





"""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      UpperCamelCase_						(					unittest.TestCase   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=7			,  lowerCAmelCase_=3			,  lowerCAmelCase_=18			,  lowerCAmelCase_=30			,  lowerCAmelCase_=400			,  lowerCAmelCase_=True			,  lowerCAmelCase_=None			,  lowerCAmelCase_=True			,  )						->							int:
										_snake_case													=			size if size is not None else {'height': 18, 'width': 18}
										_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 		lowerCAmelCase     (						self					)						->							List[Any]:
										return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class      UpperCamelCase_						(					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		LayoutLMvaImageProcessor if is_pytesseract_available() else None




					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			LayoutLMvaImageProcessingTester(self					)




					@property
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(lowerCAmelCase_			,  'do_resize'					)					)
										self.assertTrue(hasattr(lowerCAmelCase_			,  'size'					)					)
										self.assertTrue(hasattr(lowerCAmelCase_			,  'apply_ocr'					)					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			self.image_processing_class.from_dict(self.image_processor_dict					)
										self.assertEqual(image_processor.size			,  {'height': 18, 'width': 18}					)

										_snake_case													=			self.image_processing_class.from_dict(self.image_processor_dict			,  size=42					)
										self.assertEqual(image_processor.size			,  {'height': 42, 'width': 42}					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										pass




					def 		lowerCAmelCase     (						self					)						->							str:
										# Initialize image_processing
										_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=lowerCAmelCase_					)
										for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_			,  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			,  lowerCAmelCase_					)
										self.assertIsInstance(encoding.boxes			,  lowerCAmelCase_					)

										# Test batched
										_snake_case													=			image_processing(lowerCAmelCase_			,  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 		lowerCAmelCase     (						self					)						->							Any:
										# Initialize image_processing
										_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=lowerCAmelCase_			,  numpify=lowerCAmelCase_					)
										for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_			,  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(lowerCAmelCase_			,  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 		lowerCAmelCase     (						self					)						->							Dict:
										# Initialize image_processing
										_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=lowerCAmelCase_			,  torchify=lowerCAmelCase_					)
										for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_			,  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(lowerCAmelCase_			,  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 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										# with apply_OCR = True
										_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(lowerCAmelCase_			,  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
										_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													=			[[[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			,  lowerCAmelCase_					)
										self.assertListEqual(encoding.boxes			,  lowerCAmelCase_					)

										# with apply_OCR = False
										_snake_case													=			LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase_					)

										_snake_case													=			image_processing(lowerCAmelCase_			,  return_tensors='pt'					)

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










import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_ 							=       logging.get_logger(__name__)


UpperCAmelCase_ 							=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase_ 							=       {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

UpperCAmelCase_ 							=       {
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}





@lru_cache()
def   lowerCamelCase__       (       )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			(
					    list(range(ord('!'  )    ,      ord('~'  ) + 1  )  ) + list(range(ord('¡'  )    ,      ord('¬'  ) + 1  )  ) + list(range(ord('®'  )    ,      ord('ÿ'  ) + 1  )  )
					)
					_snake_case													=			bs[:]
					_snake_case													=			0
					for b in range(2**8  ):
										if b not in bs:
															bs.append(UpperCamelCase__  )
															cs.append(2**8 + n  )
															n += 1
					_snake_case													=			[chr(UpperCamelCase__  ) for n in cs]
					return dict(zip(UpperCamelCase__    ,      UpperCamelCase__  )  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple  )     ->	int:



					'''simple docstring'''




					_snake_case													=			set()
					_snake_case													=			word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)  )
										_snake_case													=			char
					return pairs




class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		['''input_ids''', '''attention_mask''']




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_="replace"			,  lowerCAmelCase_="<s>"			,  lowerCAmelCase_="</s>"			,  lowerCAmelCase_="</s>"			,  lowerCAmelCase_="<s>"			,  lowerCAmelCase_="<unk>"			,  lowerCAmelCase_="<pad>"			,  lowerCAmelCase_="<mask>"			,  lowerCAmelCase_=False			,  **lowerCAmelCase_			,  )						->							Tuple:
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else bos_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else eos_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else sep_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else cls_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else unk_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else mask_token

										super().__init__(
										    errors=lowerCAmelCase_			,  bos_token=lowerCAmelCase_			,  eos_token=lowerCAmelCase_			,  unk_token=lowerCAmelCase_			,  sep_token=lowerCAmelCase_			,  cls_token=lowerCAmelCase_			,  pad_token=lowerCAmelCase_			,  mask_token=lowerCAmelCase_			,  add_prefix_space=lowerCAmelCase_			,  **lowerCAmelCase_			,  )

										with open(lowerCAmelCase_			,  encoding='utf-8'					) as vocab_handle:
															_snake_case													=			json.load(lowerCAmelCase_					)
										_snake_case													=			{v: k for k, v in self.encoder.items()}
										_snake_case													=			errors  # how to handle errors in decoding
										_snake_case													=			bytes_to_unicode()
										_snake_case													=			{v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase_			,  encoding='utf-8'					) as merges_handle:
															_snake_case													=			merges_handle.read().split('\n'					)[1:-1]
										_snake_case													=			[tuple(merge.split()					) for merge in bpe_merges]
										_snake_case													=			dict(zip(lowerCAmelCase_			,  range(len(lowerCAmelCase_					)					)					)					)
										_snake_case													=			{}
										_snake_case													=			add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										_snake_case													=			re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'					)




