
'''simple docstring'''

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  A  (						unittest.TestCase     ):





	def __init__(       self      :							Optional[Any]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							int=7   ,				__magic_name__      :							List[str]=3   ,				__magic_name__      :							Optional[Any]=18   ,				__magic_name__      :							Optional[Any]=30   ,				__magic_name__      :							Any=400   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							List[Any]=None   ,				__magic_name__      :							List[str]=True   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				size if size is not None else {"height": 18, "width": 18}
				lowerCAmelCase__										=				parent
				lowerCAmelCase__										=				batch_size
				lowerCAmelCase__										=				num_channels
				lowerCAmelCase__										=				image_size
				lowerCAmelCase__										=				min_resolution
				lowerCAmelCase__										=				max_resolution
				lowerCAmelCase__										=				do_resize
				lowerCAmelCase__										=				size
				lowerCAmelCase__										=				apply_ocr







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

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






@require_torch
@require_pytesseract
class  A  (						SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Union[str, Any]   =  LayoutLMvaImageProcessor if is_pytesseract_available() else None





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				LayoutLMvaImageProcessingTester(self				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.image_processing_class(**self.image_processor_dict				)
				self.assertTrue(hasattr(__magic_name__   ,				"do_resize"				)				)
				self.assertTrue(hasattr(__magic_name__   ,				"size"				)				)
				self.assertTrue(hasattr(__magic_name__   ,				"apply_ocr"				)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

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

				lowerCAmelCase__										=				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      :							Any				):


				"""simple docstring"""

				pass





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

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

				# Test not batched input
				lowerCAmelCase__										=				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   ,				__magic_name__				)
				self.assertIsInstance(encoding.boxes   ,				__magic_name__				)

				# Test batched
				lowerCAmelCase__										=				image_processing(__magic_name__   ,				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[Any]				):


				"""simple docstring"""

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

				# Test not batched input
				lowerCAmelCase__										=				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__										=				image_processing(__magic_name__   ,				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				):


				"""simple docstring"""

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

				# Test not batched input
				lowerCAmelCase__										=				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__										=				image_processing(__magic_name__   ,				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      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				LayoutLMvaImageProcessor()

				from datasets import load_dataset

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

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

				lowerCAmelCase__										=				image_processing(__magic_name__   ,				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__										=				[["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__										=				[[[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   ,				__magic_name__				)
				self.assertListEqual(encoding.boxes   ,				__magic_name__				)

				# with apply_OCR = False
				lowerCAmelCase__										=				LayoutLMvaImageProcessor(apply_ocr=__magic_name__				)

				lowerCAmelCase__										=				image_processing(__magic_name__   ,				return_tensors="pt"				)

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










import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      with open(UpperCAmelCase__			) as metadata_file:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
      lowerCamelCase             =		LukeConfig(use_entity_aware_attention=UpperCAmelCase__ , **metadata["model_config"]			)

      # Load in the weights from the checkpoint_path
      lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location="cpu"			)

      # Load the entity vocab file
      lowerCamelCase             =		load_entity_vocab(UpperCAmelCase__			)

      lowerCamelCase             =		RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase             =		AddedToken("<ent>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      lowerCamelCase             =		AddedToken("<ent2>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}			)
      config.vocab_size += 2

      print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""			)
      tokenizer.save_pretrained(UpperCAmelCase__			)
      with open(os.path.join(UpperCAmelCase__ , LukeTokenizer.vocab_files_names["entity_vocab_file"]			) , "w"			) as f:
            json.dump(UpperCAmelCase__ , UpperCAmelCase__			)

      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__			)

      # Initialize the embeddings of the special tokens
      lowerCamelCase             =		state_dict["embeddings.word_embeddings.weight"]
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["@"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["#"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		torch.cat([word_emb, ent_emb, enta_emb]			)

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers			):
            for matrix_name in ["query.weight", "query.bias"]:
                  lowerCamelCase             =		F"""encoder.layer.{layer_index}.attention.self."""
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      lowerCamelCase             =		state_dict["entity_embeddings.entity_embeddings.weight"]
      lowerCamelCase             =		entity_emb[entity_vocab["[MASK]"]]

      lowerCamelCase             =		LukeModel(config=UpperCAmelCase__			).eval()

      lowerCamelCase      ,				lowerCamelCase             =		model.load_state_dict(UpperCAmelCase__ , strict=UpperCAmelCase__			)
      if not (len(UpperCAmelCase__			) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {", ".join(UpperCAmelCase__			)}. Expected only missing embeddings.position_ids"""			)
      if not (all(key.startswith("entity_predictions"			) or key.startswith("lm_head"			) for key in unexpected_keys			)):
            raise ValueError(
                "Unexpected keys"
                F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"			) or key.startswith("lm_head"			))]			)}"""			)

      # Check outputs
      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__ , task="entity_classification"			)

      lowerCamelCase             =		(
          "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
          " new world number one avoid a humiliating second- round exit at Wimbledon ."
      )
      lowerCamelCase             =		(39, 42)
      lowerCamelCase             =		tokenizer(UpperCAmelCase__ , entity_spans=[span] , add_prefix_space=UpperCAmelCase__ , return_tensors="pt"			)

      lowerCamelCase             =		model(**UpperCAmelCase__			)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 42, 1024)			)
            lowerCamelCase             =		torch.tensor(
                [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 42, 768)			)
            lowerCamelCase             =		torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]			)

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""			)
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 1, 1024)			)
            lowerCamelCase             =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 1, 768)			)
            lowerCamelCase             =		torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]			)

      if not (outputs.entity_last_hidden_state.shape != expected_shape):
            raise ValueError(
                F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
                F""" {expected_shape}"""			)
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(UpperCAmelCase__			)			)
      model.save_pretrained(UpperCAmelCase__			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      with open(UpperCAmelCase__ , "r" , encoding="utf-8"			) as f:
            for index, line in enumerate(UpperCAmelCase__			):
                  lowerCamelCase      ,				lowerCamelCase             =		line.rstrip().split("\t"			)
                  lowerCamelCase             =		index

      return entity_vocab


if __name__ == "__main__":
     a_    :  Tuple						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
     parser.add_argument(
         '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
     )
     parser.add_argument(
         '--entity_vocab_path',
         default=None,
         type=str,
         help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
     )
     parser.add_argument(
         '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
     )
     a_    :  Dict						  =		parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )