.assertEqual(encoding.pixel_values.shape			,		(1, 3, 2_2_4, 2_2_4))








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 _UpperCAmelCase   (	a						:	int=32  ,					a						:	int=10  ,					a						:	Tuple=100  ,					a						:	str=1026  ,					a						:	Union[str, Any]=True  ,					a						:	Optional[int]="data/tokenized_stories_train_wikitext103.jbl"  ,					a						:	Tuple="igf_context_pairs.jbl"  ,					):
							set_seed(3 )
							# generate train_data and objective_set
							snake_case__  , snake_case__	  =       generate_datasets(
							    a  ,					a  ,					number=a  ,					min_len=1026  ,					trim=a )
							# keeps model same across runs
							set_seed(4 )
							# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
							# can we train on GPU?
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )

							# load pretrained model
							snake_case__	  =       load_gpta("""gpt2""" ).to(a )
							print("""computing perplexity on objective set""" )
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	int  ,					a						:	Dict=15  ,					a						:	Optional[int]=128  ,					a						:	str=100  ,					a						:	Optional[int]="igf_model.pt"  ,					):
							set_seed(42 )

							# Load pre-trained model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )

							# Initialize secondary learner to use embedding weights of model
							snake_case__	  =       SecondaryLearner(a )

							# Train secondary learner
							snake_case__	  =       train_secondary_learner(
							    a  ,					a  ,					max_epochs=a  ,					batch_size=a  ,					eval_freq=100  ,					igf_model_path=a  ,					)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	List[Any]  ,					a						:	str  ,					a						:	List[Any]=32  ,					a						:	Any=1000  ,					a						:	str=16  ,					a						:	Tuple=1.0  ,					a						:	str=recopy_gpta  ,					a						:	Any=None  ,					a						:	Optional[int]=10  ,					a						:	Tuple="gpt2_finetuned.pt"  ,					):
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
							snake_case__	  =       RandomSampler(a )
							snake_case__	  =       DataLoader(a  ,					sampler=a )

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

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

							snake_case__	  =       []
							snake_case__	  =       []

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

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

																												# Here we implement the simple non-constant threshold for the predicted IG(X) value
																												# We will decay the selectivity of our secondary learner filter from
																												# 1 standard deviation above average to 1 below average after 10 batches.

																												if global_step == 10:
																																			snake_case__	  =       -1
																												if predicted_q < threshold:
																																			snake_case__	  =       False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu() ) )
																												snake_case__	  =       outputs[0]
																												lm_loss.backward()
																												examples += 1

																					del outputs

																					# Once the batch is filled with enough contexts, backprop on the batch.
																					if examples == batch_size:
																												torch.cuda.empty_cache()
																												snake_case__	  =       0
																												# Do LM backprop
																												torch.nn.utils.clip_grad_norm_(model.parameters()  ,					3.0 )
																												lm_optimizer.step()
																												lm_scheduler.step()  # Update learning rate schedule
																												global_step += 1
																												# Compute the performance of the transformer model at this batch
																												if global_step % eval_interval == 0:
																																			snake_case__	  =       compute_perplexity(a  ,					a  ,					a )
																																			test_perps.append(a )

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

    # save finetuned transformer model
							torch.save(model.state_dict()  ,					a )
							torch.cuda.empty_cache()
							# Do some cleaning up so we can reinitialize for the next run of this function
							del lm_optimizer
							del lm_scheduler
							return model

def _UpperCAmelCase   (	):
							snake_case__	  =       argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" )

							# Required parameters
							parser.add_argument(
							    """--data_dir"""  ,					default=a  ,					type=a  ,					required=a  ,					help="""The input data dir. Should contain data files for WikiText."""  ,					)
							parser.add_argument(
							    """--model_name_or_path"""  ,					default=a  ,					type=a  ,					required=a  ,					help="""Path to pretrained model or model identifier from huggingface.co/models"""  ,					)
							parser.add_argument(
							    """--data_file"""  ,					type=a  ,					default=a  ,					help=(
							        """A jbl file containing tokenized data which can be split as objective dataset, """
							        """train_dataset and test_dataset."""
							    )  ,					)

							parser.add_argument(
							    """--igf_data_file"""  ,					type=a  ,					default=a  ,					help="""A jbl file containing the context and information gain pairs to train secondary learner."""  ,					)

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

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

							parser.add_argument(
							    """--context_len"""  ,					default=32  ,					type=a  ,					help=(
							        """The maximum total input sequence length after tokenization. Sequences longer """
							        """than this will be truncated, sequences shorter will be padded."""
							    )  ,					)

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

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

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

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

							parser.add_argument(
							    """--eval_interval"""  ,					default=10  ,					type=a  ,					help=(
							        """decay the selectivity of our secondary learner filter from"""
							        """1 standard deviation above average to 1 below average after 10 batches"""
							    )  ,					)

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

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

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

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

							parser.add_argument(
							    """--threshold"""  ,					default=1.0  ,					type=a  ,					help=(
							        """The threshold value used by secondary learner to filter the train_data and allow only"""
							        """ informative data as input to the model"""
							    )  ,					)

							parser.add_argument("""--finetuned_model_name"""  ,					default="""gpt2_finetuned.pt"""  ,					type=a  ,					help="""finetuned_model_name""" )

							parser.add_argument(
							    """--recopy_model"""  ,					default=a  ,					type=a  ,					help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""  ,					)

							# function calls
							# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
							generate_n_pairs(
							    context_len=32  ,					max_steps=10  ,					size_objective_set=100  ,					min_len=1026  ,					trim=a  ,					data_file="""data/tokenized_stories_train_wikitext103.jbl"""  ,					igf_data_file="""igf_context_pairs.jbl"""  ,					)

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

							# Train secondary learner
							snake_case__	  =       training_secondary_learner(
							    a  ,					secondary_learner_max_epochs=15  ,					secondary_learner_batch_size=128  ,					eval_freq=100  ,					igf_model_path="""igf_model.pt"""  ,					)

							# load pretrained gpt2 model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )
							set_seed(42 )

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

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


if __name__ == "__main__":
	main()