					@property
					def 		lowerCAmelCase     (						self					)						->							Any:
										return len(self.encoder					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										return dict(self.encoder			,  **self.added_tokens_encoder					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Dict:
										if token in self.cache:
															return self.cache[token]
										_snake_case													=			tuple(lowerCAmelCase_					)
										_snake_case													=			get_pairs(lowerCAmelCase_					)

										if not pairs:
															return token

										while True:
															_snake_case													=			min(lowerCAmelCase_			,  key=lambda lowerCAmelCase_					: self.bpe_ranks.get(lowerCAmelCase_			,  float('inf'					)					)					)
															if bigram not in self.bpe_ranks:
																				break
															_snake_case							,       _snake_case													=			bigram
															_snake_case													=			[]
															_snake_case													=			0
															while i < len(lowerCAmelCase_					):
																				try:
																									_snake_case													=			word.index(lowerCAmelCase_			,  lowerCAmelCase_					)
																				except ValueError:
																									new_word.extend(word[i:]					)
																									break
																				else:
																									new_word.extend(word[i:j]					)
																									_snake_case													=			j

																				if word[i] == first and i < len(lowerCAmelCase_					) - 1 and word[i + 1] == second:
																									new_word.append(first + second					)
																									i += 2
																				else:
																									new_word.append(word[i]					)
																									i += 1
															_snake_case													=			tuple(lowerCAmelCase_					)
															_snake_case													=			new_word
															if len(lowerCAmelCase_					) == 1:
																				break
															else:
																				_snake_case													=			get_pairs(lowerCAmelCase_					)
										_snake_case													=			' '.join(lowerCAmelCase_					)
										_snake_case													=			word
										return word




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							List[Any]:
										_snake_case													=			[]
										for token in re.findall(self.pat			,  lowerCAmelCase_					):
															_snake_case													=			''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase_					).split(' '					)					)
										return bpe_tokens




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							str:
										return self.encoder.get(lowerCAmelCase_			,  self.encoder.get(self.unk_token					)					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[int]:
										return self.decoder.get(lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										_snake_case													=			''.join(lowerCAmelCase_					)
										_snake_case													=			bytearray([self.byte_decoder[c] for c in text]					).decode('utf-8'			,  errors=self.errors					)
										return text




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							Tuple[str]:
										if not os.path.isdir(lowerCAmelCase_					):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
										_snake_case													=			os.path.join(
										    lowerCAmelCase_			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
										_snake_case													=			os.path.join(
										    lowerCAmelCase_			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']					)

										with open(lowerCAmelCase_			,  'w'			,  encoding='utf-8'					) as f:
															f.write(json.dumps(self.encoder			,  indent=2			,  sort_keys=lowerCAmelCase_			,  ensure_ascii=lowerCAmelCase_					) + '\n'					)

										_snake_case													=			0
										with open(lowerCAmelCase_			,  'w'			,  encoding='utf-8'					) as writer:
															writer.write('#version: 0.2\n'					)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,  key=lambda lowerCAmelCase_					: kv[1]					):
																				if index != token_index:
																									logger.warning(
																									    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																									    ' Please check that the tokenizer is not corrupted!'					)
																									_snake_case													=			token_index
																				writer.write(' '.join(lowerCAmelCase_					) + '\n'					)
																				index += 1

										return vocab_file, merge_file




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[int]:
										if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_snake_case													=			[self.cls_token_id]
										_snake_case													=			[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = False					)						->							List[int]:
										if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase_			,  token_ids_a=lowerCAmelCase_			,  already_has_special_tokens=lowerCAmelCase_					)

										if token_ids_a is None:
															return [1] + ([0] * len(lowerCAmelCase_					)) + [1]
										return [1] + ([0] * len(lowerCAmelCase_					)) + [1, 1] + ([0] * len(lowerCAmelCase_					)) + [1]




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[int]:
										_snake_case													=			[self.sep_token_id]
										_snake_case													=			[self.cls_token_id]

										if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=False			,  **lowerCAmelCase_					)						->							str:
										_snake_case													=			kwargs.pop('add_prefix_space'			,  self.add_prefix_space					)
										if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase_					) > 0 and not text[0].isspace()):
															_snake_case													=			' ' + text
										return (text, kwargs)



