



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class    _lowercase (a_   ):
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = None  ,			**snake_case__  ,			):

									'''simple docstring'''


									super().__init__(
									    snake_case__  ,			split=snake_case__  ,			features=snake_case__  ,			cache_dir=snake_case__  ,			keep_in_memory=snake_case__  ,			streaming=snake_case__  ,			num_proc=snake_case__  ,			**snake_case__  ,			)
									UpperCamelCase_			= path_or_paths if isinstance(snake_case__  ,			snake_case__     ) else {self.split: path_or_paths}
									UpperCamelCase_			= Csv(
									    cache_dir=snake_case__  ,			data_files=snake_case__  ,			features=snake_case__  ,			**snake_case__  ,			)

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									if self.streaming:
														UpperCamelCase_			= self.builder.as_streaming_dataset(split=self.split     )
									# Build regular (map-style) dataset
									else:
														UpperCamelCase_			= None
														UpperCamelCase_			= None
														UpperCamelCase_			= None
														UpperCamelCase_			= None

														self.builder.download_and_prepare(
														    download_config=snake_case__  ,			download_mode=snake_case__  ,			verification_mode=snake_case__  ,			base_path=snake_case__  ,			num_proc=self.num_proc  ,			)
														UpperCamelCase_			= self.builder.as_dataset(
														    split=self.split  ,			verification_mode=snake_case__  ,			in_memory=self.keep_in_memory     )
									return dataset




class    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__  ,			snake_case__ = None  ,			snake_case__ = None  ,			**snake_case__  ,			):

									'''simple docstring'''


									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""     )

									UpperCamelCase_			= dataset
									UpperCamelCase_			= path_or_buf
									UpperCamelCase_			= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									UpperCamelCase_			= num_proc
									UpperCamelCase_			= "utf-8"
									UpperCamelCase_			= to_csv_kwargs

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.to_csv_kwargs.pop("path_or_buf"  ,			snake_case__     )
									UpperCamelCase_			= self.to_csv_kwargs.pop("header"  ,			snake_case__     )
									UpperCamelCase_			= self.to_csv_kwargs.pop("index"  ,			snake_case__     )

									if isinstance(self.path_or_buf  ,			(str, bytes, os.PathLike)     ):
														with open(self.path_or_buf  ,			"wb+"     ) as buffer:
																			UpperCamelCase_			= self._write(file_obj=snake_case__  ,			header=snake_case__  ,			index=snake_case__  ,			**self.to_csv_kwargs     )
									else:
														UpperCamelCase_			= self._write(file_obj=self.path_or_buf  ,			header=snake_case__  ,			index=snake_case__  ,			**self.to_csv_kwargs     )
									return written

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= args

									UpperCamelCase_			= query_table(
									    table=self.dataset.data  ,			key=slice(snake_case__  ,			offset + self.batch_size     )  ,			indices=self.dataset._indices  ,			)
									UpperCamelCase_			= batch.to_pandas().to_csv(
									    path_or_buf=snake_case__  ,			header=header if (offset == 0) else False  ,			index=snake_case__  ,			**snake_case__     )
									return csv_str.encode(self.encoding     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= 0

									if self.num_proc is None or self.num_proc == 1:
														for offset in logging.tqdm(
														    range(0  ,			len(self.dataset     )  ,			self.batch_size     )  ,			unit="ba"  ,			disable=not logging.is_progress_bar_enabled()  ,			desc="Creating CSV from Arrow format"  ,			):
																			UpperCamelCase_			= self._batch_csv((offset, header, index, to_csv_kwargs)     )
																			written += file_obj.write(snake_case__     )

									else:
														UpperCamelCase_	,     UpperCamelCase_			= len(self.dataset     ), self.batch_size
														with multiprocessing.Pool(self.num_proc     ) as pool:
																			for csv_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_csv  ,			[(offset, header, index, to_csv_kwargs) for offset in range(0  ,			snake_case__  ,			snake_case__     )]  ,			)  ,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,			unit="ba"  ,			disable=not logging.is_progress_bar_enabled()  ,			desc="Creating CSV from Arrow format"  ,			):
																								written += file_obj.write(snake_case__     )

									return written





"""simple docstring"""







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

import sentencepiece as spm

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


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

UpperCAmelCase:  str			     =				logging.get_logger(__name__)

UpperCAmelCase:  Tuple			     =				{"""vocab_file""": """tokenizer.model"""}

UpperCAmelCase:  int			     =				{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
UpperCAmelCase:  Union[str, Any]			     =				{
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCAmelCase:  List[Any]			     =				"""▁"""

UpperCAmelCase     , UpperCAmelCase:  int			     =				"""[INST]""", """[/INST]"""
UpperCAmelCase     , UpperCAmelCase:  Union[str, Any]			     =				"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCAmelCase:  Optional[int]			     =				"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_							:       int						       =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_							:       Dict						       =  ["input_ids", "attention_mask"]






