
"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


lowercase_						    =					logging.get_logger(__name__)

lowercase_						    =					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_						    =					{
    "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"
        ),
    },
}

lowercase_						    =					{
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowercase_						    =					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}



class 		UpperCAmelCase_	(SCREAMING_SNAKE_CASE__						):
	"""simple docstring"""




	UpperCamelCase_						:     str				   =    VOCAB_FILES_NAMES
	UpperCamelCase_						:     int				   =    PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase_						:     Tuple				   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCamelCase_						:     List[Any]				   =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
	UpperCamelCase_						:     str				   =    DebertaTokenizer




	def __init__( self       : Tuple     ,   a_       : int=None     ,   a_       : Any=None     ,   a_       : Tuple=None     ,   a_       : Dict="replace"     ,   a_       : Tuple="[CLS]"     ,   a_       : Optional[Any]="[SEP]"     ,   a_       : Tuple="[SEP]"     ,   a_       : Any="[CLS]"     ,   a_       : List[Any]="[UNK]"     ,   a_       : Any="[PAD]"     ,   a_       : Any="[MASK]"     ,   a_       : Any=False     ,   **a_       : Union[str, Any]     ,   )->					List[str]:



						"""simple docstring"""

						super().__init__(
						    a_     ,   a_     ,   tokenizer_file=a_     ,   errors=a_     ,   bos_token=a_     ,   eos_token=a_     ,   unk_token=a_     ,   sep_token=a_     ,   cls_token=a_     ,   pad_token=a_     ,   mask_token=a_     ,   add_prefix_space=a_     ,   **a_     ,   )
						UpperCAmelCase_						:    Optional[int]    					=  kwargs.pop("""add_bos_token"""     ,   a_ )

						UpperCAmelCase_						:    Optional[Any]    					=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
						if pre_tok_state.get("""add_prefix_space"""     ,   a_ ) != add_prefix_space:
											UpperCAmelCase_						:    Union[str, Any]    					=  getattr(a_     ,   pre_tok_state.pop("""type""" ) )
											UpperCAmelCase_						:    int    					=  add_prefix_space
											UpperCAmelCase_						:    Any    					=  pre_tok_class(**a_ )

						UpperCAmelCase_						:    List[str]    					=  add_prefix_space




	@property
	def   a      ( self       : str )->					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   a      ( self       : Dict     ,   a_       : List[str] )->					Tuple:



						"""simple docstring"""

						UpperCAmelCase_						:    Optional[Any]    					=  AddedToken(a_     ,   lstrip=a_     ,   rstrip=a_ ) if isinstance(a_     ,   a_ ) else value
						UpperCAmelCase_						:    int    					=  value




	def   a      ( self       : List[str]     ,   a_       : Optional[int]     ,   a_       : Union[str, Any] = None )->					List[int]:



						"""simple docstring"""

						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						UpperCAmelCase_						:    Optional[Any]    					=  [self.cls_token_id]
						UpperCAmelCase_						:    str    					=  [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep




	def   a      ( self       : List[Any]     ,   a_       : Optional[Any]     ,   a_       : Union[str, Any] = None )->					List[int]:



						"""simple docstring"""

						UpperCAmelCase_						:    str    					=  [self.sep_token_id]
						UpperCAmelCase_						:    List[str]    					=  [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   a      ( self       : Optional[int]     ,   *a_       : str     ,   **a_       : List[str] )->					BatchEncoding:



						"""simple docstring"""

						UpperCAmelCase_						:    List[str]    					=  kwargs.get("""is_split_into_words"""     ,   a_ )
						assert self.add_prefix_space or not is_split_into_words, (
						    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*a_     ,   **a_ )




	def   a      ( self       : Tuple     ,   *a_       : Tuple     ,   **a_       : str )->					BatchEncoding:



						"""simple docstring"""

						UpperCAmelCase_						:    Optional[int]    					=  kwargs.get("""is_split_into_words"""     ,   a_ )

						assert self.add_prefix_space or not is_split_into_words, (
						    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*a_     ,   **a_ )




	def   a      ( self       : Union[str, Any]     ,   a_       : Tuple     ,   a_       : Any = None )->					Tuple[str]:



						"""simple docstring"""

						UpperCAmelCase_						:    Union[str, Any]    					=  self._tokenizer.model.save(a_     ,   name=a_ )
						return tuple(a_ )






	def   a      ( self       : Tuple     ,   a_       : Optional[int] )->					List[int]:



						"""simple docstring"""

						UpperCAmelCase_						:    Dict    					=  []
						for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(a_     ,   add_special_tokens=a_ ) + [self.eos_token_id] )

						if len(a_ ) > self.model_max_length:
											UpperCAmelCase_						:    str    					=  input_ids[-self.model_max_length :]
						return input_ids



"""simple docstring"""







import unittest

from transformers.image_processing_utils import get_size_dict



class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 def   a      ( self       : Union[str, Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

      # Test a single int value which represents the shortest edge
      UpperCAmelCase_						:    List[str]    					=  get_size_dict(2_24     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      # Test a tuple of ints which represents (height, width)
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict((1_50, 2_00) )
      self.assertEqual(a_     ,   {"""height""": 1_50, """width""": 2_00} )

      # Test a tuple of ints which represents (width, height)
      UpperCAmelCase_						:    List[str]    					=  get_size_dict((1_50, 2_00)     ,   height_width_order=a_ )
      self.assertEqual(a_     ,   {"""height""": 2_00, """width""": 1_50} )

      # Test an int representing the shortest edge and max_size which represents the longest edge
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24, """longest_edge""": 2_56} )

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(a_ ):
           get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )

