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


_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Dict     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				Tuple     			=						{
    '''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'''
        ),
    },
}

_lowerCamelCase   :				List[str]     			=						{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}


class   lowercase      (       A_       ):
							lowercase__  :    Optional[Any]									=							VOCAB_FILES_NAMES
							lowercase__  :    List[Any]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    List[str]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    List[Any]									=							["""input_ids""", """attention_mask"""]
							lowercase__  :    Any									=							RobertaTokenizer




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



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE           =    add_prefix_space

												SCREAMING_SNAKE_CASE           =    "post_processor"
												SCREAMING_SNAKE_CASE           =    getattr(self.backend_tokenizer		,							_lowerCamelCase		,							_lowerCamelCase )
												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"		,							_lowerCamelCase ) != add_prefix_space:
																						SCREAMING_SNAKE_CASE           =    add_prefix_space
																						SCREAMING_SNAKE_CASE           =    True

																	if state.get("trim_offsets"		,							_lowerCamelCase ) != trim_offsets:
																						SCREAMING_SNAKE_CASE           =    trim_offsets
																						SCREAMING_SNAKE_CASE           =    True

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




							@property
							def 		__snake_case(  self				:   Any )   ->			Optional[int]:



												'''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 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Tuple )   ->			List[str]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Optional[Any]		,							*_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   int )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_lowerCamelCase )
												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(*_lowerCamelCase		,							**_lowerCamelCase )




							def 		__snake_case(  self				:   int		,							*_UpperCamelCase				:   List[Any]		,							**_UpperCamelCase				:   List[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_lowerCamelCase )

												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(*_lowerCamelCase		,							**_lowerCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Optional[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Any=None )   ->			Any:



												'''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 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[str]:



												'''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]





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowerCamelCase   :				Union[str, Any]     			=						TypeVar('''T''')


class   lowercase      (       Generic[T]       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {}  # dictionary of lists
												SCREAMING_SNAKE_CASE           =    directed




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   T		,							_UpperCamelCase				:   T )   ->			GraphAdjacencyList[T]:



												'''simple docstring'''

												if not self.directed:  # For undirected graphs
																	# if both source vertex and destination vertex are both present in the
																	# adjacency list, add destination vertex to source vertex list of adjacent
																	# vertices and add source vertex to destination vertex list of adjacent
																	# vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination vertex
																	# to source vertex list of adjacent vertices, then create a new vertex with
																	# destination vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
																	# if only destination vertex is present in adjacency list, add source vertex
																	# to destination vertex list of adjacent vertices, then create a new vertex
																	# with source vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif destination_vertex in self.adj_list:
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and assign a list
																	# containing the destination vertex as it's first adjacent vertex also
																	# create a new vertex with destination vertex as key and assign a list
																	# containing the source vertex as it's first adjacent vertex.
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
												else:  # For directed graphs
																	# if both source vertex and destination vertex are present in adjacency
																	# list, add destination vertex to source vertex list of adjacent vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination
																	# vertex to source vertex list of adjacent vertices and create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    []
																	# if only destination vertex is present in adjacency list, create a new
																	# vertex with source vertex as key and assign a list containing destination
																	# vertex as first adjacent vertex
																	elif destination_vertex in self.adj_list:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and a list containing
																	# destination vertex as it's first adjacent vertex. Then create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    []

												return self





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



												'''simple docstring'''

												return pformat(self.adj_list )




