



"""simple docstring"""







# Copyright 2022 The HuggingFace Team and The OpenBMB 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


lowerCAmelCase_   						=   {
    'configuration_cpmant': ['CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CpmAntConfig'],
    'tokenization_cpmant': ['CpmAntTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'CpmAntForCausalLM',
							    'CpmAntModel',
							    'CpmAntPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
							from .tokenization_cpmant import CpmAntTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cpmant import (
														    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CpmAntForCausalLM,
														    CpmAntModel,
														    CpmAntPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""







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

import sentencepiece as spm

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


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   '▁'

lowerCAmelCase_   						=   {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase_   						=   {
    'facebook/xglm-564M': 2_048,
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Any   	=				PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					str   	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					int   	=				["input_ids", "attention_mask"]







		def __init__(	self						:  int			,_snake_case						:  Dict			,_snake_case						:  Dict="<s>"			,_snake_case						:  Dict="</s>"			,_snake_case						:  str="</s>"			,_snake_case						:  Optional[Any]="<s>"			,_snake_case						:  Optional[Any]="<unk>"			,_snake_case						:  Optional[int]="<pad>"			,_snake_case						:  Optional[Dict[str, Any]] = None			,**_snake_case						:  str			,)					->		None:






				"""simple docstring"""



				lowercase__   :    Any				=			{} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				lowercase__   :    Any				=			7
				lowercase__   :    Optional[int]				=			[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

				lowercase__   :    Dict				=			kwargs.get('''additional_special_tokens'''			,[]						)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=_snake_case			,eos_token=_snake_case			,unk_token=_snake_case			,sep_token=_snake_case			,cls_token=_snake_case			,pad_token=_snake_case			,sp_model_kwargs=self.sp_model_kwargs			,**_snake_case			,)

				lowercase__   :    List[str]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(str(_snake_case						)						)
				lowercase__   :    str				=			vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowercase__   :    Optional[int]				=			1

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowercase__   :    Optional[int]				=			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

				lowercase__   :    List[str]				=			len(self.sp_model						)
				lowercase__   :    Tuple				=			{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words						)}
				self.fairseq_tokens_to_ids.update(_snake_case						)

				lowercase__   :    Union[str, Any]				=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}







		def __getstate__(	self						:  int						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.__dict__.copy()
				lowercase__   :    Optional[int]				=			None
				lowercase__   :    Any				=			self.sp_model.serialized_model_proto()
				return state







		def __setstate__(	self						:  Dict			,_snake_case						:  List[str]						)					->		Any:






				"""simple docstring"""



				lowercase__   :    int				=			d

				# for backward compatibility
				if not hasattr(self			,'''sp_model_kwargs'''						):
						lowercase__   :    Dict				=			{}

				lowercase__   :    Optional[int]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
				lowercase__   :    Optional[Any]				=			[self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None			,_snake_case						:  bool = False						)					->		List[int]:






				"""simple docstring"""



				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=_snake_case			,token_ids_a=_snake_case			,already_has_special_tokens=_snake_case						)

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







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			[self.sep_token_id]

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







		@property
		def 					UpperCAmelCase    (	self						:  str						)					->		Tuple:






				"""simple docstring"""



				return len(self.sp_model						) + self.fairseq_offset + self.num_madeup_words







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			{self.convert_ids_to_tokens(_snake_case						): i for i in range(self.vocab_size						)}
				vocab.update(self.added_tokens_encoder						)
				return vocab







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  str						)					->		List[str]:






				"""simple docstring"""



				return self.sp_model.encode(_snake_case			,out_type=_snake_case						)







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Optional[int]						)					->		List[Any]:






				"""simple docstring"""



				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				lowercase__   :    Tuple				=			self.sp_model.PieceToId(_snake_case						)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[str]						)					->		Any:






				"""simple docstring"""



				if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset						)







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Tuple						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			''''''.join(_snake_case						).replace(_snake_case			,''' '''						).strip()
				return out_string







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  str			,_snake_case						:  Optional[str] = None						)					->		Tuple[str]:






				"""simple docstring"""



				if not os.path.isdir(_snake_case						):
						logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
						return
				lowercase__   :    Any				=			os.path.join(
				    _snake_case			,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(_snake_case						) and os.path.isfile(self.vocab_file						):
						copyfile(self.vocab_file			,_snake_case						)
				elif not os.path.isfile(self.vocab_file						):
						with open(_snake_case			,'''wb'''						) as fi:
								lowercase__   :    Dict				=			self.sp_model.serialized_model_proto()
								fi.write(_snake_case						)

				return (out_vocab_file,)
