


'''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       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Union[str, Any]=7    ,      UpperCAmelCase__       :      int=3    ,      UpperCAmelCase__       :      Tuple=1_8    ,      UpperCAmelCase__       :      Optional[int]=3_0    ,      UpperCAmelCase__       :      Union[str, Any]=4_0_0    ,      UpperCAmelCase__       :      int=True    ,      UpperCAmelCase__       :      Any=None    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      )				->  int:
        lowerCAmelCase     =			size if size is not None else {'height': 1_8, 'width': 1_8}
        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 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Union[str, Any]:
        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}




@require_torch
@require_pytesseract
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[int]		=     LayoutLMvaImageProcessor if is_pytesseract_available() else None





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
        lowerCAmelCase     =			LayoutLMvaImageProcessingTester(self    )





     @property
     def 							__UpperCAmelCase      ( self       :      Any    )				->  List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 							__UpperCAmelCase      ( self       :      int    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'do_resize'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'size'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'apply_ocr'    )    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size    ,      {'height': 1_8, 'width': 1_8}    )

        lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      Dict    )				->  str:
        pass





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Union[str, Any]:
        # Initialize image_processing
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowerCAmelCase     =			prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      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    ,      UpperCAmelCase__    )
        self.assertIsInstance(encoding.boxes    ,      UpperCAmelCase__    )

        # Test batched
        lowerCAmelCase     =			image_processing(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        # Initialize image_processing
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowerCAmelCase     =			prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=UpperCAmelCase__    ,      numpify=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      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(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Any:
        # Initialize image_processing
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowerCAmelCase     =			prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=UpperCAmelCase__    ,      torchify=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      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(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      str    )				->  Optional[int]:
        # with apply_OCR = True
        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(UpperCAmelCase__    ,      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
        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     =			[[[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    ,      UpperCAmelCase__    )
        self.assertListEqual(encoding.boxes    ,      UpperCAmelCase__    )

        # with apply_OCR = False
        lowerCAmelCase     =			LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__    )

        lowerCAmelCase     =			image_processing(UpperCAmelCase__    ,      return_tensors='pt'    )

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









'''simple docstring'''




import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__snake_case    						=logging.get_logger(__name__)


__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

__snake_case    						={"""facebook/blenderbot-3B""": 128}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	List[Any]		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	str		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     ['''input_ids''', '''attention_mask''']
     lowerCamelCase				:	List[Any]		=     BlenderbotTokenizer





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      List[Any]=None    ,      UpperCAmelCase__       :      str="replace"    ,      UpperCAmelCase__       :      Dict="<s>"    ,      UpperCAmelCase__       :      Tuple="</s>"    ,      UpperCAmelCase__       :      Optional[Any]="</s>"    ,      UpperCAmelCase__       :      Any="<s>"    ,      UpperCAmelCase__       :      List[str]="<unk>"    ,      UpperCAmelCase__       :      int="<pad>"    ,      UpperCAmelCase__       :      Union[str, Any]="<mask>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      **UpperCAmelCase__       :      Optional[int]    ,      )				->  int:
        super().__init__(
            UpperCAmelCase__    ,      UpperCAmelCase__    ,      tokenizer_file=UpperCAmelCase__    ,      errors=UpperCAmelCase__    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      sep_token=UpperCAmelCase__    ,      cls_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      pad_token=UpperCAmelCase__    ,      mask_token=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )

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

        lowerCAmelCase     =			add_prefix_space

        lowerCAmelCase     =			'post_processor'
        lowerCAmelCase     =			getattr(self.backend_tokenizer    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        if tokenizer_component_instance:
           lowerCAmelCase     =			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:
              lowerCAmelCase     =			tuple(state['sep']    )
           if "cls" in state:
              lowerCAmelCase     =			tuple(state['cls']    )

           lowerCAmelCase     =			False

           if state.get('add_prefix_space'    ,      UpperCAmelCase__    ) != add_prefix_space:
              lowerCAmelCase     =			add_prefix_space
              lowerCAmelCase     =			True

           if state.get('trim_offsets'    ,      UpperCAmelCase__    ) != trim_offsets:
              lowerCAmelCase     =			trim_offsets
              lowerCAmelCase     =			True

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





     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  str:
        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 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			AddedToken(UpperCAmelCase__    ,      lstrip=UpperCAmelCase__    ,      rstrip=UpperCAmelCase__    ) if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else value
        lowerCAmelCase     =			value





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      *UpperCAmelCase__       :      Union[str, Any]    ,      **UpperCAmelCase__       :      List[str]    )				->  BatchEncoding:
        lowerCAmelCase     =			kwargs.get('is_split_into_words'    ,      UpperCAmelCase__    )
        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

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





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      *UpperCAmelCase__       :      str    ,      **UpperCAmelCase__       :      List[str]    )				->  BatchEncoding:
        lowerCAmelCase     =			kwargs.get('is_split_into_words'    ,      UpperCAmelCase__    )

        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

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





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[str] = None    )				->  Tuple[str]:
        lowerCAmelCase     =			self._tokenizer.model.save(UpperCAmelCase__    ,      name=UpperCAmelCase__    )
        return tuple(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[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 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  Any:
        return token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      "Conversation"    )				->  List[int]:
        lowerCAmelCase     =			[]
        for is_user, text in conversation.iter_texts():
           if is_user:
              # We need to space prefix as it's being done within blenderbot
              inputs.append(' ' + text    )
           else:
              # Generated responses should contain them already.
              inputs.append(UpperCAmelCase__    )

        lowerCAmelCase     =			'  '.join(UpperCAmelCase__    )
        lowerCAmelCase     =			self.encode(UpperCAmelCase__    )
        if len(UpperCAmelCase__    ) > self.model_max_length:
           lowerCAmelCase     =			input_ids[-self.model_max_length :]
           logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''    )
        return input_ids





