



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_roberta import RobertaTokenizer


UpperCAmelCase							 =					logging.get_logger(__name__)

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

UpperCAmelCase							 =					{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase							 =					{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}

class  __snake_case      (   _UpperCAmelCase):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Dict         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      int         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      Tuple         =					['''input_ids''', '''attention_mask''']
							UpperCamelCase__				:      Optional[Any]         =					RobertaTokenizer


							def __init__(							self  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_="replace"  ,  a_="<s>"  ,  a_="</s>"  ,  a_="</s>"  ,  a_="<s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<mask>"  ,  a_=False  ,  a_=True  ,  **a_  ,  ):
											super().__init__(
											    A_  ,  A_  ,  tokenizer_file=A_  ,  errors=A_  ,  bos_token=A_  ,  eos_token=A_  ,  sep_token=A_  ,  cls_token=A_  ,  unk_token=A_  ,  pad_token=A_  ,  mask_token=A_  ,  add_prefix_space=A_  ,  trim_offsets=A_  ,  **A_  ,  )

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

											a__				      =		add_prefix_space

											a__				      =		"""post_processor"""
											a__				      =		getattr(self.backend_tokenizer  ,  A_  ,  A_		)
											if tokenizer_component_instance:
															a__				      =		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:
																			a__				      =		tuple(state["""sep"""]		)
															if "cls" in state:
																			a__				      =		tuple(state["""cls"""]		)

															a__				      =		False

															if state.get("""add_prefix_space"""  ,  A_		) != add_prefix_space:
																			a__				      =		add_prefix_space
																			a__				      =		True

															if state.get("""trim_offsets"""  ,  A_		) != trim_offsets:
																			a__				      =		trim_offsets
																			a__				      =		True

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


							@property
							def _a      (							self		):
											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  ,  a_		):
											a__				      =		AddedToken(A_  ,  lstrip=A_  ,  rstrip=A_		) if isinstance(A_  ,  A_		) else value
											a__				      =		value


							def _a      (							self  ,  *a_  ,  **a_		):
											a__				      =		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  ,  *a_  ,  **a_		):
											a__				      =		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  ,  a_  ,  a_ = None		):
											a__				      =		self._tokenizer.model.save(A_  ,  name=A_		)
											return tuple(A_		)


							def _a      (							self  ,  a_  ,  a_=None		):
											a__				      =		[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 _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[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 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head:
								return True
				# split the list to two parts
				a__      ,			a__				      =		head.next, head
				while fast and fast.next:
								a__				      =		fast.next.next
								a__				      =		slow.next
				a__				      =		slow.next
				a__				      =		None  # Don't forget here! But forget still works!
				# reverse the second part
				a__				      =		None
				while second:
								a__				      =		second.next
								a__				      =		node
								a__				      =		second
								a__				      =		nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								a__				      =		node.next
								a__				      =		head.next
				return True

def 			A_      (	__a     :				int		):

				"""simple docstring"""
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				a__				      =		a__				      =		a__				      =		head
				while fast and fast.next:
								a__      ,			a__				      =		fast.next.next, slow.next

				# 2. Push the second half into the stack
				a__				      =		[slow.val]
				while slow.next:
								a__				      =		slow.next
								stack.append(slow.val		)

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								a__				      =		cur.next

				return True




def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head or not head.next:
								return True
				a__				      =		{}
				a__				      =		0
				while head:
								if head.val in d:
												d[head.val].append(__a		)
								else:
												a__				      =		[pos]
								a__				      =		head.next
								pos += 1
				a__				      =		pos - 1
				a__				      =		0
				for v in d.values():
								if len(__a		) % 2 != 0:
												middle += 1
								else:
												a__				      =		0
												for i in range(0 ,		len(__a		)		):
																if v[i] + v[len(__a		) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True

