



'''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:
  lowercase_			=  None

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

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

# fmt: off
lowercase_			=  "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      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	List[Any]										=	VOCAB_FILES_NAMES
 __lowerCamelCase   :	Optional[int]										=	LlamaTokenizer
 __lowerCamelCase   :	Union[str, Any]										=	'left'
 __lowerCamelCase   :	str										=	['input_ids', 'attention_mask']



 def __init__(self			,       A=None			,       A=None			,       A=False			,       A="<unk>"			,       A="<s>"			,       A="</s>"			,       A=True			,       A=False			,       **A			,       )   ->					Dict:


      """simple docstring"""



      super().__init__(
          vocab_file=A			,       tokenizer_file=A			,       clean_up_tokenization_spaces=A			,       unk_token=A			,       bos_token=A			,       eos_token=A			,       **A			,       )
      _a     =  add_bos_token
      _a     =  add_eos_token
      self.update_post_processor()

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



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a     =  self.bos_token
      _a     =  self.bos_token_id

      _a     =  self.eos_token
      _a     =  self.eos_token_id

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

      _a     =  []
      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     =  processors.TemplateProcessing(
          single=A			,       pair=A			,       special_tokens=A	)



 @property
 def  a__  (self	)   ->					Any:


      """simple docstring"""



      return self._add_eos_token



 @property
 def  a__  (self	)   ->					str:


      """simple docstring"""



      return self._add_bos_token



 @add_eos_token.setter
 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      _a     =  value
      self.update_post_processor()



 @add_bos_token.setter
 def  a__  (self			,       A	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  value
      self.update_post_processor()



 def  a__  (self			,       A			,       A = 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(A	):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
           return
      _a     =  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  a__  (self			,       A	)   ->					Tuple:


      """simple docstring"""



      _a     =  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     =  []
      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]:
                _a     =  (
                    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]:
           _a     =  (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 importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


lowercase_			=  importlib.metadata.version("numpy")
lowercase_			=  ".".join([str(x) for x in sys.version_info[:3]])






class __A      (  A						):



 '''simple docstring'''







 def  a__  (self	)   ->					Any:


      """simple docstring"""



      require_version_core('''numpy<1000.4.5'''	)
      require_version_core('''numpy<1000.4'''	)
      require_version_core('''numpy<1000'''	)

      # le
      require_version_core('''numpy<=1000.4.5'''	)
      require_version_core(f'''numpy<={numpy_ver}'''	)

      # eq
      require_version_core(f'''numpy=={numpy_ver}'''	)

      # ne
      require_version_core('''numpy!=1000.4.5'''	)

      # ge
      require_version_core('''numpy>=1.0'''	)
      require_version_core('''numpy>=1.0.0'''	)
      require_version_core(f'''numpy>={numpy_ver}'''	)

      # gt
      require_version_core('''numpy>1.0.0'''	)

      # mix
      require_version_core('''numpy>1.0.0,<1000'''	)

      # requirement w/o version
      require_version_core('''numpy'''	)

      # unmet requirements due to version conflict
      for req in ["numpy==1.0.0", "numpy>=1000.0.0", f'''numpy<{numpy_ver}''']:
           try:
                require_version_core(A	)
           except ImportError as e:
                self.assertIn(f'''{req} is required'''			,       str(A	)	)
                self.assertIn('''but found'''			,       str(A	)	)

        # unmet requirements due to missing module
      for req in ["numpipypie>1", "numpipypie2"]:
           try:
                require_version_core(A	)
           except importlib.metadata.PackageNotFoundError as e:
                self.assertIn(f'''The \'{req}\' distribution was not found and is required by this application'''			,       str(A	)	)
                self.assertIn('''Try: pip install transformers -U'''			,       str(A	)	)

        # bogus requirements formats:
        # 1. whole thing
      for req in ["numpy??1.0.0", "numpy1.0.0"]:
           try:
                require_version_core(A	)
           except ValueError as e:
                self.assertIn('''requirement needs to be in the pip package format'''			,       str(A	)	)
        # 2. only operators
      for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
           try:
                require_version_core(A	)
           except ValueError as e:
                self.assertIn('''need one of '''			,       str(A	)	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      require_version('''python>=3.6.0'''	)

      # not matching requirements
      for req in ["python>9.9.9", "python<3.0.0"]:
           try:
                require_version_core(A	)
           except ImportError as e:
                self.assertIn(f'''{req} is required'''			,       str(A	)	)
                self.assertIn(f'''but found python=={python_ver}'''			,       str(A	)	)



