






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_mvp import MvpTokenizer


a_		:    Optional[Any]   							=     logging.get_logger(__name__)


a_		:    List[Any]   							=     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
a_		:    str   							=     {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

a_		:    Optional[int]   							=     {
    'RUCAIBox/mvp': 10_24,
}

class 		_snake_case	(	A__       ):
			_lowercase		:     str        =				VOCAB_FILES_NAMES
			_lowercase		:     Any        =				PRETRAINED_VOCAB_FILES_MAP
			_lowercase		:     List[str]        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowercase		:     Tuple        =				['''input_ids''', '''attention_mask''']
			_lowercase		:     Optional[int]        =				MvpTokenizer






			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		,       )   ->							int:
						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		,       )

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

						SCREAMING_SNAKE_CASE							=			add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						SCREAMING_SNAKE_CASE							=			'post_processor'
						SCREAMING_SNAKE_CASE							=			getattr(self.backend_tokenizer		,       a		,       a)
						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'		,       a) != add_prefix_space:
												SCREAMING_SNAKE_CASE							=			add_prefix_space
												SCREAMING_SNAKE_CASE							=			True

									if state.get('trim_offsets'		,       a) != trim_offsets:
												SCREAMING_SNAKE_CASE							=			trim_offsets
												SCREAMING_SNAKE_CASE							=			True

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






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						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  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							str:
						SCREAMING_SNAKE_CASE							=			AddedToken(a		,       lstrip=a		,       rstrip=a) if isinstance(a		,       a) else value
						SCREAMING_SNAKE_CASE							=			value






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							BatchEncoding:
						SCREAMING_SNAKE_CASE							=			kwargs.get('is_split_into_words'		,       a)

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    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  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							BatchEncoding:
						SCREAMING_SNAKE_CASE							=			kwargs.get('is_split_into_words'		,       a)

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    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  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None)   ->							Tuple[str]:
						SCREAMING_SNAKE_CASE							=			self._tokenizer.model.save(a		,       name=a)
						return tuple(a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a=None)   ->							Dict:
						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  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None)   ->							List[int]:
						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]








'''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


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							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."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							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."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d