import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DetaImageProcessor





class 					A							(    unittest.TestCase					):


   def __init__(self					:						Any	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Any=7	,  __UpperCAmelCase					:						Union[str, Any]=3	,  __UpperCAmelCase					:						Any=3_0	,  __UpperCAmelCase					:						Optional[Any]=4_0_0	,  __UpperCAmelCase					:						Optional[int]=True	,  __UpperCAmelCase					:						Union[str, Any]=None	,  __UpperCAmelCase					:						Union[str, Any]=True	,  __UpperCAmelCase					:						Dict=[0.5, 0.5, 0.5]	,  __UpperCAmelCase					:						Optional[int]=[0.5, 0.5, 0.5]	,  __UpperCAmelCase					:						List[Any]=True	,  __UpperCAmelCase					:						Optional[int]=1 / 2_5_5	,  __UpperCAmelCase					:						Tuple=True	,  )						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  min_resolution
         UpperCAmelCase__								=  max_resolution
         UpperCAmelCase__								=  do_resize
         UpperCAmelCase__								=  size
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  image_mean
         UpperCAmelCase__								=  image_std
         UpperCAmelCase__								=  do_rescale
         UpperCAmelCase__								=  rescale_factor
         UpperCAmelCase__								=  do_pad


   def       lowercase_				(self					:						Dict		)						->    int:
         """simple docstring"""



         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }





   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						Dict=False		)						->    List[str]:
         """simple docstring"""



         if not batched:
               UpperCAmelCase__								=  image_inputs[0]
               if isinstance(__UpperCAmelCase	,  Image.Image		):
                     UpperCAmelCase__		,				UpperCAmelCase__								=  image.size
               else:
                     UpperCAmelCase__		,				UpperCAmelCase__								=  image.shape[1], image.shape[2]
               if w < h:
                     UpperCAmelCase__								=  int(self.size["shortest_edge"] * h / w		)
                     UpperCAmelCase__								=  self.size["shortest_edge"]
               elif w > h:
                     UpperCAmelCase__								=  self.size["shortest_edge"]
                     UpperCAmelCase__								=  int(self.size["shortest_edge"] * w / h		)
               else:
                     UpperCAmelCase__								=  self.size["shortest_edge"]
                     UpperCAmelCase__								=  self.size["shortest_edge"]

         else:
               UpperCAmelCase__								=  []
               for image in image_inputs:
                     UpperCAmelCase__		,				UpperCAmelCase__								=  self.get_expected_values([image]		)
                     expected_values.append((expected_height, expected_width)		)
               UpperCAmelCase__								=  max(__UpperCAmelCase	,  key=lambda __UpperCAmelCase		: item[0]		)[0]
               UpperCAmelCase__								=  max(__UpperCAmelCase	,  key=lambda __UpperCAmelCase		: item[1]		)[1]

         return expected_height, expected_width





@require_torch
@require_vision
class 					A							(    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			str              =      DetaImageProcessor if is_vision_available() else None


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  DetaImageProcessingTester(self		)


   @property
   def       lowercase_				(self					:						Optional[Any]		)						->    List[str]:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def       lowercase_				(self					:						List[str]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(__UpperCAmelCase	,  "image_mean"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase	,  "image_std"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase	,  "do_normalize"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase	,  "do_resize"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase	,  "do_rescale"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase	,  "do_pad"		)		)
         self.assertTrue(hasattr(__UpperCAmelCase	,  "size"		)		)


   def       lowercase_				(self					:						List[str]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size	,  {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}		)
         self.assertEqual(image_processor.do_pad	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Dict		)						->    Optional[Any]:
         """simple docstring"""



         pass


   def       lowercase_				(self					:						List[Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         # create random PIL images
         UpperCAmelCase__								=  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__								=  image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.image_processor_tester.get_expected_values(__UpperCAmelCase		)

         self.assertEqual(
             encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

         # Test batched
         UpperCAmelCase__		,				UpperCAmelCase__								=  self.image_processor_tester.get_expected_values(__UpperCAmelCase	,  batched=__UpperCAmelCase		)

         UpperCAmelCase__								=  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,
                 expected_height,
                 expected_width,
             )	,  )


   def       lowercase_				(self					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         UpperCAmelCase__								=  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__								=  image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.image_processor_tester.get_expected_values(__UpperCAmelCase		)

         self.assertEqual(
             encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

         # Test batched
         UpperCAmelCase__								=  image_processing(__UpperCAmelCase	,  return_tensors="pt"		).pixel_values

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.image_processor_tester.get_expected_values(__UpperCAmelCase	,  batched=__UpperCAmelCase		)

         self.assertEqual(
             encoded_images.shape	,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )	,  )


   def       lowercase_				(self					:						Optional[int]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict		)
         # create random PyTorch tensors
         UpperCAmelCase__								=  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__								=  image_processing(image_inputs[0]	,  return_tensors="pt"		).pixel_values

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.image_processor_tester.get_expected_values(__UpperCAmelCase		)

         self.assertEqual(
             encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

         # Test batched
         UpperCAmelCase__								=  image_processing(__UpperCAmelCase	,  return_tensors="pt"		).pixel_values

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.image_processor_tester.get_expected_values(__UpperCAmelCase	,  batched=__UpperCAmelCase		)

         self.assertEqual(
             encoded_images.shape	,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )	,  )


   @slow
   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,  "r"		) as f:
               UpperCAmelCase__								=  json.loads(f.read()		)

