





'''simple docstring'''






import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter




class    _A   (				TensorFormatter[Mapping, np.ndarray, Mapping]		):

		def __init__(      self				:      str     ,			__magic_name__				:      List[Any]=None     ,			**__magic_name__				:      List[str] )				->						Any:


					"""simple docstring"""

					super().__init__(features=__magic_name__ )
					__snake_case				:			int       			=    np_array_kwargs

		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      Optional[Any] )				->						Any:


					"""simple docstring"""

					if isinstance(__magic_name__     ,			__magic_name__ ):
								if column and all(
								    isinstance(__magic_name__     ,			np.ndarray ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
											return np.stack(__magic_name__ )
								else:
											# don't use np.array(column, dtype=object)
											# since it fails in certain cases
											# see https://stackoverflow.com/q/51005699
											__snake_case				:			int       			=    np.empty(len(__magic_name__ )     ,			dtype=__magic_name__ )
											__snake_case				:			Optional[int]       			=    column
											return out
					return column

		def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      Tuple )				->						List[Any]:


					"""simple docstring"""

					if isinstance(__magic_name__     ,			(str, bytes, type(__magic_name__ )) ):
								return value
					elif isinstance(__magic_name__     ,			(np.character, np.ndarray) ) and np.issubdtype(value.dtype     ,			np.character ):
								return value
					elif isinstance(__magic_name__     ,			np.number ):
								return value

					__snake_case				:			Tuple       			=    {}

					if isinstance(__magic_name__     ,			np.ndarray ) and np.issubdtype(value.dtype     ,			np.integer ):
								__snake_case				:			Union[str, Any]       			=    {"""dtype""": np.intaa}
					elif isinstance(__magic_name__     ,			np.ndarray ) and np.issubdtype(value.dtype     ,			np.floating ):
								__snake_case				:			int       			=    {"""dtype""": np.floataa}
					elif config.PIL_AVAILABLE and "PIL" in sys.modules:
								import PIL.Image

								if isinstance(__magic_name__     ,			PIL.Image.Image ):
											return np.asarray(__magic_name__     ,			**self.np_array_kwargs )

					return np.asarray(__magic_name__     ,			**{**default_dtype, **self.np_array_kwargs} )

		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      Optional[Any] )				->						Dict:


					"""simple docstring"""

					if config.TORCH_AVAILABLE and "torch" in sys.modules:
								import torch

								if isinstance(__magic_name__     ,			torch.Tensor ):
											return self._tensorize(data_struct.detach().cpu().numpy()[()] )
					if hasattr(__magic_name__     ,			"""__array__""" ) and not isinstance(__magic_name__     ,			(np.ndarray, np.character, np.number) ):
								__snake_case				:			Union[str, Any]       			=    data_struct.__array__()
					# support for nested types like struct of list of struct
					if isinstance(__magic_name__     ,			np.ndarray ):
								if data_struct.dtype == object:
											return self._consolidate([self.recursive_tensorize(__magic_name__ ) for substruct in data_struct] )
					if isinstance(__magic_name__     ,			(list, tuple) ):
								return self._consolidate([self.recursive_tensorize(__magic_name__ ) for substruct in data_struct] )
					return self._tensorize(__magic_name__ )

		def   lowercase__    (      self				:      Any     ,			__magic_name__				:      dict )				->						Union[str, Any]:


					"""simple docstring"""

					return map_nested(self._recursive_tensorize     ,			__magic_name__     ,			map_list=__magic_name__ )

		def   lowercase__    (      self				:      Tuple     ,			__magic_name__				:      pa.Table )				->						Mapping:


					"""simple docstring"""

					__snake_case				:			Union[str, Any]       			=    self.numpy_arrow_extractor().extract_row(__magic_name__ )
					__snake_case				:			Tuple       			=    self.python_features_decoder.decode_row(__magic_name__ )
					return self.recursive_tensorize(__magic_name__ )

		def   lowercase__    (      self				:      Optional[int]     ,			__magic_name__				:      pa.Table )				->						np.ndarray:


					"""simple docstring"""

					__snake_case				:			Dict       			=    self.numpy_arrow_extractor().extract_column(__magic_name__ )
					__snake_case				:			str       			=    self.python_features_decoder.decode_column(__magic_name__     ,			pa_table.column_names[0] )
					__snake_case				:			List[Any]       			=    self.recursive_tensorize(__magic_name__ )
					__snake_case				:			Union[str, Any]       			=    self._consolidate(__magic_name__ )
					return column

		def   lowercase__    (      self				:      Any     ,			__magic_name__				:      pa.Table )				->						Mapping:


