



'''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		):





				'''simple docstring'''





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







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

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





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





				'''simple docstring'''





				lowerCAmelCase__					    =    LayoutLMvaImageProcessor if is_pytesseract_available() else None

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=LayoutLMvaImageProcessingTester(self)

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=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 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

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

											__lowercase       	=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 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											pass

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

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

											# Test not batched input
											__lowercase       	=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
											__lowercase       	=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 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											__lowercase       	=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
											__lowercase       	=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
											__lowercase       	=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 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											__lowercase       	=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
											__lowercase       	=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
											__lowercase       	=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 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__lowercase       	=LayoutLMvaImageProcessor()

											from datasets import load_dataset

											__lowercase       	=load_dataset('hf-internal-testing/fixtures_docvqa'  ,       split='test')

											__lowercase       	=Image.open(ds[0]['file']).convert('RGB')

											__lowercase       	=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
											__lowercase       	=[['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
											__lowercase       	=[[[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
											__lowercase       	=LayoutLMvaImageProcessor(apply_ocr=_A)

											__lowercase       	=image_processing(_A  ,       return_tensors='pt')

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








'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCamelCase	 =       logging.get_logger(__name__)


lowerCamelCase	 =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase	 =       {
    """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""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase	 =       {
    """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,
}





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    VOCAB_FILES_NAMES
				lowerCAmelCase__					    =    PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__					    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__					    =    ["""input_ids""", """attention_mask"""]
				lowerCAmelCase__					    =    BartTokenizer

				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  Any=None  ,       _lowerCAmelCase  :  Tuple=None  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  str="replace"  ,       _lowerCAmelCase  :  List[Any]="<s>"  ,       _lowerCAmelCase  :  int="</s>"  ,       _lowerCAmelCase  :  Dict="</s>"  ,       _lowerCAmelCase  :  Optional[int]="<s>"  ,       _lowerCAmelCase  :  str="<unk>"  ,       _lowerCAmelCase  :  List[str]="<pad>"  ,       _lowerCAmelCase  :  Dict="<mask>"  ,       _lowerCAmelCase  :  Tuple=False  ,       _lowerCAmelCase  :  str=True  ,       **_lowerCAmelCase  :  Tuple  ,       ):







											'''simple docstring'''

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

											__lowercase       	=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
											if pre_tok_state.get('add_prefix_space'  ,       _lowerCAmelCase) != add_prefix_space:
																		__lowercase       	=getattr(_lowerCAmelCase  ,       pre_tok_state.pop('type'))
																		__lowercase       	=add_prefix_space
																		__lowercase       	=pre_tok_class(**_lowerCAmelCase)

											__lowercase       	=add_prefix_space

											# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
											__lowercase       	='post_processor'
											__lowercase       	=getattr(self.backend_tokenizer  ,       _lowerCAmelCase  ,       _lowerCAmelCase)
											if tokenizer_component_instance:
																		__lowercase       	=json.loads(tokenizer_component_instance.__getstate__())

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																									__lowercase       	=tuple(state['sep'])
																		if "cls" in state:
																									__lowercase       	=tuple(state['cls'])

																		__lowercase       	=False

																		if state.get('add_prefix_space'  ,       _lowerCAmelCase) != add_prefix_space:
																									__lowercase       	=add_prefix_space
																									__lowercase       	=True

																		if state.get('trim_offsets'  ,       _lowerCAmelCase) != trim_offsets:
																									__lowercase       	=trim_offsets
																									__lowercase       	=True

																		if changes_to_apply:
																									__lowercase       	=getattr(_lowerCAmelCase  ,       state.pop('type'))
																									__lowercase       	=component_class(**_lowerCAmelCase)
																									setattr(self.backend_tokenizer  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

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







											'''simple docstring'''

											if self._mask_token is None:
																		if self.verbose:
																									logger.error('Using mask_token, but it is not set yet.')
																		return None
											return str(self._mask_token)

				@mask_token.setter
				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=AddedToken(_lowerCAmelCase  ,       lstrip=_lowerCAmelCase  ,       rstrip=_lowerCAmelCase) if isinstance(_lowerCAmelCase  ,       _lowerCAmelCase) else value
											__lowercase       	=value

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  int  ,       **_lowerCAmelCase  :  str):







											'''simple docstring'''

											__lowercase       	=kwargs.get('is_split_into_words'  ,       _lowerCAmelCase)

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.')

											return super()._batch_encode_plus(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  List[str]  ,       **_lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											__lowercase       	=kwargs.get('is_split_into_words'  ,       _lowerCAmelCase)

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.')

											return super()._encode_plus(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Optional[str] = None):







											'''simple docstring'''

											__lowercase       	=self._tokenizer.model.save(_lowerCAmelCase  ,       name=_lowerCAmelCase)
											return tuple(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[str]=None):







											'''simple docstring'''

											__lowercase       	=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[int]  ,       _lowerCAmelCase  :  Optional[List[int]] = None):







											'''simple docstring'''

											__lowercase       	=[self.sep_token_id]
											__lowercase       	=[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]



