
'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_big_bird import BigBirdTokenizer
else:
							_lowercase			:  str   							=	None

_lowercase			:  int   							=	logging.get_logger(__name__)
_lowercase			:  int   							=	{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase			:  Dict   							=	{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

_lowercase			:  Any   							=	{
    "google/bigbird-roberta-base": 4_0_9_6,
    "google/bigbird-roberta-large": 4_0_9_6,
    "google/bigbird-base-trivia-itc": 4_0_9_6,
}


_lowercase			:  List[str]   							=	"▁"






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					VOCAB_FILES_NAMES
					lowerCAmelCase_					       =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_					       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_					       =					BigBirdTokenizer
					lowerCAmelCase_					       =					['''input_ids''', '''attention_mask''']
					lowerCAmelCase_					       =					[]






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE="<unk>"    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="<pad>"    ,							__SCREAMING_SNAKE_CASE="[SEP]"    ,							__SCREAMING_SNAKE_CASE="[MASK]"    ,							__SCREAMING_SNAKE_CASE="[CLS]"    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else bos_token
											lowercase_     :					Any													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else eos_token
											lowercase_     :					List[Any]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else unk_token
											lowercase_     :					str													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else pad_token
											lowercase_     :					Optional[int]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else cls_token
											lowercase_     :					Union[str, Any]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else sep_token

											# Mask token behave like a normal word, i.e. include the space before it
											lowercase_     :					Dict													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else mask_token

											super().__init__(
											    __SCREAMING_SNAKE_CASE    ,							tokenizer_file=__SCREAMING_SNAKE_CASE    ,							bos_token=__SCREAMING_SNAKE_CASE    ,							eos_token=__SCREAMING_SNAKE_CASE    ,							unk_token=__SCREAMING_SNAKE_CASE    ,							sep_token=__SCREAMING_SNAKE_CASE    ,							pad_token=__SCREAMING_SNAKE_CASE    ,							cls_token=__SCREAMING_SNAKE_CASE    ,							mask_token=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)

											lowercase_     :					str													=	vocab_file
											lowercase_     :					int													=	False if not self.vocab_file else True






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	[self.sep_token_id]
											lowercase_     :					Optional[int]													=	[self.cls_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    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											if already_has_special_tokens:
																	if token_ids_a is not None:
																							raise ValueError(
																							    '''You should not supply a second sequence if the provided sequence of '''
																							    '''ids is already formatted with special tokens for the model.'''		)
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	[self.sep_token_id]
											lowercase_     :					Optional[Any]													=	[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    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																	    '''tokenizer.'''		)

											if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
																	return
											lowercase_     :					Tuple													=	os.path.join(
											    __SCREAMING_SNAKE_CASE    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		):
																	copyfile(self.vocab_file    ,							__SCREAMING_SNAKE_CASE		)

											return (out_vocab_file,)





'''simple docstring'''







import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_lowercase			:  Tuple   							=	logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__					)
class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					field(default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}					)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': (
					            '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
					            '''to the `max_length` value of the model configuration.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': (
					            '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
					            '''to the `num_beams` value of the model configuration.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
					    }   ,						)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	super().to_dict()
											for k, v in d.items():
																	if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																							lowercase_     :					str													=	v.to_dict()
											return d



