

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 DetrImageProcessor




class      UpperCamelCase (     unittest.TestCase   ):

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Any=7							,    snake_case__ :      int=3							,    snake_case__ :      Any=3_0							,    snake_case__ :      int=4_0_0							,    snake_case__ :      int=True							,    snake_case__ :      Optional[int]=None							,    snake_case__ :      List[Any]=True							,    snake_case__ :      int=1 / 2_5_5							,    snake_case__ :      Tuple=True							,    snake_case__ :      Any=[0.5, 0.5, 0.5]							,    snake_case__ :      str=[0.5, 0.5, 0.5]							,    snake_case__ :      Tuple=True							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






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




    def UpperCamelCase			(			self :      int							,    snake_case__ :      List[str]							,    snake_case__ :      int=False    ):

     """simple docstring"""






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

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

     return expected_height, expected_width




@require_torch
@require_vision
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =DetrImageProcessor if is_vision_available() else None

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			DetrImageProcessingTester(self    )

    @property
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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_rescale'    )    )
     self.assertTrue(hasattr(UpperCamelCase__							,    'rescale_factor'    )    )
     self.assertTrue(hasattr(UpperCamelCase__							,    'do_resize'    )    )
     self.assertTrue(hasattr(UpperCamelCase__							,    'size'    )    )
     self.assertTrue(hasattr(UpperCamelCase__							,    'do_pad'    )    )

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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__    )

     SCREAMING_SNAKE_CASE								=			self.image_processing_class.from_dict(
         self.image_processor_dict							,    size=4_2							,    max_size=8_4							,    pad_and_return_pixel_mask=UpperCamelCase__    )
     self.assertEqual(image_processor.size							,    {'shortest_edge': 4_2, 'longest_edge': 8_4}    )
     self.assertEqual(image_processor.do_pad							,    UpperCamelCase__    )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     pass

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE								=			image_processing(image_inputs[0]							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.image_processor_tester.get_expected_values(UpperCamelCase__							,    batched=UpperCamelCase__    )

     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      str    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			image_processing(UpperCamelCase__							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			image_processing(UpperCamelCase__							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			{'image_id': 3_9_7_6_9, 'annotations': target}

     # encode them
     SCREAMING_SNAKE_CASE								=			DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'    )
     SCREAMING_SNAKE_CASE								=			image_processing(images=UpperCamelCase__							,    annotations=UpperCamelCase__							,    return_tensors='pt'    )

     # verify pixel values
     SCREAMING_SNAKE_CASE								=			torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
     self.assertEqual(encoding['pixel_values'].shape							,    UpperCamelCase__    )

     SCREAMING_SNAKE_CASE								=			torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,    UpperCamelCase__							,    atol=1E-4    )    )

     # verify area
     SCREAMING_SNAKE_CASE								=			torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,    UpperCamelCase__    )    )
     # verify boxes
     SCREAMING_SNAKE_CASE								=			torch.Size([6, 4]    )
     self.assertEqual(encoding['labels'][0]['boxes'].shape							,    UpperCamelCase__    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,    UpperCamelCase__							,    atol=1E-3    )    )
     # verify image_id
     SCREAMING_SNAKE_CASE								=			torch.tensor([3_9_7_6_9]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,    UpperCamelCase__    )    )
     # verify is_crowd
     SCREAMING_SNAKE_CASE								=			torch.tensor([0, 0, 0, 0, 0, 0]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,    UpperCamelCase__    )    )
     # verify class_labels
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			torch.tensor([4_8_0, 6_4_0]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,    UpperCamelCase__    )    )
     # verify size
     SCREAMING_SNAKE_CASE								=			torch.tensor([8_0_0, 1_0_6_6]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,    UpperCamelCase__    )    )




    @slow
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

     SCREAMING_SNAKE_CASE								=			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'    )

