'''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				:		Optional[Any]       			=		None

_UpperCamelCase				:		List[Any]       			=		logging.get_logger(__name__)
_UpperCamelCase				:		List[Any]       			=		{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase			,      _UpperCamelCase				:		int       			=		'[INST]', '[/INST]'
_UpperCamelCase			,      _UpperCamelCase				:		Tuple       			=		'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
_UpperCamelCase				:		Any       			=		'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   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  VOCAB_FILES_NAMES
   __lowerCamelCase    =  LlamaTokenizer
   __lowerCamelCase    =  '''left'''
   __lowerCamelCase    =  ['''input_ids''', '''attention_mask''']





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

    __UpperCAmelCase         =					vocab_file
    __UpperCAmelCase         =					False if not self.vocab_file else True





   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					self.bos_token
    __UpperCAmelCase         =					self.bos_token_id

    __UpperCAmelCase         =					self.eos_token
    __UpperCAmelCase         =					self.eos_token_id

    __UpperCAmelCase         =					f"""{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"""
    __UpperCAmelCase         =					f"""{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"""

    __UpperCAmelCase         =					[]
    if self.add_bos_token:
     special_tokens.append((bos, bos_token_id)	)
    if self.add_eos_token:
     special_tokens.append((eos, eos_token_id)	)
    __UpperCAmelCase         =					processors.TemplateProcessing(
        single=a			,pair=a			,special_tokens=a	)





   @property
   def 			snake_case	(							self:    Any	):
    return self._add_eos_token





   @property
   def 			snake_case	(							self:    Dict	):
    return self._add_bos_token





   @add_eos_token.setter
   def 			snake_case	(							self:    Any			,a:    List[str]	):
    __UpperCAmelCase         =					value
    self.update_post_processor()





   @add_bos_token.setter
   def 			snake_case	(							self:    Tuple			,a:    Tuple	):
    __UpperCAmelCase         =					value
    self.update_post_processor()





   def 			snake_case	(							self:    Union[str, Any]			,a:    str			,a:    Optional[str] = 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(a	):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
     return
    __UpperCAmelCase         =					os.path.join(
        a			,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

    return (out_vocab_file,)





   def 			snake_case	(							self:    Any			,a:    "Conversation"	):
    __UpperCAmelCase         =					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...)'	)

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




'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image

class   _lowercase(     unittest.TestCase   ,_lowerCamelCase				):



   """simple docstring"""





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					load_tool('image-captioning'	)
    self.tool.setup()
    __UpperCAmelCase         =					load_tool('image-captioning'			,remote=a	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)



