code: 
  
  
  import json
  import os
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
  
  lowerCAmelCase__   	=				{
      'vocab_file': {
          'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
          'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
          'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
          'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
          'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
          'microsoft/deberta-xlarge-mnli': (
              'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
          ),
      },
      'merges_file': {
          'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
          'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
          'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
          'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
          'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
          'microsoft/deberta-xlarge-mnli': (
              'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
          ),
      },
  }
  
  lowerCAmelCase__   	=				{
      'microsoft/deberta-base': 5_12,
      'microsoft/deberta-large': 5_12,
      'microsoft/deberta-xlarge': 5_12,
      'microsoft/deberta-base-mnli': 5_12,
      'microsoft/deberta-large-mnli': 5_12,
      'microsoft/deberta-xlarge-mnli': 5_12,
  }
  
  lowerCAmelCase__   	=				{
      'microsoft/deberta-base': {'do_lower_case': False},
      'microsoft/deberta-large': {'do_lower_case': False},
  }
  def 							_UpperCAmelCase       ():
         _A   : List[str]       					=				(
             list(range(ord("!"		)       ,							ord("~"		) + 1		)		) + list(range(ord("¡"		)       ,							ord("¬"		) + 1		)		) + list(range(ord("®"		)       ,							ord("ÿ"		) + 1		)		)
         )
         _A   : Dict       					=				bs[:]
         _A   : Tuple       					=				0
         for b in range(2**8		):
                if b not in bs:
                       bs.append(UpperCamelCase__		)
                       cs.append(2**8 + n		)
                       n += 1
         _A   : List[str]       					=				[chr(UpperCamelCase__		) for n in cs]
         return dict(zip(UpperCamelCase__       ,							UpperCamelCase__		)		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]		):
         _A   : List[str]       					=				set()
         _A   : str       					=				word[0]
         for char in word[1:]:
                pairs.add((prev_char, char)		)
                _A   : List[Any]       					=				char
         return pairs
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE						    =				["input_ids", "attention_mask", "token_type_ids"]
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase="replace"				,		__lowerCamelCase="[CLS]"				,		__lowerCamelCase="[SEP]"				,		__lowerCamelCase="[SEP]"				,		__lowerCamelCase="[CLS]"				,		__lowerCamelCase="[UNK]"				,		__lowerCamelCase="[PAD]"				,		__lowerCamelCase="[MASK]"				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		**__lowerCamelCase				,		)    ->       int:
              _A   : Optional[Any]       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else bos_token
              _A   : Optional[Any]       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else eos_token
              _A   : Dict       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else sep_token
              _A   : int       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else cls_token
              _A   : Union[str, Any]       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else unk_token
              _A   : List[str]       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else pad_token
  
              # Mask token behave like a normal word, i.e. include the space before it
              _A   : Any       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else mask_token
  
              super().__init__(
                  errors=__lowerCamelCase				,		bos_token=__lowerCamelCase				,		eos_token=__lowerCamelCase				,		unk_token=__lowerCamelCase				,		sep_token=__lowerCamelCase				,		cls_token=__lowerCamelCase				,		pad_token=__lowerCamelCase				,		mask_token=__lowerCamelCase				,		add_prefix_space=__lowerCamelCase				,		add_bos_token=__lowerCamelCase				,		**__lowerCamelCase				,		)
              _A   : List[Any]       					=				add_bos_token
  
              with open(__lowerCamelCase				,		encoding="utf-8") as vocab_handle:
                     _A   : int       					=				json.load(__lowerCamelCase)
              _A   : str       					=				{v: k for k, v in self.encoder.items()}
              _A   : int       					=				errors  # how to handle errors in decoding
              _A   : Optional[int]       					=				bytes_to_unicode()
              _A   : Tuple       					=				{v: k for k, v in self.byte_encoder.items()}
              with open(__lowerCamelCase				,		encoding="utf-8") as merges_handle:
                     _A   : int       					=				merges_handle.read().split("\n")[1:-1]
              _A   : Union[str, Any]       					=				[tuple(merge.split()) for merge in bpe_merges]
              _A   : List[Any]       					=				dict(zip(__lowerCamelCase				,		range(len(__lowerCamelCase))))
              _A   : Optional[Any]       					=				{}
              _A   : Tuple       					=				add_prefix_space
  
              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              _A   : List[str]       					=				re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")
  
  
       @property
       # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              return len(self.encoder)
  
  
       def 				_lowerCamelCase				(		self)    ->       int:
              return dict(self.encoder				,		**self.added_tokens_encoder)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Tuple:
              if token in self.cache:
                     return self.cache[token]
              _A   : Union[str, Any]       					=				tuple(__lowerCamelCase)
              _A   : int       					=				get_pairs(__lowerCamelCase)
  
              if not pairs:
                     return token
  
              while True:
                     _A   : int       					=				min(__lowerCamelCase				,		key=lambda __lowerCamelCase: self.bpe_ranks.get(__lowerCamelCase				,		float("inf")))
                     if bigram not in self.bpe_ranks:
                            break
                     _A					,    _A   : List[Any]       					=				bigram
                     _A   : Dict       					=				[]
                     _A   : Dict       					=				0
                     while i < len(__lowerCamelCase):
                            try:
                                   _A   : Dict       					=				word.index(__lowerCamelCase				,		__lowerCamelCase)
                            except ValueError:
                                   new_word.extend(word[i:])
                                   break
                            else:
                                   new_word.extend(word[i:j])
                                   _A   : Tuple       					=				j
  
