



"""simple docstring"""







import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

__snake_case	:				str  					=			logging.get_logger(__name__)

__snake_case	:				Dict  					=			{'vocab_file': 'tokenizer.model'}

__snake_case	:				Dict  					=			{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
__snake_case	:				Optional[int]  					=			{
    'hf-internal-testing/llama-tokenizer': 2_048,
}
__snake_case	:				Union[str, Any]  					=			'▁'

__snake_case	:				Optional[Any]  					=			'[INST]', '[/INST]'
__snake_case	:				int  					=			'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__snake_case	:				str  					=			'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  (					a				):





     """simple docstring"""






     _lowerCamelCase					: Optional[int]				   =VOCAB_FILES_NAMES
     _lowerCamelCase					: Tuple				   =PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase					: Optional[int]				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase					: Optional[Any]				   =["input_ids", "attention_mask"]





     def __init__(					self :							Dict	,      _lowerCamelCase :							str	,      _lowerCamelCase :							int="<unk>"	,      _lowerCamelCase :							Optional[Any]="<s>"	,      _lowerCamelCase :							List[str]="</s>"	,      _lowerCamelCase :							Dict=None	,      _lowerCamelCase :							Optional[Dict[str, Any]] = None	,      _lowerCamelCase :							Optional[int]=True	,      _lowerCamelCase :							int=False	,      _lowerCamelCase :							Optional[Any]=False	,      _lowerCamelCase :							str=True	,      **_lowerCamelCase :							List[str]	,      ):
          A__				=   {} if sp_model_kwargs is None else sp_model_kwargs
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else bos_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else eos_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else unk_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else pad_token
          super().__init__(
              bos_token=_lowerCamelCase	,      eos_token=_lowerCamelCase	,      unk_token=_lowerCamelCase	,      pad_token=_lowerCamelCase	,      add_bos_token=_lowerCamelCase	,      add_eos_token=_lowerCamelCase	,      sp_model_kwargs=self.sp_model_kwargs	,      clean_up_tokenization_spaces=_lowerCamelCase	,      legacy=_lowerCamelCase	,      **_lowerCamelCase	,      )
          if legacy:
               logger.warning_once(
                   F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                   ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
          A__				=   legacy
          A__				=   vocab_file
          A__				=   add_bos_token
          A__				=   add_eos_token
          A__				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(_lowerCamelCase )





     def __getstate__(					self :							List[str] ):
          A__				=   self.__dict__.copy()
          A__				=   None
          A__				=   self.sp_model.serialized_model_proto()
          return state





     def __setstate__(					self :							Tuple	,      _lowerCamelCase :							str ):
          A__				=   d
          A__				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )





     @property
     def 						A__  (					self :							Tuple ):
          return self.sp_model.get_piece_size()





     def 						A__  (					self :							int ):
          A__				=   {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Dict	,      **_lowerCamelCase :							str ):
          # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
          # the beginning of the text
          if not self.legacy:
               A__				=   SPIECE_UNDERLINE + text.replace(_lowerCamelCase	,      ''' ''' )
          return super().tokenize(_lowerCamelCase	,      **_lowerCamelCase )





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							int ):
          if not self.legacy:
               A__				=   text.startswith(_lowerCamelCase )
               if is_first:
                    A__				=   text[1:]

          A__				=   self.sp_model.encode(_lowerCamelCase	,      out_type=_lowerCamelCase )

          if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
               A__				=   ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
          return tokens





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Union[str, Any] ):
          return self.sp_model.piece_to_id(_lowerCamelCase )





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Dict ):
          A__				=   self.sp_model.IdToPiece(_lowerCamelCase )
          return token





     def 						A__  (					self :							List[str]	,      _lowerCamelCase :							List[Any] ):
          A__				=   []
          A__				=   ''''''
          A__				=   False
          for i, token in enumerate(_lowerCamelCase ):
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special and i != 0:
                         out_string += " "
                    out_string += self.sp_model.decode(_lowerCamelCase ) + token
                    A__				=   True
                    A__				=   []
               else:
                    current_sub_tokens.append(_lowerCamelCase )
                    A__				=   False
          out_string += self.sp_model.decode(_lowerCamelCase )
          return out_string





