


'''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,
# specifically showcasing the checkpointing capability,
# 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_    	=							1_6
lowerCAmelCase_    	=							3_2







def 	_A			(   UpperCAmelCase			,UpperCAmelCase = 16	):






   '''simple docstring'''







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

   def tokenize_function(UpperCAmelCase	):
      # max_length=None => use the model max length (it's actually the default)
      A__         =    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__         =    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__         =    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__         =    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(
          UpperCAmelCase			,padding='longest'			,max_length=UpperCAmelCase			,pad_to_multiple_of=UpperCAmelCase			,return_tensors='pt'			,)

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






   '''simple docstring'''







   if os.environ.get('TESTING_MOCKED_DATALOADERS'			,UpperCAmelCase	) == "1":
      A__         =    2
   # Initialize accelerator
   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']	)

   # New Code #
   # Parse out whether we are saving every epoch or after a certain number of batches
   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

   set_seed(UpperCAmelCase	)

   A__     , A__         =    get_dataloaders(UpperCAmelCase			,UpperCAmelCase	)
   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

   # 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=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__         =    model.to(accelerator.device	)

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

   # Instantiate scheduler
   A__         =    get_linear_schedule_with_warmup(
       optimizer=UpperCAmelCase			,num_warmup_steps=100			,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__         =    accelerator.prepare(
       UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	)

   # New Code #
   # 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

   # We need to load the checkpoint back in before training here with `load_state`
   # The total number of epochs is adjusted based on where the state is being loaded from,
   # as we assume continuation of the same training script
   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(UpperCAmelCase	)[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(UpperCAmelCase	)
         resume_step -= starting_epoch * len(UpperCAmelCase	)

    # Now we train the model
   for epoch in range(UpperCAmelCase			,UpperCAmelCase	):
      model.train()
      # New Code #
      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(UpperCAmelCase			,UpperCAmelCase	)
         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(UpperCAmelCase	):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device	)
         A__         =    model(**UpperCAmelCase	)
         A__         =    outputs.loss
         A__         =    loss / gradient_accumulation_steps
         accelerator.backward(UpperCAmelCase	)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
         # New Code #
         overall_step += 1

         # New Code #
         # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
         # These are saved to folders named `step_{overall_step}`
         # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
         # If mixed precision was used, will also save a "scalar.bin" file
         if isinstance(UpperCAmelCase			,UpperCAmelCase	):
            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			,UpperCAmelCase	)
               accelerator.save_state(UpperCAmelCase	)

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

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

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `epoch_{epoch}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if checkpointing_steps == "epoch":
         A__         =    F"""epoch_{epoch}"""
         if args.output_dir is not None:
            A__         =    os.path.join(args.output_dir			,UpperCAmelCase	)
         accelerator.save_state(UpperCAmelCase	)







def 	_A			(   ):






   '''simple docstring'''







   A__         =    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.'	)
   parser.add_argument(
       '--checkpointing_steps'			,type=UpperCAmelCase			,default=UpperCAmelCase			,help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'			,)
   parser.add_argument(
       '--output_dir'			,type=UpperCAmelCase			,default='.'			,help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,)
   parser.add_argument(
       '--resume_from_checkpoint'			,type=UpperCAmelCase			,default=UpperCAmelCase			,help='If the training should continue from a checkpoint folder.'			,)
   A__         =    parser.parse_args()
   A__         =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(UpperCAmelCase			,UpperCAmelCase	)


if __name__ == "__main__":
    main()








'''simple docstring'''







from typing import Any

import numpy as np

class   _snake_case:






 def __init__(self					:     int						,		a					:     np.ndarray						,		a					:     int						)					->      None:






    """simple docstring"""


    if np.any(tableau[:, -1]						,		where=tableau[:, -1] < 0						):
       raise ValueError('RHS must be > 0'						)

    A__         =    tableau
    A__     , A__         =    tableau.shape

    # Number of decision variables x1, x2, x3...
    A__         =    n_vars

    # Number of artificial variables to be minimised
    A__         =    len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    A__         =    (self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    A__         =    self.n_rows - self.n_stages