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_="<unk>"				,UpperCAmelCase_="<s>"				,UpperCAmelCase_="</s>"				,UpperCAmelCase_=None				,UpperCAmelCase_ = None				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=True				,**UpperCAmelCase_				,):
			_lowercase				: List[str]     =   {} if sp_model_kwargs is None else sp_model_kwargs
			_lowercase				: List[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else bos_token
			_lowercase				: Union[str, Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else eos_token
			_lowercase				: Optional[int]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else unk_token
			_lowercase				: Optional[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else pad_token
			super().__init__(
			    bos_token=UpperCAmelCase_				,eos_token=UpperCAmelCase_				,unk_token=UpperCAmelCase_				,pad_token=UpperCAmelCase_				,add_bos_token=UpperCAmelCase_				,add_eos_token=UpperCAmelCase_				,sp_model_kwargs=self.sp_model_kwargs				,clean_up_tokenization_spaces=UpperCAmelCase_				,legacy=UpperCAmelCase_				,**UpperCAmelCase_				,)
			if legacy:
					logger.warning_once(
					    f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
					    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""   )
			_lowercase				: Tuple     =   legacy
			_lowercase				: Optional[int]     =   vocab_file
			_lowercase				: Any     =   add_bos_token
			_lowercase				: Union[str, Any]     =   add_eos_token
			_lowercase				: Union[str, Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(UpperCAmelCase_   )






	def __getstate__(     self   ):
			_lowercase				: Union[str, Any]     =   self.__dict__.copy()
			_lowercase				: Optional[int]     =   None
			_lowercase				: Optional[Any]     =   self.sp_model.serialized_model_proto()
			return state






	def __setstate__(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   d
			_lowercase				: List[Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.sp_model.get_piece_size()






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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,**UpperCAmelCase_   ):
			# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
			# the beginning of the text
			if not self.legacy:
					_lowercase				: str     =   SPIECE_UNDERLINE + text.replace(UpperCAmelCase_				,""" """   )
			return super().tokenize(UpperCAmelCase_				,**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if not self.legacy:
					_lowercase				: Any     =   text.startswith(UpperCAmelCase_   )
					if is_first:
							_lowercase				: str     =   text[1:]

			_lowercase				: Dict     =   self.sp_model.encode(UpperCAmelCase_				,out_type=UpperCAmelCase_   )

			if not self.legacy and not is_first and not text.startswith(""" """   ) and tokens[0].startswith(UpperCAmelCase_   ):
					_lowercase				: List[str]     =   ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
			return tokens






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return self.sp_model.piece_to_id(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   self.sp_model.IdToPiece(UpperCAmelCase_   )
			return token






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   []
			_lowercase				: Dict     =   """"""
			_lowercase				: List[Any]     =   False
			for i, token in enumerate(UpperCAmelCase_   ):
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special and i != 0:
									out_string += " "
							out_string += self.sp_model.decode(UpperCAmelCase_   ) + token
							_lowercase				: Optional[Any]     =   True
							_lowercase				: str     =   []
					else:
							current_sub_tokens.append(UpperCAmelCase_   )
							_lowercase				: Optional[Any]     =   False
			out_string += self.sp_model.decode(UpperCAmelCase_   )
			return out_string






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

			if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase_   ) and os.path.isfile(self.vocab_file   ):
					copyfile(self.vocab_file				,UpperCAmelCase_   )
			elif not os.path.isfile(self.vocab_file   ):
					with open(UpperCAmelCase_				,"""wb"""   ) as fi:
							_lowercase				: List[Any]     =   self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_   )

			return (out_vocab_file,)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			_lowercase				: Any     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: List[str]     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Union[str, Any]     =   bos_token_id + token_ids_a + eos_token_id

			if token_ids_a is not None:
					_lowercase				: int     =   output + bos_token_id + token_ids_a + eos_token_id

			return output






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None				,UpperCAmelCase_ = False   ):
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=UpperCAmelCase_				,token_ids_a=UpperCAmelCase_				,already_has_special_tokens=UpperCAmelCase_   )

			_lowercase				: Tuple     =   [1] if self.add_bos_token else []
			_lowercase				: Dict     =   [1] if self.add_eos_token else []

			if token_ids_a is None:
					return bos_token_id + ([0] * len(UpperCAmelCase_   )) + eos_token_id
			return (
			    bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			    + bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Union[str, Any]     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: str     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Any     =   [0] * len(bos_token_id + token_ids_a + eos_token_id   )

			if token_ids_a is not None:
					output += [1] * len(bos_token_id + token_ids_a + eos_token_id   )

			return output






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   list(conversation.iter_texts()   )
			if not all([is_user for is_user, msg in dialogue[::2]]   ) or not all(
			    [not is_user for is_user, msg in dialogue[1::2]]   ):
					raise ValueError(
					    """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""   )

			_lowercase				: List[int]     =   []
			if len(conversation.past_user_inputs   ) > 0:
					if not conversation.past_user_inputs[0].startswith(UpperCAmelCase_   ) or E_SYS not in conversation.past_user_inputs[0]:
							_lowercase				: Optional[int]     =   (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(UpperCAmelCase_   ) or E_SYS not in dialogue[0][1]:
					_lowercase				: List[str]     =   (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

			dialog_tokens += sum(
			    [
			        [self.bos_token_id]
			        + self.encode(
			            f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """				,add_special_tokens=UpperCAmelCase_   )
			        + [self.eos_token_id]
			        for prompt, answer in zip(dialogue[::2]				,dialogue[1::2]   )
			    ]				,[]				,)
			if not (dialogue[-1][0]):
					raise ValueError(f"""Last message must be from user, got {dialogue[-1]["role"]}"""   )
			dialog_tokens += [self.bos_token_id] + self.encode(
			    f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""				,add_special_tokens=UpperCAmelCase_   )
			return dialog_tokens



