



"""simple docstring"""







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							"""▁"""

UpperCamelCase     =							{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

UpperCamelCase     =							{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

UpperCamelCase     =							{"""vinai/bartpho-syllable""": 1024}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[str]							  =     VOCAB_FILES_NAMES
			A__	:					Tuple							  =     PRETRAINED_VOCAB_FILES_MAP
			A__	:					Union[str, Any]							  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__	:					str							  =     ["input_ids", "attention_mask"]


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__="<s>"						,	SCREAMING_SNAKE_CASE__="</s>"						,	SCREAMING_SNAKE_CASE__="</s>"						,	SCREAMING_SNAKE_CASE__="<s>"						,	SCREAMING_SNAKE_CASE__="<unk>"						,	SCREAMING_SNAKE_CASE__="<pad>"						,	SCREAMING_SNAKE_CASE__="<mask>"						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	None:
				# Mask token behave like a normal word, i.e. include the space before it
				A__											=					AddedToken(SCREAMING_SNAKE_CASE__						,	lstrip=SCREAMING_SNAKE_CASE__						,	rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) else mask_token

				A__											=					{} if sp_model_kwargs is None else sp_model_kwargs

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

				A__											=					vocab_file
				A__											=					monolingual_vocab_file
				A__											=					spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(str(SCREAMING_SNAKE_CASE__		)		)

				# Load the reduced vocab

				# Keep order of special tokens for backward compatibility
				A__											=					{}
				A__											=					0
				for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
					if str(SCREAMING_SNAKE_CASE__		) not in self.fairseq_tokens_to_ids:
						A__											=					cnt
						cnt += 1
				with open(SCREAMING_SNAKE_CASE__						,	"r"						,	encoding="utf-8"		) as f:
					for line in f.readlines():
						A__											=					line.strip().split()[0]
						A__											=					len(self.fairseq_tokens_to_ids		)
				if str(SCREAMING_SNAKE_CASE__		) not in self.fairseq_tokens_to_ids:
					A__											=					len(self.fairseq_tokens_to_ids		)

				A__											=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}


			def __getstate__(      self		)      ->	Dict:
				A__											=					self.__dict__.copy()
				A__											=					None
				A__											=					self.sp_model.serialized_model_proto()
				return state


			def __setstate__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				A__											=					d

				# for backward compatibility
				if not hasattr(self						,	"sp_model_kwargs"		):
					A__											=					{}

				A__											=					spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)


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

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				A__											=					[self.cls_token_id]
				A__											=					[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = False		)      ->	List[int]:

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

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None		)      ->	List[int]:
				A__											=					[self.sep_token_id]
				A__											=					[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]


			@property
			def   snake_case__	(      self		)      ->	str:
				return len(self.fairseq_ids_to_tokens		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				return self.sp_model.encode(SCREAMING_SNAKE_CASE__						,	out_type=SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
				else:
					return self.unk_token_id


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				return self.fairseq_ids_to_tokens[index]


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	str:
				A__											=					"".join(SCREAMING_SNAKE_CASE__		).replace(SCREAMING_SNAKE_CASE__						,	" "		).strip()
				return out_string







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None		)      ->	Tuple[str]:
				if not os.path.isdir(SCREAMING_SNAKE_CASE__		):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
					return
				A__											=					os.path.join(
				    SCREAMING_SNAKE_CASE__						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				A__											=					os.path.join(
				    SCREAMING_SNAKE_CASE__						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]						,	)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(SCREAMING_SNAKE_CASE__		) and os.path.isfile(self.vocab_file		):
					copyfile(self.vocab_file						,	SCREAMING_SNAKE_CASE__		)
				elif not os.path.isfile(self.vocab_file		):
					with open(SCREAMING_SNAKE_CASE__						,	"wb"		) as fi:
						A__											=					self.sp_model.serialized_model_proto()
						fi.write(SCREAMING_SNAKE_CASE__		)

				if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
				    SCREAMING_SNAKE_CASE__		) and os.path.isfile(self.monolingual_vocab_file		):
					copyfile(self.monolingual_vocab_file						,	SCREAMING_SNAKE_CASE__		)
				elif not os.path.isfile(self.monolingual_vocab_file		):
					with open(SCREAMING_SNAKE_CASE__						,	"w"						,	encoding="utf-8"		) as fp:
						for token in self.fairseq_tokens_to_ids:
							if token not in self.all_special_tokens:
								fp.write(f"""{str(SCREAMING_SNAKE_CASE__		)} \n"""		)

				return out_vocab_file, out_monolingual_vocab_file






'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase     :							Tuple       						=     '\nHuman: <<task>>\n\nAssistant: '


lowercase     :							Union[str, Any]       						=     'huggingface-tools/default-prompts'
lowercase     :							int       						=     {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__="run"		):




    '''simple docstring'''



    if prompt_or_repo_id is None:
        A			:  List[Any]   =		DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search('''\\s'''						,							snake_case__		) is not None:
        return prompt_or_repo_id

    A			:  int   =		cached_file(
        snake_case__						,							PROMPT_FILES[mode]						,							repo_type='''dataset'''						,							user_agent={'''agent''': agent_name}		)
    with open(snake_case__						,							'''r'''						,							encoding='''utf-8'''		) as f:
        return f.read()




