




import argparse

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)
#
# 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
#
########################################################################


_lowerCamelCase     :		List[str]								=       16
_lowerCamelCase     :		Any								=       32







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_ = 16						)       ->	Optional[Any]:



       """simple docstring"""

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

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

       def collate_fn(lowercase_						):
              # 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(
                  lowercase_			,    padding='''longest'''			,    max_length=lowercase_			,    pad_to_multiple_of=lowercase_			,    return_tensors='''pt'''			,    )

       # Instantiate dataloaders.
       A__							=   DataLoader(
           tokenized_datasets['''train''']			,    shuffle=lowercase_			,    collate_fn=lowercase_			,    batch_size=lowercase_			,    drop_last=lowercase_						)
       A__							=   DataLoader(
           tokenized_datasets['''validation''']			,    shuffle=lowercase_			,    collate_fn=lowercase_			,    batch_size=lowercase_			,    drop_last=(accelerator.mixed_precision == '''fp8''')			,    )

       return train_dataloader, eval_dataloader







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   Accelerator(cpu=args.cpu			,    mixed_precision=args.mixed_precision						)
       # 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''']						)

       A__							=   evaluate.load('''glue'''			,    '''mrpc'''						)

       # 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

       set_seed(lowercase_						)
       A__			,							A__							=   get_dataloaders(lowercase_			,    lowercase_						)
       # 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=lowercase_						)

       # 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=lowercase_						)

       # Instantiate scheduler
       A__							=   get_linear_schedule_with_warmup(
           optimizer=lowercase_			,    num_warmup_steps=100			,    num_training_steps=(len(lowercase_						) * 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(
           lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)

       # Now we train the model
       for epoch in range(lowercase_						):
              model.train()
              for step, batch in enumerate(lowercase_						):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device						)
                     A__							=   model(**lowercase_						)
                     A__							=   outputs.loss
                     A__							=   loss / gradient_accumulation_steps
                     accelerator.backward(lowercase_						)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              A__							=   metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""			,    lowercase_						)







def SCREAMING_SNAKE_CASE    (	)       ->	Dict:



       """simple docstring"""

       A__							=   argparse.ArgumentParser(description='''Simple example of training script.'''						)
       parser.add_argument(
           '''--mixed_precision'''			,    type=lowercase_			,    default=lowercase_			,    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.'''						)
       A__							=   parser.parse_args()
       A__							=   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_			,    lowercase_						)


if __name__ == "__main__":
  main()








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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase     :		Union[str, Any]								=       """▁"""

_lowerCamelCase     :		Optional[Any]								=       {"""vocab_file""": """spiece.model"""}

_lowerCamelCase     :		str								=       {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

_lowerCamelCase     :		List[str]								=       {
    """google/pegasus-xsum""": 512,
}


_lowerCamelCase     :		int								=       logging.get_logger(__name__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES

    UpperCAmelCase__							  =				VOCAB_FILES_NAMES
    UpperCAmelCase__							  =				PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =				['''input_ids''', '''attention_mask''']


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str="<pad>" ,			UpperCAmelCase__				:     Optional[Any]="</s>" ,			UpperCAmelCase__				:     Any="<unk>" ,			UpperCAmelCase__				:     Union[str, Any]="<mask_2>" ,			UpperCAmelCase__				:     List[str]="<mask_1>" ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Optional[Any]=103 ,			UpperCAmelCase__				:     Optional[Dict[str, Any]] = None ,			**UpperCAmelCase__				:     Dict ,			)						->None:





           '''simple docstring'''

           A__							=   offset
           if additional_special_tokens is not None:
                  if not isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         raise TypeError(
                             f"""additional_special_tokens should be of type {type(UpperCAmelCase__)}, but is"""
                             f""" {type(UpperCAmelCase__)}""")

                  A__							=   (
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f"""<unk_{i}>""" for i in range(len(UpperCAmelCase__) ,			self.offset - 1)
                  ]

                  if len(set(UpperCAmelCase__)) != len(UpperCAmelCase__):
                         raise ValueError(
                             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                             f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""")
                  A__							=   additional_special_tokens_extended
           else:
                  A__							=   [mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 ,			self.offset)]

           A__							=   {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__ ,			unk_token=UpperCAmelCase__ ,			mask_token=UpperCAmelCase__ ,			pad_token=UpperCAmelCase__ ,			mask_token_sent=UpperCAmelCase__ ,			offset=UpperCAmelCase__ ,			additional_special_tokens=UpperCAmelCase__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**UpperCAmelCase__ ,			)
           A__							=   mask_token_sent
           A__							=   vocab_file
           A__							=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)

           # add special tokens to encoder dict
           A__							=   {
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      })

           if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 ,			self.offset - 1)})

           A__							=   {v: k for k, v in self.encoder.items()}


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->int:





           '''simple docstring'''

           return len(self.sp_model) + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict[str, int]:





           '''simple docstring'''

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


    def __getstate__(      self				:     Any)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   self.__dict__.copy()
           A__							=   None
           return state


    def __setstate__(      self				:     int ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__							=   d

           # for backward compatibility
           if not hasattr(self ,			'''sp_model_kwargs'''):
                  A__							=   {}

           A__							=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str)						->List[str]:





           '''simple docstring'''

           return self.sp_model.encode(UpperCAmelCase__ ,			out_type=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           if token in self.decoder:
                  return self.decoder[token]
           elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
           A__							=   self.sp_model.piece_to_id(UpperCAmelCase__)
           return sp_id + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     int)						->str:





           '''simple docstring'''

           if index in self.encoder:
                  return self.encoder[index]
           elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
           else:
                  A__							=   self.sp_model.IdToPiece(index - self.offset)
           return token


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->Optional[int]:





           '''simple docstring'''

           A__							=   []
           A__							=   ''''''
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(UpperCAmelCase__) + token
                         A__							=   []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[int]=False)						->Union[str, Any]:





           '''simple docstring'''

           return 1


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     List ,			UpperCAmelCase__				:     Optional[List] = None ,			UpperCAmelCase__				:     bool = False)						->List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  return self._special_token_mask(UpperCAmelCase__)
           elif token_ids_a is None:
                  return self._special_token_mask(UpperCAmelCase__) + [1]
           else:
                  return self._special_token_mask(token_ids_a + token_ids_a) + [1]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     List[str]=None)						->List[int]:





           '''simple docstring'''

           if token_ids_a is None:
                  return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]





    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__							=   os.path.join(
               UpperCAmelCase__ ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

           return (out_vocab_file,)


