

'''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:
  _a			:	Any								=  None

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

_a							, _a			:	str								=  '[INST]', '[/INST]'
_a							, _a			:	Any								=  '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
_a			:	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_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Any      					=				VOCAB_FILES_NAMES
  __lowerCAmelCase   :	int      					=				LlamaTokenizer
  __lowerCAmelCase   :	Optional[int]      					=				"left"
  __lowerCAmelCase   :	Any      					=				["input_ids", "attention_mask"]
  def __init__(       self				,	a_=None				,	a_=None				,	a_=False				,	a_="<unk>"				,	a_="<s>"				,	a_="</s>"				,	a_=True				,	a_=False				,	**a_				,	)							->			Any:


    """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_				,	)
    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       )							->			Union[str, Any]:


    """simple docstring"""
    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:


    """simple docstring"""
    return self._add_eos_token
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    return self._add_bos_token
  @add_eos_token.setter
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		value
    self.update_post_processor()
  @add_bos_token.setter
  def   snake_case_     (       self				,	a_       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		value
    self.update_post_processor()
  def   snake_case_     (       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
    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				,	a_       )							->			Tuple:


    """simple docstring"""
    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 argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				)
  parser.add_argument('--prompt'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''  )
  parser.add_argument('--length'    ,				type=SCREAMING_SNAKE_CASE    ,				default=20  )
  parser.add_argument('--stop_token'    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				help='Token at which text generation is stopped'  )
  parser.add_argument(
      '--temperature'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='temperature of 1.0 has no effect, lower tend toward greedy sampling'    ,				)
  parser.add_argument(
      '--repetition_penalty'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='primarily useful for CTRL model; in that case, use 1.2'  )
  parser.add_argument('--k'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0  )
  parser.add_argument('--penalty_alpha'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.0  )
  parser.add_argument('--p'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.9  )

  parser.add_argument('--prefix'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Text added prior to input.'  )
  parser.add_argument('--padding_text'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Deprecated, the use of `--prefix` is preferred.'  )
  parser.add_argument('--xlm_language'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Optional language when used with the XLM model.'  )

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument('--no_cuda'    ,				action='store_true'    ,				help='Avoid using CUDA when available'  )
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''  )

  set_seed(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path  )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device  )

  if args.fpaa:
    model.half()

  logger.info(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

    # Remove all text after the stop token
    UpperCAmelCase  							=		text[: text.find(args.stop_token  ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    UpperCAmelCase  							=		(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE  )  ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


