'''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:
  __snake_case  : Optional[Any]		       =    None

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

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

# fmt: off
__snake_case  : List[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       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   Dict  					=							VOCAB_FILES_NAMES
       __lowercase   :   Optional[int]  					=							LlamaTokenizer
       __lowercase   :   int  					=							'left'
       __lowercase   :   List[str]  					=							['input_ids', 'attention_mask']

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=False       ,	_SCREAMING_SNAKE_CASE="<unk>"       ,	_SCREAMING_SNAKE_CASE="<s>"       ,	_SCREAMING_SNAKE_CASE="</s>"       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=False       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	Tuple:
           super().__init__(
               vocab_file=_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	bos_token=_SCREAMING_SNAKE_CASE       ,	eos_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE       ,	)
           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					)						->	Optional[int]:
           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=_SCREAMING_SNAKE_CASE       ,	pair=_SCREAMING_SNAKE_CASE       ,	special_tokens=_SCREAMING_SNAKE_CASE					)

       @property
       def    __A			( self					)						->	Union[str, Any]:
           return self._add_eos_token

       @property
       def    __A			( self					)						->	Union[str, Any]:
           return self._add_bos_token

       @add_eos_token.setter
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           A_      =  value
           self.update_post_processor()

       @add_bos_token.setter
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  value
           self.update_post_processor()

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           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(_SCREAMING_SNAKE_CASE					):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
               return
           A_      =  os.path.join(
               _SCREAMING_SNAKE_CASE       ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

           return (out_vocab_file,)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           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(_SCREAMING_SNAKE_CASE					) 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(_SCREAMING_SNAKE_CASE					) 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=_SCREAMING_SNAKE_CASE					)
                   + [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=_SCREAMING_SNAKE_CASE					)
           return dialog_tokens







'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
  __snake_case  : Any		       =    argparse.ArgumentParser()

  # Hyperparameters sent by the client are passed as command-line arguments to the script.
  parser.add_argument('--epochs', type=int, default=1)
  parser.add_argument('--per_device_train_batch_size', type=int, default=16)
  parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
  parser.add_argument('--model_name_or_path', type=str)
  parser.add_argument('--learning_rate', type=str, default=5E-5)
  parser.add_argument('--do_train', type=bool, default=True)
  parser.add_argument('--do_eval', type=bool, default=True)
  parser.add_argument('--output_dir', type=str)

  __snake_case       ,       __snake_case  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

  # Set up logging
  __snake_case  : Union[str, Any]		       =    logging.getLogger(__name__)

  logging.basicConfig(
      level=logging.getLevelName('INFO'),
      handlers=[logging.StreamHandler(sys.stdout)],
      format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
  )

  # Load model and tokenizer
  __snake_case  : Optional[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case  : int		       =    train_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Dict		       =    {
      x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : str		       =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    test_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Optional[int]		       =    {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

  # fine optimizer and loss
  __snake_case  : Optional[int]		       =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  __snake_case  : Union[str, Any]		       =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  __snake_case  : int		       =    [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case  : Union[str, Any]		       =    time.time() - start_train_time

  logger.info('*** Train ***')
  logger.info(F"""train_runtime = {end_train_time}""")
  for key, value in train_results.history.items():
    logger.info(F"""  {key} = {value}""")