    # Objectives for each stage
    A__         =    ['max']

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
       self.objectives.append('min'						)

    A__         =    ['']

    # Index of current pivot row and column
    A__         =    None
    A__         =    None

    # Does objective row only contain (non)-negative values?
    A__         =    False






 @staticmethod
 def 	_UpperCamelCase   (*a					:     int						)					->      list[str]:






    """simple docstring"""


    if len(a						) != 3:
       raise ValueError('Must provide n_vars, n_slack, and n_art_vars'						)

    if not all(x >= 0 and isinstance(a						,		a						) for x in args						):
       raise ValueError('All arguments must be non-negative integers'						)

    # decision | slack | artificial
    A__         =    ['x', 's', 'a']
    A__         =    []
    for i in range(3						):
       for j in range(args[i]						):
          titles.append(string_starts[i] + str(j + 1						)						)
    titles.append('RHS'						)
    return titles






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						)					->      tuple[Any, Any]:






    """simple docstring"""


    A__         =    self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    A__         =    (objective == 'min') - (objective == 'max')
    A__         =    np.argmax(sign * tableau[0, : self.n_vars]						)

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
       A__         =    True
       return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    A__         =    slice(self.n_stages						,		self.n_rows						)

    # RHS
    A__         =    tableau[s, -1]

    # Elements of pivot column within slice
    A__         =    tableau[s, col_idx]

    # Array filled with nans
    A__         =    np.full(self.n_rows - self.n_stages						,		np.nan						)

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    A__         =    np.divide(a						,		a						,		out=a						,		where=divisor > 0						)

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    A__         =    np.nanargmin(a						) + self.n_stages
    return row_idx, col_idx






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						,		a					:     int						,		a					:     int						)					->      np.ndarray:






    """simple docstring"""


    A__         =    tableau[row_idx].copy()

    A__         =    piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx]						):
       tableau[idx] += -coeff * piv_row
    A__         =    piv_row
    return tableau






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     np.ndarray						)					->      np.ndarray:






    """simple docstring"""


    self.objectives.pop()

    if not self.objectives:
       return tableau

    # Slice containing ids for artificial columns
    A__         =    slice(-self.n_art_vars - 1						,		-1						)

    # Delete the artificial variable columns
    A__         =    np.delete(a						,		a						,		axis=1						)

    # Delete the objective row of the first stage
    A__         =    np.delete(a						,		0						,		axis=0						)

    A__         =    1
    self.n_rows -= 1
    A__         =    0
    A__         =    False
    return tableau






 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      dict[Any, Any]:






    """simple docstring"""


    for _ in range(1_00						):
       # Completion of each stage removes an objective. If both stages
       # are complete, then no objectives are left
       if not self.objectives:
          A__         =    self.generate_col_titles(
              self.n_vars						,		self.n_slack						,		self.n_art_vars						)

          # Find the values of each variable at optimal solution
          return self.interpret_tableau(self.tableau						,		self.col_titles						)

       A__     , A__         =    self.find_pivot(self.tableau						)

       # If there are no more negative values in objective row
       if self.stop_iter:
          # Delete artificial variable columns and rows. Update attributes
          A__         =    self.change_stage(self.tableau						)
       else:
          A__         =    self.pivot(self.tableau						,		a						,		a						)
    return {}






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						,		a					:     list[str]						)					->      dict[str, float]:






    """simple docstring"""


    A__         =    {'P': abs(tableau[0, -1]						)}

    for i in range(self.n_vars						):
       # Gives ids of nonzero entries in the ith column
       A__         =    np.nonzero(tableau[:, i]						)
       A__         =    len(nonzero[0]						)

       # First entry in the nonzero ids
       A__         =    nonzero[0][0]
       A__         =    tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
          A__         =    tableau[nonzero_rowidx, -1]
          A__         =    rhs_val

        # Check for basic variables
    for title in col_titles:
       # Don't add RHS or slack variables to output dict
       if title[0] not in "R-s-a":
          output_dict.setdefault(a						,		0						)
    return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()