     # encode them
     SCREAMING_SNAKE_CASE								=			DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'    )
     SCREAMING_SNAKE_CASE								=			image_processing(images=UpperCamelCase__							,    annotations=UpperCamelCase__							,    masks_path=UpperCamelCase__							,    return_tensors='pt'    )

     # verify pixel values
     SCREAMING_SNAKE_CASE								=			torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
     self.assertEqual(encoding['pixel_values'].shape							,    UpperCamelCase__    )

     SCREAMING_SNAKE_CASE								=			torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,    UpperCamelCase__							,    atol=1E-4    )    )

     # verify area
     SCREAMING_SNAKE_CASE								=			torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,    UpperCamelCase__    )    )
     # verify boxes
     SCREAMING_SNAKE_CASE								=			torch.Size([6, 4]    )
     self.assertEqual(encoding['labels'][0]['boxes'].shape							,    UpperCamelCase__    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,    UpperCamelCase__							,    atol=1E-3    )    )
     # verify image_id
     SCREAMING_SNAKE_CASE								=			torch.tensor([3_9_7_6_9]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,    UpperCamelCase__    )    )
     # verify is_crowd
     SCREAMING_SNAKE_CASE								=			torch.tensor([0, 0, 0, 0, 0, 0]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,    UpperCamelCase__    )    )
     # verify class_labels
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			8_2_2_8_7_3
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()							,    UpperCamelCase__    )
     # verify orig_size
     SCREAMING_SNAKE_CASE								=			torch.tensor([4_8_0, 6_4_0]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,    UpperCamelCase__    )    )
     # verify size
     SCREAMING_SNAKE_CASE								=			torch.tensor([8_0_0, 1_0_6_6]    )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,    UpperCamelCase__    )    )




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


a_    :						Optional[int]    		=      logging.get_logger(__name__)


a_    :						Union[str, Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

a_    :						List[Any]    		=      {
    "allenai/led-base-16384": 1_6384,
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =LEDTokenizer
    __UpperCamelCase         =["input_ids", "attention_mask"]

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

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE								=			add_prefix_space

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

      SCREAMING_SNAKE_CASE								=			False

      if state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
       SCREAMING_SNAKE_CASE								=			add_prefix_space
       SCREAMING_SNAKE_CASE								=			True

      if state.get('trim_offsets'							,    snake_case__    ) != trim_offsets:
       SCREAMING_SNAKE_CASE								=			trim_offsets
       SCREAMING_SNAKE_CASE								=			True

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

    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """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 UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else value
     SCREAMING_SNAKE_CASE								=			value

    def UpperCamelCase			(			self :      Dict							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    snake_case__    )

     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(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    snake_case__    )

     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(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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 :      Optional[Any]							,    snake_case__ :      Union[Dict[str, EncodedInput], BatchEncoding]							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[bool] = None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			super()._pad(
         encoded_inputs=snake_case__							,    max_length=snake_case__							,    padding_strategy=snake_case__							,    pad_to_multiple_of=snake_case__							,    return_attention_mask=snake_case__							,    )

     # Load from model defaults
     if return_attention_mask is None:
      SCREAMING_SNAKE_CASE								=			'attention_mask' in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
      SCREAMING_SNAKE_CASE								=			encoded_inputs[self.model_input_names[0]]
      # `global_attention_mask` need to have the same length as other (sequential) inputs.
      SCREAMING_SNAKE_CASE								=			len(encoded_inputs['global_attention_mask']    ) != len(snake_case__    )

      if needs_to_be_padded:
       SCREAMING_SNAKE_CASE								=			len(snake_case__    ) - len(encoded_inputs['global_attention_mask']    )

       if self.padding_side == "right":
        # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
        SCREAMING_SNAKE_CASE								=			(
            encoded_inputs['global_attention_mask'] + [-1] * difference
        )
       elif self.padding_side == "left":
        SCREAMING_SNAKE_CASE								=			[-1] * difference + encoded_inputs[
            'global_attention_mask'
        ]
       else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side    )    )

     return encoded_inputs

