"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_pytesseract_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor


class        a				(		unittest.TestCase   ):


    def __init__(					self	:  int   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str=7   ,				__lowerCAmelCase	:  Dict=3   ,				__lowerCAmelCase	:  List[Any]=18   ,				__lowerCAmelCase	:  int=30   ,				__lowerCAmelCase	:  Any=400   ,				__lowerCAmelCase	:  Tuple=True   ,				__lowerCAmelCase	:  str=None   ,				__lowerCAmelCase	:  Optional[Any]=True   ,				):
      _UpperCAmelCase						 =      size if size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase						 =      parent
      _UpperCAmelCase						 =      batch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      image_size
      _UpperCAmelCase						 =      min_resolution
      _UpperCAmelCase						 =      max_resolution
      _UpperCAmelCase						 =      do_resize
      _UpperCAmelCase						 =      size
      _UpperCAmelCase						 =      apply_ocr


    def      lowerCAmelCase_  (					self	:  int					):
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  List[str]     							=							LayoutLMvaImageProcessor if is_pytesseract_available() else None


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      LayoutLMvaImageProcessingTester(self					)


    @property
    def      lowerCAmelCase_  (					self	:  Dict					):
      return self.image_processor_tester.prepare_image_processor_dict()


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      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	:  List[str]					):
      _UpperCAmelCase						 =      self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size   ,				{"""height""": 18, """width""": 18}					)

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


    def      lowerCAmelCase_  (					self	:  List[str]					):
      pass


    def      lowerCAmelCase_  (					self	:  List[str]					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				Image.Image					)

      # Test not batched input
      _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      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	:  Optional[int]					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      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						 =      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
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      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						 =      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	:  Optional[int]					):
      # with apply_OCR = True
      _UpperCAmelCase						 =      LayoutLMvaImageProcessor()

      from datasets import load_dataset

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

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

      _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      [["""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						 =      [[[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
      _UpperCAmelCase						 =      LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase					)

      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					)

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







"""simple docstring"""


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				:


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  str = None   ,				__lowerCAmelCase	:  uuid.UUID = None   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  Tuple=None					):
      if not conversation_id:
        _UpperCAmelCase						 =      uuid.uuida()
      if past_user_inputs is None:
        _UpperCAmelCase						 =      []
      if generated_responses is None:
        _UpperCAmelCase						 =      []

      _UpperCAmelCase						 =      conversation_id
      _UpperCAmelCase						 =      past_user_inputs
      _UpperCAmelCase						 =      generated_responses
      _UpperCAmelCase						 =      text


    def __eq__(					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        return False
      if self.uuid == other.uuid:
        return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  bool = False					):
      if self.new_user_input:
        if overwrite:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
              f'''with: "{text}".'''					)
          _UpperCAmelCase						 =      text
        else:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
              f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''					)
      else:
        _UpperCAmelCase						 =      text


    def      lowerCAmelCase_  (					self	:  int					):
      if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input					)
      _UpperCAmelCase						 =      None


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      self.generated_responses.append(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      for user_input, generated_response in zip(self.past_user_inputs   ,				self.generated_responses					):
        yield True, user_input
        yield False, generated_response
      if self.new_user_input:
        yield True, self.new_user_input


    def __repr__(					self	:  Optional[int]					):
      _UpperCAmelCase						 =      f'''Conversation id: {self.uuid} \n'''
      for is_user, text in self.iter_texts():
        _UpperCAmelCase						 =      """user""" if is_user else """bot"""
        output += f'''{name} >> {text} \n'''
      return output



@add_end_docstrings(
    lowerCAmelCase_	,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '	,							)
class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  List[Any]   ,				*__lowerCAmelCase	:  List[Any]   ,				**__lowerCAmelCase	:  Tuple					):
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.tokenizer.pad_token_id is None:
        _UpperCAmelCase						 =      self.tokenizer.eos_token


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  List[Any]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}

      if min_length_for_response is not None:
        _UpperCAmelCase						 =      min_length_for_response
      if minimum_tokens is not None:
        _UpperCAmelCase						 =      minimum_tokens

      if "max_length" in generate_kwargs:
        _UpperCAmelCase						 =      generate_kwargs["""max_length"""]
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
        _UpperCAmelCase						 =      clean_up_tokenization_spaces

      if generate_kwargs:
        forward_params.update(__lowerCAmelCase					)
      return preprocess_params, forward_params, postprocess_params


    def __call__(					self	:  Tuple   ,				__lowerCAmelCase	:  Union[Conversation, List[Conversation]]   ,				__lowerCAmelCase	:  int=0   ,				**__lowerCAmelCase	:  List[str]					):
      _UpperCAmelCase						 =      super().__call__(__lowerCAmelCase   ,				num_workers=__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					) and len(__lowerCAmelCase					) == 1:
        return outputs[0]
      return outputs


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Conversation   ,				__lowerCAmelCase	:  Optional[int]=32					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""					)
      if conversation.new_user_input is None:
        raise ValueError(
            f'''Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. '''
            """Add user inputs with the conversation's `add_user_input` method"""					)
      if hasattr(self.tokenizer   ,				"""_build_conversation_input_ids"""					):
        _UpperCAmelCase						 =      self.tokenizer._build_conversation_input_ids(__lowerCAmelCase					)
      else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        _UpperCAmelCase						 =      self._legacy_parse_and_tokenize(__lowerCAmelCase					)

      if self.framework == "pt":
        _UpperCAmelCase						 =      torch.LongTensor([input_ids]					)
      elif self.framework == "tf":
        _UpperCAmelCase						 =      tf.constant([input_ids]					)
      return {"input_ids": input_ids, "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]=10   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      generate_kwargs.get("""max_length"""   ,				self.model.config.max_length					)

      _UpperCAmelCase						 =      model_inputs["""input_ids"""].shape[1]
      if max_length - minimum_tokens < n:
        logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''					)
        _UpperCAmelCase						 =      max_length - minimum_tokens
        _UpperCAmelCase						 =      model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
          _UpperCAmelCase						 =      model_inputs["""attention_mask"""][:, -trim:]
      _UpperCAmelCase						 =      model_inputs.pop("""conversation"""					)
      _UpperCAmelCase						 =      max_length
      _UpperCAmelCase						 =      self.model.generate(**__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.model.config.is_encoder_decoder:
        _UpperCAmelCase						 =      1
      else:
        _UpperCAmelCase						 =      n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]=True					):
      _UpperCAmelCase						 =      model_outputs["""output_ids"""]
      _UpperCAmelCase						 =      self.tokenizer.decode(
          output_ids[0]   ,				skip_special_tokens=__lowerCAmelCase   ,				clean_up_tokenization_spaces=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__lowerCAmelCase					)
      return conversation


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Conversation					):
      _UpperCAmelCase						 =      self.tokenizer.eos_token_id
      _UpperCAmelCase						 =      []
      for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					) + [eos_token_id]					)
        else:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					)					)

      if len(__lowerCAmelCase					) > self.tokenizer.model_max_length:
        _UpperCAmelCase						 =      input_ids[-self.tokenizer.model_max_length :]
      return input_ids
