
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
#
########################################################################


UpperCAmelCase_   :				Dict						      = 16
UpperCAmelCase_   :				str						      = 32







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    Dict = 16       )			->	Dict:

       """simple docstring"""

       a_      :	List[str]							=						AutoTokenizer.from_pretrained('bert-base-cased'       )
       a_      :	Tuple							=						load_dataset('glue'						,					'mrpc'       )

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

       def collate_fn(__A				:    Tuple       ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              a_      :	Optional[Any]							=						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":
                     a_      :	Union[str, Any]							=						16
              elif accelerator.mixed_precision != "no":
                     a_      :	List[Any]							=						8
              else:
                     a_      :	List[Any]							=						None

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

       # Instantiate dataloaders.
       a_      :	Tuple							=						DataLoader(
           tokenized_datasets['train']						,					shuffle=__lowerCAmelCase						,					collate_fn=__lowerCAmelCase						,					batch_size=__lowerCAmelCase       )
       a_      :	Optional[Any]							=						DataLoader(
           tokenized_datasets['validation']						,					shuffle=__lowerCAmelCase						,					collate_fn=__lowerCAmelCase						,					batch_size=__lowerCAmelCase       )

       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

   UpperCAmelCase_   :				List[str]						      = mocked_dataloaders  # noqa: F811







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    int       )			->	Dict:

       """simple docstring"""

       if os.environ.get('TESTING_MOCKED_DATALOADERS'						,					__lowerCAmelCase       ) == "1":
              a_      :	Optional[Any]							=						2
       # Initialize accelerator
       a_      :	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[int]							=						config['lr']
       a_      :	Optional[int]							=						int(config['num_epochs']       )
       a_      :	Dict							=						int(config['seed']       )
       a_      :	Tuple							=						int(config['batch_size']       )

       a_      :	str							=						evaluate.load('glue'						,					'mrpc'       )

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

       set_seed(__lowerCAmelCase       )
       a_							,	a_      :	List[str]							=						get_dataloaders(__lowerCAmelCase						,					__lowerCAmelCase       )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a_      :	str							=						AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,					return_dict=__lowerCAmelCase       )

       # 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_      :	List[str]							=						model.to(accelerator.device       )

       # Instantiate optimizer
       a_      :	List[Any]							=						AdamW(params=model.parameters()						,					lr=__lowerCAmelCase       )

       # Instantiate scheduler
       a_      :	Dict							=						get_linear_schedule_with_warmup(
           optimizer=__lowerCAmelCase						,					num_warmup_steps=1_00						,					num_training_steps=(len(__lowerCAmelCase       ) * 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_      :	Tuple							=						accelerator.prepare(
           __lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase       )

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

              model.eval()
              a_      :	Any							=						0
              for step, batch in enumerate(__lowerCAmelCase       ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device       )
                     with torch.no_grad():
                            a_      :	Union[str, Any]							=						model(**__lowerCAmelCase       )
                     a_      :	Optional[Any]							=						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(__lowerCAmelCase       ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   a_      :	Optional[Any]							=						predictions[: len(eval_dataloader.dataset       ) - samples_seen]
                                   a_      :	Tuple							=						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=__lowerCAmelCase						,					references=__lowerCAmelCase						,					)

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







def  SCREAMING_SNAKE_CASE_		(    )			->	Any:

       """simple docstring"""

       a_      :	Any							=						argparse.ArgumentParser(description='Simple example of training script.'       )
       parser.add_argument(
           '--mixed_precision'						,					type=__lowerCAmelCase						,					default=__lowerCAmelCase						,					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_      :	int							=						parser.parse_args()
       a_      :	Any							=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(__lowerCAmelCase						,					__lowerCAmelCase       )


if __name__ == "__main__":
   main()






from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_   :				Dict						      = logging.get_logger(__name__)

UpperCAmelCase_   :				int						      = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Union[str, Any]       =		'''autoformer'''
      snake_case__       :  Any       =		{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
          '''num_hidden_layers''': '''encoder_layers''',
      }
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : str = "student_t"  ,							SCREAMING_SNAKE_CASE__       : str = "nll"  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : int = 6_4  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : str = "gelu"  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 0.02  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : int = 1_0  ,							SCREAMING_SNAKE_CASE__       : int = 2_5  ,							SCREAMING_SNAKE_CASE__       : int = 3  ,							**SCREAMING_SNAKE_CASE__       : int  ,							)	->    Union[str, Any]:
             # time series specific configuration
             a_      :	Optional[int]							=						prediction_length
             a_      :	Tuple							=						context_length if context_length is not None else prediction_length
             a_      :	Tuple							=						distribution_output
             a_      :	int							=						loss
             a_      :	Tuple							=						input_size
             a_      :	int							=						num_time_features
             a_      :	Dict							=						lags_sequence
             a_      :	List[str]							=						scaling
             a_      :	Tuple							=						num_dynamic_real_features
             a_      :	Dict							=						num_static_real_features
             a_      :	Dict							=						num_static_categorical_features
             if cardinality is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The cardinality should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Any							=						cardinality
             else:
                    a_      :	Tuple							=						[0]
             if embedding_dimension is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The embedding dimension should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Tuple							=						embedding_dimension
             else:
                    a_      :	Optional[Any]							=						[min(5_0  ,							(cat + 1) // 2  ) for cat in self.cardinality]
             a_      :	Optional[Any]							=						num_parallel_samples

             # Transformer architecture configuration
             a_      :	Dict							=						input_size * len(self.lags_sequence  ) + self._number_of_features
             a_      :	Tuple							=						d_model
             a_      :	int							=						encoder_attention_heads
             a_      :	Optional[int]							=						decoder_attention_heads
             a_      :	str							=						encoder_ffn_dim
             a_      :	List[str]							=						decoder_ffn_dim
             a_      :	Any							=						encoder_layers
             a_      :	Any							=						decoder_layers

             a_      :	List[Any]							=						dropout
             a_      :	Tuple							=						attention_dropout
             a_      :	Union[str, Any]							=						activation_dropout
             a_      :	List[Any]							=						encoder_layerdrop
             a_      :	List[str]							=						decoder_layerdrop

             a_      :	Optional[Any]							=						activation_function
             a_      :	Union[str, Any]							=						init_std

             a_      :	List[str]							=						use_cache

             # Autoformer
             a_      :	Tuple							=						label_length
             a_      :	List[Any]							=						moving_average
             a_      :	Optional[Any]							=						autocorrelation_factor

             super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    int:
             return (
                 sum(self.embedding_dimension  )
                 + self.num_dynamic_real_features
                 + self.num_time_features
                 + self.num_static_real_features
                 + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
             )


