






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A      =				logging.get_logger(__name__)

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

__A      =				{
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

__A      =				{'allegro/herbert-base-cased': 514}
__A      =				{}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					VOCAB_FILES_NAMES
							A_						  =					PRETRAINED_VOCAB_FILES_MAP
							A_						  =					PRETRAINED_INIT_CONFIGURATION
							A_						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_						  =					HerbertTokenizer

							def __init__(  self:   List[str]		,     __A:   Dict=None		,     __A:   List[Any]=None		,     __A:   Dict=None		,     __A:   Tuple="<s>"		,     __A:   Optional[int]="<unk>"		,     __A:   Dict="<pad>"		,     __A:   Optional[Any]="<mask>"		,     __A:   Optional[Any]="</s>"		,     **__A:   Optional[int]		,     )    ->				str:
												super().__init__(
												    __A		,     __A		,     tokenizer_file=__A		,     cls_token=__A		,     unk_token=__A		,     pad_token=__A		,     mask_token=__A		,     sep_token=__A		,     **__A		,     )

							def       __A (  self:   List[Any]		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												_A								= [self.cls_token_id]
												_A								= [self.sep_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep

												return cls + token_ids_a + sep + token_ids_a + sep

							def       __A (  self:   Optional[Any]		,     __A:   List[int]		,     __A:   Optional[List[int]] = None		,     __A:   bool = False   )    ->				List[int]:
												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__A		,     token_ids_a=__A		,     already_has_special_tokens=__A   )

												if token_ids_a is None:
																	return [1] + ([0] * len(__A   )) + [1]
												return [1] + ([0] * len(__A   )) + [1] + ([0] * len(__A   )) + [1]

							def       __A (  self:   Union[str, Any]		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												_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   ) * [0] + len(token_ids_a + sep   ) * [1]



							def       __A (  self:   str		,     __A:   str		,     __A:   Optional[str] = None   )    ->				Tuple[str]:
												_A								= self._tokenizer.model.save(__A		,     name=__A   )
												return tuple(__A   )











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A      =				logging.get_logger(__name__)

__A      =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"roberta"

							def __init__(  self:   Dict		,     __A:   int=5_02_65		,     __A:   Union[str, Any]=7_68		,     __A:   Union[str, Any]=12		,     __A:   str=12		,     __A:   int=30_72		,     __A:   str="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   int=0.1		,     __A:   Optional[int]=5_12		,     __A:   Union[str, Any]=2		,     __A:   str=0.02		,     __A:   str=1e-12		,     __A:   Any=1		,     __A:   str=0		,     __A:   Any=2		,     __A:   Optional[int]="absolute"		,     __A:   Optional[Any]=True		,     __A:   Union[str, Any]=None		,     **__A:   List[str]		,     )    ->				Dict:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= use_cache
												_A								= classifier_dropout





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Dict   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )



