"""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 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
#
########################################################################


__lowercase		:		Optional[int]														=							16
__lowercase		:		Union[str, Any]														=							32





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = 16):
     __snake_case				=							AutoTokenizer.from_pretrained('''bert-base-cased''')
     __snake_case				=							load_dataset('''glue''',      '''mrpc''')

     def tokenize_function(snake_case):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case				=							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():
          __snake_case				=							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
     __snake_case				=							tokenized_datasets.rename_column('''label''',      '''labels''')

     def collate_fn(snake_case):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __snake_case				=							1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __snake_case				=							16
          elif accelerator.mixed_precision != "no":
               __snake_case				=							8
          else:
               __snake_case				=							None

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

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

     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

       __lowercase		:		str														=							mocked_dataloaders  # noqa: F811





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):



     if os.environ.get('''TESTING_MOCKED_DATALOADERS''',      A__) == "1":
          __snake_case				=							2
     # Initialize accelerator
     __snake_case				=							Accelerator(cpu=args.cpu,      mixed_precision=args.mixed_precision)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __snake_case				=							config['''lr''']
     __snake_case				=							int(config['''num_epochs'''])
     __snake_case				=							int(config['''seed'''])
     __snake_case				=							int(config['''batch_size'''])

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

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

     set_seed(A__)
     __snake_case		,	__snake_case				=							get_dataloaders(A__,      A__)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __snake_case				=							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).
     __snake_case				=							model.to(accelerator.device)

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

     # Instantiate scheduler
     __snake_case				=							get_linear_schedule_with_warmup(
         optimizer=A__,      num_warmup_steps=1_00,      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.
     __snake_case		,	__snake_case		,	__snake_case		,	__snake_case		,	__snake_case				=							accelerator.prepare(
         A__,      A__,      A__,      A__,      A__)

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

          model.eval()
          __snake_case				=							0
          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():
                    __snake_case				=							model(**A__)
               __snake_case				=							outputs.logits.argmax(dim=-1)
               __snake_case		,	__snake_case				=							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(A__) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         __snake_case				=							predictions[: len(eval_dataloader.dataset) - samples_seen]
                         __snake_case				=							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=A__,      references=A__,      )

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





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							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.''')
     __snake_case				=							parser.parse_args()
     __snake_case				=							{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(A__,      A__)


if __name__ == "__main__":
       main()
"""simple docstring"""



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}