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Optional[str] = None ):
          if not os.path.isdir(_lowerCamelCase ):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
               return
          A__				=   os.path.join(
              _lowerCamelCase	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
               copyfile(self.vocab_file	,      _lowerCamelCase )
          elif not os.path.isfile(self.vocab_file ):
               with open(_lowerCamelCase	,      '''wb''' ) as fi:
                    A__				=   self.sp_model.serialized_model_proto()
                    fi.write(_lowerCamelCase )

          return (out_vocab_file,)





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							str=None ):
          A__				=   [self.bos_token_id] if self.add_bos_token else []
          A__				=   [self.eos_token_id] if self.add_eos_token else []

          A__				=   bos_token_id + token_ids_a + eos_token_id

          if token_ids_a is not None:
               A__				=   output + bos_token_id + token_ids_a + eos_token_id

          return output





     def 						A__  (					self :							List[str]	,      _lowerCamelCase :							List[int]	,      _lowerCamelCase :							Optional[List[int]] = None	,      _lowerCamelCase :							bool = False ):
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_lowerCamelCase	,      token_ids_a=_lowerCamelCase	,      already_has_special_tokens=_lowerCamelCase )

          A__				=   [1] if self.add_bos_token else []
          A__				=   [1] if self.add_eos_token else []

          if token_ids_a is None:
               return bos_token_id + ([0] * len(_lowerCamelCase )) + eos_token_id
          return (
              bos_token_id
              + ([0] * len(_lowerCamelCase ))
              + eos_token_id
              + bos_token_id
              + ([0] * len(_lowerCamelCase ))
              + eos_token_id
          )





     def 						A__  (					self :							str	,      _lowerCamelCase :							List[int]	,      _lowerCamelCase :							Optional[List[int]] = None ):
          A__				=   [self.bos_token_id] if self.add_bos_token else []
          A__				=   [self.eos_token_id] if self.add_eos_token else []

          A__				=   [0] * len(bos_token_id + token_ids_a + eos_token_id )

          if token_ids_a is not None:
               output += [1] * len(bos_token_id + token_ids_a + eos_token_id )

          return output





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							"Conversation" ):
          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(_lowerCamelCase ) 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(_lowerCamelCase ) 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=_lowerCamelCase )
                  + [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=_lowerCamelCase )
          return dialog_tokens








"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case	:				Tuple  					=			16
__snake_case	:				Any  					=			32



def   a_	(			__a		,							__a      ):
     # Initialize accelerator
     if args.with_tracking:
          A__				=   Accelerator(
              cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with='''all'''		,							project_dir=args.project_dir      )
     else:
          A__				=   Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision      )

     if hasattr(args.checkpointing_steps		,							'''isdigit'''      ):
          if args.checkpointing_steps == "epoch":
               A__				=   args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               A__				=   int(args.checkpointing_steps      )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
     else:
          A__				=   None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A__				=   config['''lr''']
     A__				=   int(config['''num_epochs''']      )
     A__				=   int(config['''seed''']      )
     A__				=   int(config['''batch_size''']      )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          A__				=   os.path.split(__a      )[-1].split('''.'''      )[0]
          accelerator.init_trackers(__a		,							__a      )

     A__				=   AutoTokenizer.from_pretrained('''bert-base-cased'''      )
     A__				=   load_dataset('''glue'''		,							'''mrpc'''      )
     A__				=   evaluate.load('''glue'''		,							'''mrpc'''      )

     def tokenize_function(__a      ):
          # max_length=None => use the model max length (it's actually the default)
          A__				=   tokenizer(examples['''sentence1''']		,							examples['''sentence2''']		,							truncation=__a		,							max_length=__a      )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          A__				=   datasets.map(
              __a		,							batched=__a		,							remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,							)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     A__				=   tokenized_datasets.rename_column('''label'''		,							'''labels'''      )

     # If the batch size is too big we use gradient accumulation
     A__				=   1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          A__				=   batch_size // MAX_GPU_BATCH_SIZE
          A__				=   MAX_GPU_BATCH_SIZE

     def collate_fn(__a      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A__				=   128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               A__				=   16
          elif accelerator.mixed_precision != "no":
               A__				=   8
          else:
               A__				=   None

          return tokenizer.pad(
              __a		,							padding='''longest'''		,							max_length=__a		,							pad_to_multiple_of=__a		,							return_tensors='''pt'''		,							)

