

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_       : List[str]         =			logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
A_       : Optional[Any]         =			{
    '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_       : List[str]         =			{
    'RUCAIBox/mvp': 1024,
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Union[str, Any]  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   Any  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   int  							=       ['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:   List[Any]  							=       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__    ,       ):
						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__    ,       )

						A__							:    Any        =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
						if pre_tok_state.get("""add_prefix_space"""    ,       A__			) != add_prefix_space:
							A__							:    Union[str, Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Union[str, Any]        =		add_prefix_space
							A__							:    Union[str, Any]        =		pre_tok_class(**A__			)

						A__							:    Any        =		add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						A__							:    Optional[Any]        =		"""post_processor"""
						A__							:    List[str]        =		getattr(self.backend_tokenizer    ,       A__    ,       A__			)
						if tokenizer_component_instance:
							A__							:    Tuple        =		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:
								A__							:    Any        =		tuple(state["""sep"""]			)
							if "cls" in state:
								A__							:    Optional[Any]        =		tuple(state["""cls"""]			)

							A__							:    Optional[Any]        =		False

							if state.get("""add_prefix_space"""    ,       A__			) != add_prefix_space:
								A__							:    List[str]        =		add_prefix_space
								A__							:    List[str]        =		True

							if state.get("""trim_offsets"""    ,       A__			) != trim_offsets:
								A__							:    int        =		trim_offsets
								A__							:    int        =		True

							if changes_to_apply:
								A__							:    List[str]        =		getattr(A__    ,       state.pop("""type"""			)			)
								A__							:    Dict        =		component_class(**A__			)
								setattr(self.backend_tokenizer    ,       A__    ,       A__			)






					@property
					def   __A			( self			):
						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   __A			( self    ,       A__			):
						A__							:    str        =		AddedToken(A__    ,       lstrip=A__    ,       rstrip=A__			) if isinstance(A__    ,       A__			) else value
						A__							:    Dict        =		value






					def   __A			( self    ,       *A__    ,       **A__			):
						A__							:    Tuple        =		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   __A			( self    ,       *A__    ,       **A__			):
						A__							:    int        =		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   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    List[str]        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[int]        =		[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   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Dict        =		[self.sep_token_id]
						A__							:    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 + sep + token_ids_a + sep			) * [0]






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_       : int         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   List[Any]  							=       '''mgp-str'''






					def __init__( self    ,       A__=[32, 128]    ,       A__=4    ,       A__=3    ,       A__=27    ,       A__=38    ,       A__=5_0257    ,       A__=3_0522    ,       A__=768    ,       A__=12    ,       A__=12    ,       A__=4.0    ,       A__=True    ,       A__=False    ,       A__=1e-5    ,       A__=0.0    ,       A__=0.0    ,       A__=0.0    ,       A__=False    ,       A__=0.0_2    ,       **A__    ,       ):
						super().__init__(**A__			)

						A__							:    Dict        =		image_size
						A__							:    int        =		patch_size
						A__							:    Dict        =		num_channels
						A__							:    List[Any]        =		max_token_length
						A__							:    str        =		num_character_labels
						A__							:    Tuple        =		num_bpe_labels
						A__							:    Optional[Any]        =		num_wordpiece_labels
						A__							:    Optional[int]        =		hidden_size
						A__							:    Tuple        =		num_hidden_layers
						A__							:    Any        =		num_attention_heads
						A__							:    List[Any]        =		mlp_ratio
						A__							:    Tuple        =		distilled
						A__							:    Union[str, Any]        =		layer_norm_eps
						A__							:    Tuple        =		drop_rate
						A__							:    List[str]        =		qkv_bias
						A__							:    Optional[Any]        =		attn_drop_rate
						A__							:    Union[str, Any]        =		drop_path_rate
						A__							:    Optional[Any]        =		output_aa_attentions
						A__							:    Optional[int]        =		initializer_range



