





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


__A       :					Tuple     					=     logging.get_logger(__name__)

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

__A       :					int     					=     {
    "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"
        ),
    },
}

__A       :					Optional[int]     					=     {
    "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,
}

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

class 			A_ (a_    ):
	UpperCAmelCase__      =    VOCAB_FILES_NAMES
	UpperCAmelCase__      =    PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__      =    ['''input_ids''', '''attention_mask''', '''token_type_ids''']
	UpperCAmelCase__      =    DebertaTokenizer
	def __init__(					self							,      _A=None							,      _A=None							,      _A=None							,      _A="replace"							,      _A="[CLS]"							,      _A="[SEP]"							,      _A="[SEP]"							,      _A="[CLS]"							,      _A="[UNK]"							,      _A="[PAD]"							,      _A="[MASK]"							,      _A=False							,      **_A							,      ):






				'''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     	=       kwargs.pop('''add_bos_token'''							,      _A	)

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

				UpperCAmelCase     	=       add_prefix_space
	@property
	def   _lowercase       (					self	):






				'''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							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       AddedToken(_A							,      lstrip=_A							,      rstrip=_A	) if isinstance(_A							,      _A	) else value
				UpperCAmelCase     	=       value
	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase     	=       [self.cls_token_id]
				UpperCAmelCase     	=       [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep
	def   _lowercase       (					self							,      _A							,      _A = None	):






				'''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	) * [0] + len(token_ids_a + sep	) * [1]
	def   _lowercase       (					self							,      *_A							,      **_A	):






				'''simple docstring'''







				UpperCAmelCase     	=       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   _lowercase       (					self							,      *_A							,      **_A	):






				'''simple docstring'''







				UpperCAmelCase     	=       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   _lowercase       (					self							,      _A							,      _A = None	):






				'''simple docstring'''







				UpperCAmelCase     	=       self._tokenizer.model.save(_A							,      name=_A	)
				return tuple(_A	)







	def   _lowercase       (					self							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       []
				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     	=       input_ids[-self.model_max_length :]
				return input_ids










import numpy as np





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__="utf-8"    ,    UpperCamelCase__="surrogatepass"				)						->       Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       bin(int.from_bytes(text.encode(UpperCamelCase__    ,    UpperCamelCase__				)    ,    '''big'''				)				)[2:]
			return bits.zfill(8 * ((len(UpperCamelCase__				) + 7) // 8)				)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__="utf-8"    ,    UpperCamelCase__="surrogatepass"				)						->       str:







			'''simple docstring'''

			UpperCAmelCase     	=       int(UpperCamelCase__    ,    2				)
			return n.to_bytes((n.bit_length() + 7) // 8    ,    '''big'''				).decode(UpperCamelCase__    ,    UpperCamelCase__				) or "\0"





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       Tuple:







			'''simple docstring'''

			if size_par + len(UpperCamelCase__				) <= 2**size_par - (len(UpperCamelCase__				) - 1):
						raise ValueError('''size of parity don\'t match with size of data'''				)

			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       [bin(UpperCamelCase__				)[2:] for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				)]

			# sorted information data for the size of the output data
			UpperCAmelCase     	=       []
			# data position template + parity
			UpperCAmelCase     	=       []
			# parity bit counter
			UpperCAmelCase     	=       0
			# counter position of data bits
			UpperCAmelCase     	=       0

			for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
												data_out_gab.append('''P'''				)
												UpperCAmelCase     	=       qtd_bp + 1
									else:
												data_out_gab.append('''D'''				)
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]				)
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__				)

    # Calculates parity
			UpperCAmelCase     	=       0  # parity bit counter
			for bp in range(1    ,    size_par + 1				):
						# Bit counter one for a given parity
						UpperCAmelCase     	=       0
						# counter to control the loop reading
						UpperCAmelCase     	=       0
						for x in data_ord:
									if x is not None:
												try:
															UpperCAmelCase     	=       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCAmelCase     	=       '''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2				)

						qtd_bp += 1

			# Mount the message
			UpperCAmelCase     	=       0  # parity bit counter
			for x in range(0    ,    size_par + len(UpperCamelCase__				)				):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]				)				)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]				)

			return data_out





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       Optional[int]:







			'''simple docstring'''

			UpperCAmelCase     	=       []
			# Parity bit counter
			UpperCAmelCase     	=       0
			# Counter p data bit reading
			UpperCAmelCase     	=       0
			# list of parity received
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []

			for x in range(1    ,    len(UpperCamelCase__				) + 1				):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
									data_out_gab.append('''P'''				)
									UpperCAmelCase     	=       qtd_bp + 1
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]				)
						else:
									parity_received.append(data[cont_data]				)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       [bin(UpperCamelCase__				)[2:] for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				)]

			#  sorted information data for the size of the output data
			UpperCAmelCase     	=       []
			# Data position feedback + parity
			UpperCAmelCase     	=       []
			# Parity bit counter
			UpperCAmelCase     	=       0
			# Counter p data bit reading
			UpperCAmelCase     	=       0

			for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
									data_out_gab.append('''P'''				)
									UpperCAmelCase     	=       qtd_bp + 1
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]				)
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__				)

    # Calculates parity
			UpperCAmelCase     	=       0  # parity bit counter
			for bp in range(1    ,    size_par + 1				):
						# Bit counter one for a certain parity
						UpperCAmelCase     	=       0
						# Counter to control loop reading
						UpperCAmelCase     	=       0
						for x in data_ord:
									if x is not None:
												try:
															UpperCAmelCase     	=       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCAmelCase     	=       '''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2				)				)

						qtd_bp += 1

			# Mount the message
			UpperCAmelCase     	=       0  # Parity bit counter
			for x in range(0    ,    size_par + len(UpperCamelCase__				)				):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]				)				)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]				)

			UpperCAmelCase     	=       parity_received == parity
			return data_output, ack



