






'''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
#
########################################################################


__A   :							str     	=						1_6
__A   :							str     	=						3_2


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      # 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(snake_case__				)[-1].split("."				)[0]
            accelerator.init_trackers(snake_case__				,      snake_case__				)

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

      def tokenize_function(snake_case__				):
            # max_length=None => use the model max length (it's actually the default)
            __A		 =   tokenizer(examples["sentence1"]				,      examples["sentence2"]				,      truncation=snake_case__				,      max_length=snake_case__				)
            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(
                snake_case__				,      batched=snake_case__				,      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(snake_case__				):
            # 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(
                snake_case__				,      padding="longest"				,      max_length=snake_case__				,      pad_to_multiple_of=snake_case__				,      return_tensors="pt"				,      )

      # Instantiate dataloaders.
      __A		 =   DataLoader(
          tokenized_datasets["train"]				,      shuffle=snake_case__				,      collate_fn=snake_case__				,      batch_size=snake_case__				)
      __A		 =   DataLoader(
          tokenized_datasets["validation"]				,      shuffle=snake_case__				,      collate_fn=snake_case__				,      batch_size=snake_case__				)

      set_seed(snake_case__				)

      # 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=snake_case__				)

      # 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=snake_case__				)

      # Instantiate scheduler
      __A		 =   get_linear_schedule_with_warmup(
          optimizer=snake_case__				,      num_warmup_steps=100				,      num_training_steps=(len(snake_case__				) * 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(
          snake_case__				,      snake_case__				,      snake_case__				,      snake_case__				,      snake_case__				)

      # 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(snake_case__				)[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(snake_case__				)
                  resume_step -= starting_epoch * len(snake_case__				)

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

                  overall_step += 1

                  if isinstance(snake_case__				,      snake_case__				):
                        __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				,      snake_case__				)
                              accelerator.save_state(snake_case__				)

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

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

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

      if args.with_tracking:
            accelerator.end_training()


def     _UpperCamelCase				(  ):
      __A		 =   argparse.ArgumentParser(description="Simple example of training script."				)
      parser.add_argument(
          "--mixed_precision"				,      type=snake_case__				,      default=snake_case__				,      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=snake_case__				,      default=snake_case__				,      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=snake_case__				,      default=snake_case__				,      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=snake_case__				,      default="."				,      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,      )
      parser.add_argument(
          "--project_dir"				,      type=snake_case__				,      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(snake_case__				,      snake_case__				)


if __name__ == "__main__":
       main()










'''simple docstring'''







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class        a__     (      TensorFormatter[Mapping, np.ndarray, Mapping]    ):
     '''simple docstring'''







     def __init__(self     :						Tuple					,					lowercase__     :						Dict=None					,					**lowercase__     :						List[Any]       )	->							str:
           super().__init__(features=lowercase__       )
           __A		 =   np_array_kwargs






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						Tuple       )	->							List[Any]:
           if isinstance(lowercase__					,					lowercase__       ):
                 if column and all(
                     isinstance(lowercase__					,					np.ndarray       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
                       return np.stack(lowercase__       )
                 else:
                       # don't use np.array(column, dtype=object)
                       # since it fails in certain cases
                       # see https://stackoverflow.com/q/51005699
                       __A		 =   np.empty(len(lowercase__       )					,					dtype=lowercase__       )
                       __A		 =   column
                       return out
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						Optional[int]       )	->							Tuple:
           if isinstance(lowercase__					,					(str, bytes, type(lowercase__       ))       ):
                 return value
           elif isinstance(lowercase__					,					(np.character, np.ndarray)       ) and np.issubdtype(value.dtype					,					np.character       ):
                 return value
           elif isinstance(lowercase__					,					np.number       ):
                 return value

           __A		 =   {}

           if isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.integer       ):
                 __A		 =   {"dtype": np.intaa}
           elif isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.floating       ):
                 __A		 =   {"dtype": np.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(lowercase__					,					PIL.Image.Image       ):
                       return np.asarray(lowercase__					,					**self.np_array_kwargs       )

           return np.asarray(lowercase__					,					**{**default_dtype, **self.np_array_kwargs}       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						str       )	->							Optional[Any]:
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(lowercase__					,					torch.Tensor       ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
           if hasattr(lowercase__					,					"__array__"       ) and not isinstance(lowercase__					,					(np.ndarray, np.character, np.number)       ):
                 __A		 =   data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(lowercase__					,					np.ndarray       ):
                 if data_struct.dtype == object:
                       return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           if isinstance(lowercase__					,					(list, tuple)       ):
                 return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           return self._tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						dict       )	->							Dict:
           return map_nested(self._recursive_tensorize					,					lowercase__					,					map_list=lowercase__       )






     def      _UpperCamelCase (self     :						Optional[int]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_row(lowercase__       )
           __A		 =   self.python_features_decoder.decode_row(lowercase__       )
           return self.recursive_tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						pa.Table       )	->							np.ndarray:
           __A		 =   self.numpy_arrow_extractor().extract_column(lowercase__       )
           __A		 =   self.python_features_decoder.decode_column(lowercase__					,					pa_table.column_names[0]       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           __A		 =   self._consolidate(lowercase__       )
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_batch(lowercase__       )
           __A		 =   self.python_features_decoder.decode_batch(lowercase__       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           for column_name in batch:
                 __A		 =   self._consolidate(batch[column_name]       )
           return batch


