
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        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase=7 ,       __lowercase=3 ,       __lowercase=1_8 ,       __lowercase=3_0 ,       __lowercase=4_0_0 ,       __lowercase=True ,       __lowercase=None ,       __lowercase=True ,       ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  size if size is not None else {"""height""": 1_8, """width""": 1_8}
									a__       :						Tuple							=  parent
									a__       :						Tuple							=  batch_size
									a__       :						Dict							=  num_channels
									a__       :						str							=  image_size
									a__       :						Optional[Any]							=  min_resolution
									a__       :						Optional[int]							=  max_resolution
									a__       :						List[Any]							=  do_resize
									a__       :						Optional[int]							=  size
									a__       :						Optional[Any]							=  apply_ocr

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




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




@require_torch
@require_pytesseract
class        snake_case__    (SCREAMING_SNAKE_CASE__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:int      =					LayoutLMvaImageProcessor if is_pytesseract_available() else None

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




									a__       :						str							=  LayoutLMvaImageProcessingTester(self  )

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




									return self.image_processor_tester.prepare_image_processor_dict()

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




									a__       :						int							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size ,       {"""height""": 1_8, """width""": 1_8}  )

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




									pass

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




									a__       :						Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict  )
									# create random PIL images
									a__       :						Optional[int]							=  prepare_image_inputs(self.image_processor_tester ,       equal_resolution=A__  )
									for image in image_inputs:
												self.assertIsInstance(A__ ,       Image.Image  )

									# Test not batched input
									a__       :						int							=  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
									a__       :						Optional[int]							=  image_processing(A__ ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    ) ,       )

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




									a__       :						Optional[Any]							=  self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									a__       :						Any							=  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
									a__       :						List[Any]							=  image_processing(image_inputs[0] ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    ) ,       )

									# Test batched
									a__       :						Optional[Any]							=  image_processing(A__ ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    ) ,       )

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




									a__       :						Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									a__       :						Union[str, Any]							=  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
									a__       :						Optional[Any]							=  image_processing(image_inputs[0] ,       return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    ) ,       )

									# Test batched
									a__       :						str							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						List[Any]							=  LayoutLMvaImageProcessor()

									from datasets import load_dataset

									a__       :						str							=  load_dataset("""hf-internal-testing/fixtures_docvqa""" ,       split="""test"""  )

									a__       :						Union[str, Any]							=  Image.open(ds[0]["""file"""]  ).convert("""RGB"""  )

									a__       :						Any							=  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
									a__       :						Union[str, Any]							=  [["""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
									a__       :						Optional[Any]							=  [[[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
									a__       :						int							=  LayoutLMvaImageProcessor(apply_ocr=A__  )

									a__       :						Any							=  image_processing(A__ ,       return_tensors="""pt"""  )

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









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowercase		:				Union[str, Any]      		=data_utils.TransfoXLTokenizer
_lowercase		:				Tuple      		=data_utils.TransfoXLCorpus
_lowercase		:				Optional[int]      		=data_utils
_lowercase		:				int      		=data_utils

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       List[Any]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Optional[Any])							->	List[Any]:







			"""simple docstring"""
			if transfo_xl_dataset_file:
						# Convert a pre-processed corpus (see original TensorFlow repo)
						with open(_lowercase						,       """rb""") as fp:
									a__       :						Optional[Any]							=  pickle.load(_lowercase						,       encoding="""latin1""")
						# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
						a__       :						Dict							=  pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
						print(F'''Save vocabulary to {pytorch_vocab_dump_path}''')
						a__       :						List[str]							=  corpus.vocab.__dict__
						torch.save(_lowercase						,       _lowercase)

						a__       :						Optional[int]							=  corpus.__dict__
						corpus_dict_no_vocab.pop("""vocab"""						,       _lowercase)
						a__       :						Tuple							=  pytorch_dump_folder_path + """/""" + CORPUS_NAME
						print(F'''Save dataset to {pytorch_dataset_dump_path}''')
						torch.save(_lowercase						,       _lowercase)

			if tf_checkpoint_path:
						# Convert a pre-trained TensorFlow model
						a__       :						Optional[Any]							=  os.path.abspath(_lowercase)
						a__       :						str							=  os.path.abspath(_lowercase)

						print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''')
						# Initialise PyTorch model
						if transfo_xl_config_file == "":
									a__       :						List[str]							=  TransfoXLConfig()
						else:
									a__       :						Any							=  TransfoXLConfig.from_json_file(_lowercase)
						print(F'''Building PyTorch model from configuration: {config}''')
						a__       :						Any							=  TransfoXLLMHeadModel(_lowercase)

						a__       :						Optional[Any]							=  load_tf_weights_in_transfo_xl(_lowercase						,       _lowercase						,       _lowercase)
						# Save pytorch-model
						a__       :						Tuple							=  os.path.join(_lowercase						,       _lowercase)
						a__       :						Optional[int]							=  os.path.join(_lowercase						,       _lowercase)
						print(F'''Save PyTorch model to {os.path.abspath(_lowercase)}''')
						torch.save(model.state_dict()						,       _lowercase)
						print(F'''Save configuration file to {os.path.abspath(_lowercase)}''')
						with open(_lowercase						,       """w"""						,       encoding="""utf-8""") as f:
									f.write(config.to_json_string())


if __name__ == "__main__":
		_lowercase		:				Tuple      		=argparse.ArgumentParser()
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path to the folder to store the PyTorch model or dataset/vocab.",
		)
		parser.add_argument(
		    "--tf_checkpoint_path",
		    default="",
		    type=str,
		    help="An optional path to a TensorFlow checkpoint path to be converted.",
		)
		parser.add_argument(
		    "--transfo_xl_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained BERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--transfo_xl_dataset_file",
		    default="",
		    type=str,
		    help="An optional dataset file to be converted in a vocabulary.",
		)
		_lowercase		:				Dict      		=parser.parse_args()
		convert_transfo_xl_checkpoint_to_pytorch(
		    args.tf_checkpoint_path,
		    args.transfo_xl_config_file,
		    args.pytorch_dump_folder_path,
		    args.transfo_xl_dataset_file,
		)




