


'''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    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=7    ,   UpperCamelCase_=3    ,   UpperCamelCase_=18    ,   UpperCamelCase_=30    ,   UpperCamelCase_=400    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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



@require_torch
@require_pytesseract
class    lowercase	(							A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				LayoutLMvaImageProcessor if is_pytesseract_available() else None




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= LayoutLMvaImageProcessingTester(self							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size    ,   {'''height''': 18, '''width''': 18}							)

							UpperCamelCase__   :Optional[Any]												= self.image_processing_class.from_dict(self.image_processor_dict    ,   size=42							)
							self.assertEqual(image_processor.size    ,   {'''height''': 42, '''width''': 42}							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__   :Optional[Any]												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   Image.Image							)

							# Test not batched input
							UpperCamelCase__   :List[str]												= image_processing(image_inputs[0]    ,   return_tensors='''pt'''							)
							self.assertEqual(
							    encoding.pixel_values.shape    ,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )    ,   )

							self.assertIsInstance(encoding.words    ,   UpperCamelCase_							)
							self.assertIsInstance(encoding.boxes    ,   UpperCamelCase_							)

							# Test batched
							UpperCamelCase__   :int												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							UpperCamelCase__   :Any												= 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
							UpperCamelCase__   :Optional[int]												= 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
							UpperCamelCase__   :Tuple												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							UpperCamelCase__   :List[str]												= 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
							UpperCamelCase__   :Union[str, Any]												= image_processing(image_inputs[0]    ,   return_tensors='''pt'''							).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )    ,   )

							# Test batched
							UpperCamelCase__   :Dict												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= LayoutLMvaImageProcessor()

							from datasets import load_dataset

							UpperCamelCase__   :int												= load_dataset('''hf-internal-testing/fixtures_docvqa'''    ,   split='''test'''							)

							UpperCamelCase__   :int												= Image.open(ds[0]['''file''']							).convert('''RGB'''							)

							UpperCamelCase__   :str												= image_processing(UpperCamelCase_    ,   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
							UpperCamelCase__   :Tuple												= [['''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
							UpperCamelCase__   :Dict												= [[[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    ,   UpperCamelCase_							)
							self.assertListEqual(encoding.boxes    ,   UpperCamelCase_							)

							# with apply_OCR = False
							UpperCamelCase__   :List[str]												= LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase_							)

							UpperCamelCase__   :Tuple												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							)

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



'''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





