


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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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        =     16
__lowerCamelCase        =     32



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase = 1_6 ):
     a_    :     Tuple										=    AutoTokenizer.from_pretrained("""bert-base-cased""" )
     a_    :     Union[str, Any]										=    load_dataset("""glue"""  ,  """mrpc""" )

     def tokenize_function(__UpperCamelCase ):
          # max_length=None => use the model max length (it's actually the default)
          a_    :     Dict										=    tokenizer(examples["""sentence1"""]  ,  examples["""sentence2"""]  ,  truncation=__UpperCamelCase  ,  max_length=__UpperCamelCase )
          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_    :     Any										=    datasets.map(
              __UpperCamelCase  ,  batched=__UpperCamelCase  ,  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_    :     Any										=    tokenized_datasets.rename_column("""label"""  ,  """labels""" )

     def collate_fn(__UpperCamelCase ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          a_    :     Union[str, Any]										=    1_2_8 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_    :     Optional[Any]										=    1_6
          elif accelerator.mixed_precision != "no":
               a_    :     Any										=    8
          else:
               a_    :     List[Any]										=    None

          return tokenizer.pad(
              __UpperCamelCase  ,  padding="""longest"""  ,  max_length=__UpperCamelCase  ,  pad_to_multiple_of=__UpperCamelCase  ,  return_tensors="""pt"""  ,  )

     # Instantiate dataloaders.
     a_    :     List[Any]										=    DataLoader(
         tokenized_datasets["""train"""]  ,  shuffle=__UpperCamelCase  ,  collate_fn=__UpperCamelCase  ,  batch_size=__UpperCamelCase )
     a_    :     Optional[int]										=    DataLoader(
         tokenized_datasets["""validation"""]  ,  shuffle=__UpperCamelCase  ,  collate_fn=__UpperCamelCase  ,  batch_size=__UpperCamelCase )

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       __lowerCamelCase        =     mocked_dataloaders  # noqa: F811



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     # For testing only
     if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,  __UpperCamelCase ) == "1":
          a_    :     Tuple										=    2
     # Initialize accelerator
     a_    :     Optional[Any]										=    Accelerator(cpu=args.cpu  ,  mixed_precision=args.mixed_precision )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     a_    :     Optional[Any]										=    config["""lr"""]
     a_    :     Optional[Any]										=    int(config["""num_epochs"""] )
     a_    :     int										=    int(config["""seed"""] )
     a_    :     Union[str, Any]										=    int(config["""batch_size"""] )

     a_    :     Optional[int]										=    evaluate.load("""glue"""  ,  """mrpc""" )

     # If the batch size is too big we use gradient accumulation
     a_    :     int										=    1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          a_    :     Any										=    batch_size // MAX_GPU_BATCH_SIZE
          a_    :     Optional[int]										=    MAX_GPU_BATCH_SIZE

     set_seed(__UpperCamelCase )
     a_			,			a_    :     int										=    get_dataloaders(__UpperCamelCase  ,  __UpperCamelCase )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     a_    :     Dict										=    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""  ,  return_dict=__UpperCamelCase )

     # 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_    :     int										=    model.to(accelerator.device )

     # Instantiate optimizer
     a_    :     Dict										=    AdamW(params=model.parameters()  ,  lr=__UpperCamelCase )

     # Instantiate scheduler
     a_    :     Tuple										=    get_linear_schedule_with_warmup(
         optimizer=__UpperCamelCase  ,  num_warmup_steps=1_0_0  ,  num_training_steps=(len(__UpperCamelCase ) * 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_    :     Optional[Any]										=    accelerator.prepare(
         __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )

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

          model.eval()
          a_    :     Union[str, Any]										=    0
          for step, batch in enumerate(__UpperCamelCase ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device )
               with torch.no_grad():
                    a_    :     Any										=    model(**__UpperCamelCase )
               a_    :     List[str]										=    outputs.logits.argmax(dim=-1 )
               a_			,			a_    :     Dict										=    accelerator.gather((predictions, batch["""labels"""]) )
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(__UpperCamelCase ) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         a_    :     str										=    predictions[: len(eval_dataloader.dataset ) - samples_seen]
                         a_    :     Union[str, Any]										=    references[: len(eval_dataloader.dataset ) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=__UpperCamelCase  ,  references=__UpperCamelCase  ,  )

          a_    :     Optional[int]										=    metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''  ,  __UpperCamelCase )



def  _a			(						):
     a_    :     List[str]										=    argparse.ArgumentParser(description="""Simple example of training script.""" )
     parser.add_argument(
         """--mixed_precision"""  ,  type=__UpperCamelCase  ,  default=__UpperCamelCase  ,  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_    :     List[Any]										=    parser.parse_args()
     a_    :     str										=    {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
     training_function(__UpperCamelCase  ,  __UpperCamelCase )


if __name__ == "__main__":
       main()







import numpy as np

__lowerCamelCase        =     [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]


class   a__	:

       def __init__(   self      :     Union[str, Any]    ):
            a_    :     List[Any]										=    np.array(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str    ):
            a_			,			a_    :     Optional[int]										=    np.where(letter == self.SQUARE    )
            a_    :     Union[str, Any]										=    np.concatenate([indexa + 1, indexa + 1]    )
            return indexes

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     str										=    self.SQUARE[indexa - 1, indexa - 1]
            return letter

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     str    ):
            a_    :     List[str]										=    message.lower()
            a_    :     str										=    message.replace(""" """      ,							""""""    )
            a_    :     Union[str, Any]										=    message.replace("""j"""      ,							"""i"""    )

            a_    :     Optional[Any]										=    np.empty((2, len(lowerCamelCase_    ))    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     int										=    self.letter_to_numbers(message[letter_index]    )

                 a_    :     str										=    numbers[0]
                 a_    :     int										=    numbers[1]

            a_    :     int										=    first_step.reshape(2 * len(lowerCamelCase_    )    )
            a_    :     Optional[Any]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Optional[Any]										=    int(second_step[numbers_index * 2]    )
                 a_    :     Optional[Any]										=    int(second_step[(numbers_index * 2) + 1]    )
                 a_    :     Optional[Any]										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     List[str]										=    encoded_message + letter

            return encoded_message

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     str    ):
            a_    :     Optional[Any]										=    message.lower()
            message.replace(""" """      ,							""""""    )
            a_    :     int										=    np.empty(2 * len(lowerCamelCase_    )    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     str										=    self.letter_to_numbers(message[letter_index]    )
                 a_    :     Optional[int]										=    numbers[0]
                 a_    :     Optional[Any]										=    numbers[1]

            a_    :     Tuple										=    first_step.reshape((2, len(lowerCamelCase_    ))    )
            a_    :     Optional[int]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Dict										=    int(second_step[0, numbers_index]    )
                 a_    :     Tuple										=    int(second_step[1, numbers_index]    )
                 a_    :     Tuple										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    decoded_message + letter

            return decoded_message



