



'''simple docstring'''
import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case_  	=  logging.get_logger(__name__)

snake_case_  	=  {"vocab_file": "vocab.json"}

snake_case_  	=  {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

snake_case_  	=  {"mgp-str": 27}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      VOCAB_FILES_NAMES
	lowercase__      =      PRETRAINED_VOCAB_FILES_MAP
	lowercase__      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    int			,lowerCamelCase__ :    List[str]="[GO]"			,lowerCamelCase__ :    Union[str, Any]="[GO]"			,lowerCamelCase__ :    Dict="[s]"			,lowerCamelCase__ :    List[str]="[GO]"			,**lowerCamelCase__ :    Any			):







							'''simple docstring'''
							super().__init__(
							    unk_token=_A			,bos_token=_A			,eos_token=_A			,pad_token=_A			,**_A			,)

							with open(_A			,encoding='utf-8'			) as vocab_handle:
													_UpperCamelCase :		Optional[int]			    =       json.load(_A			)
							_UpperCamelCase :		Union[str, Any]			    =       {v: k for k, v in self.vocab.items()}





	@property
	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							return len(self.vocab			)





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return dict(self.vocab			,**self.added_tokens_encoder			)





	def      UpperCamelCase_  (			self :    Dict			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       []
							for s in text:
													char_tokens.extend(_A			)
							return char_tokens





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							return self.vocab.get(_A			,self.vocab.get(self.unk_token			)			)





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    Tuple			):







							'''simple docstring'''
							return self.decoder.get(_A			)





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[str] = None			):







							'''simple docstring'''
							if not os.path.isdir(_A			):
													logger.error('Vocabulary path ({}) should be a directory'.format(_A			)			)
													return
							_UpperCamelCase :		Optional[Any]			    =       os.path.join(
							    _A			,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

							with open(_A			,'w'			,encoding='utf-8'			) as f:
													f.write(json.dumps(self.vocab			,indent=2			,sort_keys=_A			,ensure_ascii=_A			) + '\n'			)

							return (vocab_file,)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_			:     Any  	=  {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Dict  	=  ['MvpTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MvpForCausalLM',
					    'MvpForConditionalGeneration',
					    'MvpForQuestionAnswering',
					    'MvpForSequenceClassification',
					    'MvpModel',
					    'MvpPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					snake_case_			:     Optional[Any]  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