					"""simple docstring"""

					__snake_case				:			Union[str, Any]       			=    self.numpy_arrow_extractor().extract_batch(__magic_name__ )
					__snake_case				:			Union[str, Any]       			=    self.python_features_decoder.decode_batch(__magic_name__ )
					__snake_case				:			Tuple       			=    self.recursive_tensorize(__magic_name__ )
					for column_name in batch:
								__snake_case				:			Tuple       			=    self._consolidate(batch[column_name] )
					return batch









'''simple docstring'''






import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

require_version("tokenizers>=0.13.3")

if is_sentencepiece_available():
	from .tokenization_llama import LlamaTokenizer
else:
	__UpperCamelCase										=  None

__UpperCamelCase										=  logging.get_logger(__name__)
__UpperCamelCase										=  {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase						,		__UpperCamelCase										=  "[INST]", "[/INST]"
__UpperCamelCase						,		__UpperCamelCase										=  "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
__UpperCamelCase										=  "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."




class    _A   (				__lowercase		):
		lowercase__:      Union[str, Any]							    =      VOCAB_FILES_NAMES
		lowercase__:      Tuple							    =      LlamaTokenizer
		lowercase__:      List[Any]							    =      '''left'''
		lowercase__:      int							    =      ['''input_ids''', '''attention_mask''']

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


					"""simple docstring"""

					super().__init__(
					    vocab_file=__magic_name__     ,			tokenizer_file=__magic_name__     ,			clean_up_tokenization_spaces=__magic_name__     ,			unk_token=__magic_name__     ,			bos_token=__magic_name__     ,			eos_token=__magic_name__     ,			**__magic_name__     ,			)
					__snake_case				:			List[Any]       			=    add_bos_token
					__snake_case				:			int       			=    add_eos_token
					self.update_post_processor()

					__snake_case				:			Dict       			=    vocab_file
					__snake_case				:			int       			=    False if not self.vocab_file else True

		def   lowercase__    (      self				:      Any )				->						Dict:


					"""simple docstring"""

					__snake_case				:			Any       			=    self.bos_token
					__snake_case				:			Dict       			=    self.bos_token_id

					__snake_case				:			List[str]       			=    self.eos_token
					__snake_case				:			str       			=    self.eos_token_id

					__snake_case				:			List[Any]       			=    f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
					__snake_case				:			str       			=    f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

					__snake_case				:			List[str]       			=    []
					if self.add_bos_token:
								special_tokens.append((bos, bos_token_id) )
					if self.add_eos_token:
								special_tokens.append((eos, eos_token_id) )
					__snake_case				:			Dict       			=    processors.TemplateProcessing(
					    single=__magic_name__     ,			pair=__magic_name__     ,			special_tokens=__magic_name__ )

		@property
		def   lowercase__    (      self				:      Tuple )				->						List[Any]:


					"""simple docstring"""

					return self._add_eos_token

		@property
		def   lowercase__    (      self				:      Union[str, Any] )				->						str:


					"""simple docstring"""

					return self._add_bos_token

		@add_eos_token.setter
		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      Optional[Any] )				->						int:


					"""simple docstring"""

					__snake_case				:			Any       			=    value
					self.update_post_processor()

		@add_bos_token.setter
		def   lowercase__    (      self				:      Union[str, Any]     ,			__magic_name__				:      Optional[Any] )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    value
					self.update_post_processor()

		def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      str     ,			__magic_name__				:      Optional[str] = None )				->						Tuple[str]:


					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer.""" )

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

					if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ):
								copyfile(self.vocab_file     ,			__magic_name__ )

					return (out_vocab_file,)

		def   lowercase__    (      self				:      int     ,			__magic_name__				:      "Conversation" )				->						int:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    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...)""" )

					__snake_case				:			str       			=    []
					if len(conversation.past_user_inputs ) > 0:
								if not conversation.past_user_inputs[0].startswith(__magic_name__ ) or E_SYS not in conversation.past_user_inputs[0]:
											__snake_case				:			Tuple       			=    (
											    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
											)
					elif not dialogue[0][1].startswith(__magic_name__ ) or E_SYS not in dialogue[0][1]:
								__snake_case				:			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=__magic_name__ )
					        + [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=__magic_name__ )
					return dialog_tokens


