
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     :				int    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				str=7    ,  _UpperCamelCase     :				Any=3    ,  _UpperCamelCase     :				Tuple=18    ,  _UpperCamelCase     :				Union[str, Any]=30    ,  _UpperCamelCase     :				Optional[int]=400    ,  _UpperCamelCase     :				Optional[Any]=True    ,  _UpperCamelCase     :				List[str]=None    ,  _UpperCamelCase     :				Optional[Any]=True    ,  )				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						size if size is not None else {"height": 18, "width": 18}
          _lowerCamelCase     :		str 						=						parent
          _lowerCamelCase     :		str 						=						batch_size
          _lowerCamelCase     :		Tuple 						=						num_channels
          _lowerCamelCase     :		Optional[int] 						=						image_size
          _lowerCamelCase     :		List[str] 						=						min_resolution
          _lowerCamelCase     :		Tuple 						=						max_resolution
          _lowerCamelCase     :		Tuple 						=						do_resize
          _lowerCamelCase     :		Dict 						=						size
          _lowerCamelCase     :		List[str] 						=						apply_ocr




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[int]:


          """simple docstring"""


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





@require_torch
@require_pytesseract
class 	__snake_case	( __lowerCamelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       LayoutLMvaImageProcessor if is_pytesseract_available() else None



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						LayoutLMvaImageProcessingTester(self)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->List[Any]:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						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     :				Optional[Any])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size    ,  {"""height""": 18, """width""": 18})

          _lowerCamelCase     :		Dict 						=						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42)
          self.assertEqual(image_processor.size    ,  {"""height""": 42, """width""": 42})



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Optional[Any]:


          """simple docstring"""


          pass



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Any:


          """simple docstring"""


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

          # Test not batched input
          _lowerCamelCase     :		List[str] 						=						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
          _lowerCamelCase     :		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     :				int)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowerCamelCase     :		int 						=						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
          _lowerCamelCase     :		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
          _lowerCamelCase     :		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     :				int)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowerCamelCase     :		int 						=						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
          _lowerCamelCase     :		Union[str, 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
          _lowerCamelCase     :		Union[str, 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     :				Any)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						LayoutLMvaImageProcessor()

          from datasets import load_dataset

          _lowerCamelCase     :		Optional[Any] 						=						load_dataset("""hf-internal-testing/fixtures_docvqa"""    ,  split="""test""")

          _lowerCamelCase     :		Optional[Any] 						=						Image.open(ds[0]["""file"""]).convert("""RGB""")

          _lowerCamelCase     :		List[Any] 						=						image_processing(a_    ,  return_tensors="""pt""")

          self.assertEqual(encoding.pixel_values.shape    ,  (1, 3, 224, 224))
          self.assertEqual(len(encoding.words)    ,  len(encoding.boxes))

          # fmt: off
          # the words and boxes were obtained with Tesseract 4.1.1
          _lowerCamelCase     :		List[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
          _lowerCamelCase     :		Tuple 						=						[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
          # fmt: on

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

          # with apply_OCR = False
          _lowerCamelCase     :		List[str] 						=						LayoutLMvaImageProcessor(apply_ocr=a_)

          _lowerCamelCase     :		List[str] 						=						image_processing(a_    ,  return_tensors="""pt""")

          self.assertEqual(encoding.pixel_values.shape    ,  (1, 3, 224, 224))





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCAmelCase			:							int     ={
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()







def 						A__  (					__A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		List[Any] 						=						finetuning_task.lower() if finetuning_task is not None else """"""
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
        _lowerCamelCase     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(__A   , __A   , __A	)

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


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--xlnet_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained XLNet model. \n"
            "This specifies the model architecture."
        ),
    )
    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(
        "--finetuning_task",
        default=None,
        type=str,
        help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
    )
    lowerCAmelCase			:							Union[str, Any]     =parser.parse_args()
    print(args)

    convert_xlnet_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
    )




