






import json
import os
from typing import Optional, Tuple

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


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)

__UpperCamelCase      :      Any     						=						{"""vocab_file""": """vocab.json"""}

__UpperCamelCase      :      Union[str, Any]     						=						{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

__UpperCamelCase      :      List[Any]     						=						{"""mgp-str""": 27}


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Union[str, Any]   =       VOCAB_FILES_NAMES
	__snake_case	:Dict   =       PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:Any   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







	def __init__(  self      :							str       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							int="[GO]"       ,				_lowerCAmelCase      :							Optional[int]="[GO]"       ,				_lowerCAmelCase      :							List[str]="[s]"       ,				_lowerCAmelCase      :							Tuple="[GO]"       ,				**_lowerCAmelCase      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		super().__init__(
		    unk_token=_lowerCAmelCase       ,				bos_token=_lowerCAmelCase       ,				eos_token=_lowerCAmelCase       ,				pad_token=_lowerCAmelCase       ,				**_lowerCAmelCase       ,				)

		with open(_lowerCAmelCase       ,				encoding="""utf-8"""			) as vocab_handle:
			__lowercase             =  json.load(_lowerCAmelCase			)
		__lowercase             =  {v: k for k, v in self.vocab.items()}







	@property
	def  _a   (  self      :							int			)		-> int:
		"""simple docstring"""







		return len(self.vocab			)







	def  _a   (  self      :							List[str]			)		-> Dict:
		"""simple docstring"""







		return dict(self.vocab       ,				**self.added_tokens_encoder			)







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							str			)		-> Any:
		"""simple docstring"""







		__lowercase             =  []
		for s in text:
			char_tokens.extend(_lowerCAmelCase			)
		return char_tokens







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		return self.vocab.get(_lowerCAmelCase       ,				self.vocab.get(self.unk_token			)			)







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		return self.decoder.get(_lowerCAmelCase			)







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[str] = None			)		-> Tuple[str]:
		"""simple docstring"""







		if not os.path.isdir(_lowerCAmelCase			):
			logger.error("""Vocabulary path ({}) should be a directory""".format(_lowerCAmelCase			)			)
			return
		__lowercase             =  os.path.join(
		    _lowerCAmelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

		return (vocab_file,)









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