         UpperCAmelCase__								=  {"image_id": 3_9_7_6_9, "annotations": target}

         # encode them
         UpperCAmelCase__								=  DetaImageProcessor()
         UpperCAmelCase__								=  image_processing(images=__UpperCAmelCase	,  annotations=__UpperCAmelCase	,  return_tensors="pt"		)

         # verify pixel values
         UpperCAmelCase__								=  torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
         self.assertEqual(encoding["pixel_values"].shape	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  torch.tensor([0.2796, 0.3138, 0.3481]		)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,  __UpperCAmelCase	,  atol=1E-4		)		)

         # verify area
         UpperCAmelCase__								=  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,  __UpperCAmelCase		)		)
         # verify boxes
         UpperCAmelCase__								=  torch.Size([6, 4]		)
         self.assertEqual(encoding["labels"][0]["boxes"].shape	,  __UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,  __UpperCAmelCase	,  atol=1E-3		)		)
         # verify image_id
         UpperCAmelCase__								=  torch.tensor([3_9_7_6_9]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,  __UpperCAmelCase		)		)
         # verify is_crowd
         UpperCAmelCase__								=  torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,  __UpperCAmelCase		)		)
         # verify class_labels
         UpperCAmelCase__								=  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  __UpperCAmelCase		)		)
         # verify orig_size
         UpperCAmelCase__								=  torch.tensor([4_8_0, 6_4_0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  __UpperCAmelCase		)		)
         # verify size
         UpperCAmelCase__								=  torch.tensor([8_0_0, 1_0_6_6]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  __UpperCAmelCase		)		)





   @slow
   def       lowercase_				(self					:						List[Any]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"	,  "r"		) as f:
               UpperCAmelCase__								=  json.loads(f.read()		)

         UpperCAmelCase__								=  {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

         UpperCAmelCase__								=  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

         # encode them
         UpperCAmelCase__								=  DetaImageProcessor(format="coco_panoptic"		)
         UpperCAmelCase__								=  image_processing(images=__UpperCAmelCase	,  annotations=__UpperCAmelCase	,  masks_path=__UpperCAmelCase	,  return_tensors="pt"		)

         # verify pixel values
         UpperCAmelCase__								=  torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
         self.assertEqual(encoding["pixel_values"].shape	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  torch.tensor([0.2796, 0.3138, 0.3481]		)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,  __UpperCAmelCase	,  atol=1E-4		)		)

         # verify area
         UpperCAmelCase__								=  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,  __UpperCAmelCase		)		)
         # verify boxes
         UpperCAmelCase__								=  torch.Size([6, 4]		)
         self.assertEqual(encoding["labels"][0]["boxes"].shape	,  __UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,  __UpperCAmelCase	,  atol=1E-3		)		)
         # verify image_id
         UpperCAmelCase__								=  torch.tensor([3_9_7_6_9]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,  __UpperCAmelCase		)		)
         # verify is_crowd
         UpperCAmelCase__								=  torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,  __UpperCAmelCase		)		)
         # verify class_labels
         UpperCAmelCase__								=  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  __UpperCAmelCase		)		)
         # verify masks
         UpperCAmelCase__								=  8_2_2_8_7_3
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,  __UpperCAmelCase		)
         # verify orig_size
         UpperCAmelCase__								=  torch.tensor([4_8_0, 6_4_0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  __UpperCAmelCase		)		)
         # verify size
         UpperCAmelCase__								=  torch.tensor([8_0_0, 1_0_6_6]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  __UpperCAmelCase		)		)




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


UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

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

UpperCamelCase__    							= {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[str]              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			int              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			int              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			str              =      ['input_ids', 'attention_mask']
   __UpperCAmelCase			:			str              =      BartTokenizer


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



         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	,  )

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

         UpperCAmelCase__								=  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase__								=  "post_processor"
         UpperCAmelCase__								=  getattr(self.backend_tokenizer	,  __UpperCAmelCase	,  __UpperCAmelCase		)
         if tokenizer_component_instance:
               UpperCAmelCase__								=  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:
                     UpperCAmelCase__								=  tuple(state["sep"]		)
               if "cls" in state:
                     UpperCAmelCase__								=  tuple(state["cls"]		)

               UpperCAmelCase__								=  False

               if state.get("add_prefix_space"	,  __UpperCAmelCase		) != add_prefix_space:
                     UpperCAmelCase__								=  add_prefix_space
                     UpperCAmelCase__								=  True

               if state.get("trim_offsets"	,  __UpperCAmelCase		) != trim_offsets:
                     UpperCAmelCase__								=  trim_offsets
                     UpperCAmelCase__								=  True

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


   @property
   def       lowercase_				(self					:						Tuple		)						->    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       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Union[str, Any]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else value
         UpperCAmelCase__								=  value


   def       lowercase_				(self					:						List[str]	,  *__UpperCAmelCase					:						str	,  **__UpperCAmelCase					:						List[str]		)						->    BatchEncoding:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"	,  __UpperCAmelCase		)

         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(*__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]	,  *__UpperCAmelCase					:						Optional[Any]	,  **__UpperCAmelCase					:						Optional[Any]		)						->    BatchEncoding:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"	,  __UpperCAmelCase		)

         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(*__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    Tuple[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self._tokenizer.model.save(__UpperCAmelCase	,  name=__UpperCAmelCase		)
         return tuple(__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Dict=None		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [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       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List[int]	,  __UpperCAmelCase					:						Optional[List[int]] = None		)						->    List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [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]