     # Instantiate dataloaders.
     A__				=   DataLoader(
         tokenized_datasets['''train''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )
     A__				=   DataLoader(
         tokenized_datasets['''validation''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )

     set_seed(__a      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A__				=   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,							return_dict=__a      )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     A__				=   model.to(accelerator.device      )

     # Instantiate optimizer
     A__				=   AdamW(params=model.parameters()		,							lr=__a      )

     # Instantiate scheduler
     A__				=   get_linear_schedule_with_warmup(
         optimizer=__a		,							num_warmup_steps=100		,							num_training_steps=(len(__a      ) * num_epochs) // gradient_accumulation_steps		,							)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     A__	,     A__	,     A__	,     A__	,     A__				=   accelerator.prepare(
         __a		,							__a		,							__a		,							__a		,							__a      )

     # We need to keep track of how many total steps we have iterated over
     A__				=   0
     # We also need to keep track of the stating epoch so files are named properly
     A__				=   0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''      )
               accelerator.load_state(args.resume_from_checkpoint      )
               A__				=   os.path.basename(args.resume_from_checkpoint      )
          else:
               # Get the most recent checkpoint
               A__				=   [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
               dirs.sort(key=os.path.getctime      )
               A__				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          A__				=   os.path.splitext(__a      )[0]

          if "epoch" in training_difference:
               A__				=   int(training_difference.replace('''epoch_'''		,							''''''      )      ) + 1
               A__				=   None
          else:
               A__				=   int(training_difference.replace('''step_'''		,							''''''      )      )
               A__				=   resume_step // len(__a      )
               resume_step -= starting_epoch * len(__a      )

    # Now we train the model
     for epoch in range(__a		,							__a      ):
          model.train()
          if args.with_tracking:
               A__				=   0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               A__				=   accelerator.skip_first_batches(__a		,							__a      )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               A__				=   train_dataloader
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               A__				=   model(**__a      )
               A__				=   outputs.loss
               A__				=   loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__a      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__a		,							__a      ):
                    A__				=   f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              A__				=   os.path.join(args.output_dir		,							__a      )
                         accelerator.save_state(__a      )

          model.eval()
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               with torch.no_grad():
                    A__				=   model(**__a      )
               A__				=   outputs.logits.argmax(dim=-1      )
               A__	,     A__				=   accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
               metric.add_batch(
                   predictions=__a		,							references=__a		,							)

          A__				=   metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''		,							__a      )
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': eval_metric['''accuracy'''],
                       '''f1''': eval_metric['''f1'''],
                       '''train_loss''': total_loss.item() / len(__a      ),
                       '''epoch''': epoch,
                   }		,							step=__a		,							)

          if checkpointing_steps == "epoch":
               A__				=   f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    A__				=   os.path.join(args.output_dir		,							__a      )
               accelerator.save_state(__a      )

     if args.with_tracking:
          accelerator.end_training()



def   a_	(			):
     A__				=   argparse.ArgumentParser(description='''Simple example of training script.'''      )
     parser.add_argument(
         '''--mixed_precision'''		,							type=__a		,							default=__a		,							choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,							help='''Whether to use mixed precision. Choose'''
         '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
         '''and an Nvidia Ampere GPU.'''		,							)
     parser.add_argument('''--cpu'''		,							action='''store_true'''		,							help='''If passed, will train on the CPU.'''      )
     parser.add_argument(
         '''--checkpointing_steps'''		,							type=__a		,							default=__a		,							help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''		,							)
     parser.add_argument(
         '''--resume_from_checkpoint'''		,							type=__a		,							default=__a		,							help='''If the training should continue from a checkpoint folder.'''		,							)
     parser.add_argument(
         '''--with_tracking'''		,							action='''store_true'''		,							help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,							)
     parser.add_argument(
         '''--output_dir'''		,							type=__a		,							default='''.'''		,							help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,							)
     parser.add_argument(
         '''--project_dir'''		,							type=__a		,							default='''logs'''		,							help='''Location on where to store experiment tracking logs` and relevent project information'''		,							)
     A__				=   parser.parse_args()
     A__				=   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__a		,							__a      )


if __name__ == "__main__":
 main()