                            if word[i] == first and i < len(__lowerCamelCase) - 1 and word[i + 1] == second:
                                   new_word.append(first + second)
                                   i += 2
                            else:
                                   new_word.append(word[i])
                                   i += 1
                     _A   : Optional[Any]       					=				tuple(__lowerCamelCase)
                     _A   : Any       					=				new_word
                     if len(__lowerCamelCase) == 1:
                            break
                     else:
                            _A   : Optional[int]       					=				get_pairs(__lowerCamelCase)
              _A   : List[str]       					=				" ".join(__lowerCamelCase)
              _A   : Optional[Any]       					=				word
              return word
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       List[int]:
              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              _A   : Optional[int]       					=				[self.cls_token_id]
              _A   : int       					=				[self.sep_token_id]
              return cls + token_ids_a + sep + token_ids_a + sep
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = False)    ->       List[int]:
              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=__lowerCamelCase				,		token_ids_a=__lowerCamelCase				,		already_has_special_tokens=__lowerCamelCase)
  
              if token_ids_a is None:
                     return [1] + ([0] * len(__lowerCamelCase)) + [1]
              return [1] + ([0] * len(__lowerCamelCase)) + [1] + ([0] * len(__lowerCamelCase)) + [1]
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       List[int]:
              _A   : Optional[Any]       					=				[self.sep_token_id]
              _A   : Union[str, Any]       					=				[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) * [0] + len(token_ids_a + sep) * [1]
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       str:
              _A   : Optional[Any]       					=				[]
              for token in re.findall(self.pat				,		__lowerCamelCase):
                     _A   : Tuple       					=				"".join(
                         self.byte_encoder[b] for b in token.encode("utf-8"))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase).split(" "))
              return bpe_tokens
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       List[str]:
              return self.encoder.get(__lowerCamelCase				,		self.encoder.get(self.unk_token))
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Optional[Any]:
              return self.decoder.get(__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Tuple:
              _A   : List[Any]       					=				"".join(__lowerCamelCase)
              _A   : Optional[int]       					=				bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"				,		errors=self.errors)
              return text
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       Tuple[str]:
              if not os.path.isdir(__lowerCamelCase):
                     logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                     return
              _A   : Optional[int]       					=				os.path.join(
                  __lowerCamelCase				,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
              _A   : Optional[Any]       					=				os.path.join(
                  __lowerCamelCase				,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])
  
              with open(__lowerCamelCase				,		"w"				,		encoding="utf-8") as f:
                     f.write(json.dumps(self.encoder				,		indent=2				,		sort_keys=__lowerCamelCase				,		ensure_ascii=__lowerCamelCase) + "\n")
  
              _A   : Dict       					=				0
              with open(__lowerCamelCase				,		"w"				,		encoding="utf-8") as writer:
                     writer.write("#version: 0.2\n")
                     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,		key=lambda __lowerCamelCase: kv[1]):
                            if index != token_index:
                                   logger.warning(
                                       F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                       " Please check that the tokenizer is not corrupted!")
                                   _A   : Optional[int]       					=				token_index
                            writer.write(" ".join(__lowerCamelCase) + "\n")
                            index += 1
  
              return vocab_file, merge_file
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=False				,		**__lowerCamelCase)    ->       Dict:
              _A   : List[Any]       					=				kwargs.pop("add_prefix_space"				,		self.add_prefix_space)
              if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase) > 0 and not text[0].isspace()):
                     _A   : Optional[int]       					=				" " + text
              return (text, kwargs)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       List[int]:
              _A   : Dict       					=				[]
              for is_user, text in conversation.iter_texts():
                     input_ids.extend(self.encode(__lowerCamelCase				,		add_special_tokens=__lowerCamelCase) + [self.eos_token_id])
              if len(__lowerCamelCase) > self.model_max_length:
                     _A   : Dict       					=				input_ids[-self.model_max_length :]
              return input_ids
  
style_context: 
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  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 MobileNetVaImageProcessor
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=7				,		__lowerCamelCase=3				,		__lowerCamelCase=1_8				,		__lowerCamelCase=3_0				,		__lowerCamelCase=4_0_0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
              _A   : int       					=				size if size is not None else {"shortest_edge": 2_0}
              _A   : Tuple       					=				crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
              _A   : str       					=				parent
              _A   : Any       					=				batch_size
              _A   : Dict       					=				num_channels
              _A   : int       					=				image_size
              _A   : Union[str, Any]       					=				min_resolution
              _A   : Tuple       					=				max_resolution
              _A   : Any       					=				do_resize
              _A   : Optional[int]       					=				size
              _A   : Tuple       					=				do_center_crop
              _A   : List[str]       					=				crop_size
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
              }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class   lowerCAmelCase__ (       a  ,					unittest.TestCase):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				MobileNetVaImageProcessor if is_vision_available() else None
  
  
       def 				_lowerCamelCase				(		self)    ->       Any:
              _A   : Dict       					=				MobileNetVaImageProcessingTester(self)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       Dict:
              return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_resize"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"size"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_center_crop"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"center_crop"))
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : int       					=				self.image_processing_class.from_dict(self.image_processor_dict)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 2_0})
              self.assertEqual(image_processor.crop_size				,		{"height": 1_8, "width": 1_8})
  
              _A   : Any       					=				self.image_processing_class.from_dict(self.image_processor_dict				,		size=4_2				,		crop_size=8_4)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 4_2})
              self.assertEqual(image_processor.crop_size				,		{"height": 8_4, "width": 8_4})
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              pass
  
  
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              # Initialize image_processing
              _A   : Optional[int]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PIL images
              _A   : List[Any]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		Image.Image)
  
              # Test not batched input
              _A   : str       					=				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.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Dict       					=				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.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              # Initialize image_processing
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random numpy tensors
              _A   : int       					=				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
              _A   : Tuple       					=				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.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : str       					=				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.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              # Initialize image_processing
              _A   : str       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PyTorch tensors
              _A   : List[str]       					=				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
              _A   : str       					=				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.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Any       					=				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.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  