
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:
					_SCREAMING_SNAKE_CASE     :   Any							 =      None

_SCREAMING_SNAKE_CASE     :   int							 =      logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE     :   Any							 =      {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE     :   Dict							 =      '[INST]', '[/INST]'
_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE     :   Tuple							 =      '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
_SCREAMING_SNAKE_CASE     :   Dict							 =      '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 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					VOCAB_FILES_NAMES
	_lowerCAmelCase											=					LlamaTokenizer
	_lowerCAmelCase											=					"left"
	_lowerCAmelCase											=					["input_ids", "attention_mask"]





	def __init__(      self					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__=False					,							UpperCamelCase__="<unk>"					,							UpperCamelCase__="<s>"					,							UpperCamelCase__="</s>"					,							UpperCamelCase__=True					,							UpperCamelCase__=False					,							**UpperCamelCase__					,							):
					super().__init__(
					    vocab_file=UpperCamelCase__					,							tokenizer_file=UpperCamelCase__					,							clean_up_tokenization_spaces=UpperCamelCase__					,							unk_token=UpperCamelCase__					,							bos_token=UpperCamelCase__					,							eos_token=UpperCamelCase__					,							**UpperCamelCase__					,							)
					A__	:						Optional[Any]								=				add_bos_token
					A__	:						Dict								=				add_eos_token
					self.update_post_processor()

					A__	:						Optional[int]								=				vocab_file
					A__	:						Union[str, Any]								=				False if not self.vocab_file else True





	def 	__snake_case					(      self		):
					A__	:						int								=				self.bos_token
					A__	:						Dict								=				self.bos_token_id

					A__	:						Any								=				self.eos_token
					A__	:						Any								=				self.eos_token_id

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

					A__	:						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)		)
					A__	:						Tuple								=				processors.TemplateProcessing(
					    single=UpperCamelCase__					,							pair=UpperCamelCase__					,							special_tokens=UpperCamelCase__		)





	@property
	def 	__snake_case					(      self		):
					return self._add_eos_token





	@property
	def 	__snake_case					(      self		):
					return self._add_bos_token





	@add_eos_token.setter
	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Tuple								=				value
					self.update_post_processor()





	@add_bos_token.setter
	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				value
					self.update_post_processor()





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__ = None		):
					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(UpperCamelCase__		):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
									return
					A__	:						Dict								=				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__		):
									copyfile(self.vocab_file					,							UpperCamelCase__		)

					return (out_vocab_file,)





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						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...)'''		)

					A__	:						str								=				[]
					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]:
													A__	:						Union[str, Any]								=				(
													    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]:
									A__	:						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

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652E26,
}

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199E-27,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    float							)			->							float:

				"""simple docstring"""







				if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
								A__	:						int								=				(
								    F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
								    F"Supported values are: {', '.join(__UpperCamelCase							)}"
								)
								raise ValueError(__UpperCamelCase							)
				return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()