




'''simple docstring'''
from typing import List, Optional, Tuple

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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE			=						{
    "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"
    },
}

_SCREAMING_SNAKE_CASE			=						{"allegro/herbert-base-cased": 5_14}
_SCREAMING_SNAKE_CASE			=						{}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		VOCAB_FILES_NAMES
					_UpperCamelCase							=		PRETRAINED_VOCAB_FILES_MAP
					_UpperCamelCase							=		PRETRAINED_INIT_CONFIGURATION
					_UpperCamelCase							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_UpperCamelCase							=		HerbertTokenizer



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



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

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



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						List[int]      ,       _lowercase     :						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     __snake_case						(	self     :						str      ,       _lowercase     :						str      ,       _lowercase     :						Optional[str] = None)    ->							Tuple[str]:
								A_									= self._tokenizer.model.save(_lowercase      ,       name=_lowercase)
								return tuple(_lowercase)










'''simple docstring'''
import os
import sys

_SCREAMING_SNAKE_CASE			=						os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_SCREAMING_SNAKE_CASE			=						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		int:
			return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		List[str]:
			return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModel.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Dict:
			return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		str:
			return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )




