


'''simple docstring'''






import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_   :      List[Any]				    =      1_6
a_   :      str				    =      3_2






def 		_A			(lowerCAmelCase__					:Accelerator			,  lowerCAmelCase__					:int = 16			,  lowerCAmelCase__					:str = "bert-base-cased"						)				->					Dict:




     '''simple docstring'''



     _a      =							AutoTokenizer.from_pretrained(lowerCAmelCase__						)
     _a      =							load_dataset('glue'			,  'mrpc'						)

     def tokenize_function(lowerCAmelCase__					:int						):
          # max_length=None => use the model max length (it's actually the default)
          _a      =							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
     _a      =							datasets.map(
         lowerCAmelCase__			,  batched=lowerCAmelCase__			,  remove_columns=['idx', 'sentence1', 'sentence2']			,  load_from_cache_file=lowerCAmelCase__						)

     # 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(lowerCAmelCase__					:Dict						):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(lowerCAmelCase__			,  padding='max_length'			,  max_length=1_28			,  return_tensors='pt'						)
          return tokenizer.pad(lowerCAmelCase__			,  padding='longest'			,  return_tensors='pt'						)

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

     return train_dataloader, eval_dataloader






def 		_A			(lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Tuple						)				->					Dict:




     '''simple docstring'''



     model.eval()
     _a      =							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      =							model(**lowerCAmelCase__						)
          _a      =							outputs.logits.argmax(dim=-1						)
          # It is slightly faster to call this once, than multiple times
          _a ,				_a      =							accelerator.gather(
              (predictions, batch['labels'])						)  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(lowerCAmelCase__						) - 1:
                    _a      =							predictions[: len(eval_dataloader.dataset						) - samples_seen]
                    _a      =							references[: len(eval_dataloader.dataset						) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=lowerCAmelCase__			,  references=lowerCAmelCase__			,  )

     _a      =							metric.compute()
     return eval_metric["accuracy"]






def 		_A			(lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:List[Any]						)				->					List[Any]:




     '''simple docstring'''



     _a      =							Accelerator()

     # 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']						)
     _a      =							args.model_name_or_path

     set_seed(lowerCAmelCase__						)
     _a ,				_a      =							get_dataloaders(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _a      =							AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__			,  return_dict=lowerCAmelCase__						)

     # Instantiate optimizer
     _a      =							(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     _a      =							optimizer_cls(params=model.parameters()			,  lr=lowerCAmelCase__						)

     if accelerator.state.deepspeed_plugin is not None:
          _a      =							accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          _a      =							1
     _a      =							(len(lowerCAmelCase__						) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          _a      =							get_linear_schedule_with_warmup(
              optimizer=lowerCAmelCase__			,  num_warmup_steps=0			,  num_training_steps=lowerCAmelCase__			,  )
     else:
          _a      =							DummyScheduler(lowerCAmelCase__			,  total_num_steps=lowerCAmelCase__			,  warmup_num_steps=0						)

     # 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(
         lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)

     # 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
     _a      =							evaluate.load('glue'			,  'mrpc'						)
     _a      =							num_epochs

     if args.partial_train_epoch is not None:
          _a      =							args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint						)
          _a      =							args.resume_from_checkpoint.split('epoch_'						)[1]
          _a      =							''
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          _a      =							int(lowerCAmelCase__						) + 1
          _a      =							evaluation_loop(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
          accelerator.print('resumed checkpoint performance:'			,  lowerCAmelCase__						)
          accelerator.print('resumed checkpoint\'s scheduler\'s lr:'			,  lr_scheduler.get_lr()[0]						)
          accelerator.print('resumed optimizers\'s lr:'			,  optimizer.param_groups[0]['lr']						)
          with open(os.path.join(args.output_dir			,  f'state_{starting_epoch-1}.json'						)			,  'r'						) as f:
               _a      =							json.load(lowerCAmelCase__						)
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

    # Now we train the model
     _a      =							{}
     for epoch in range(lowerCAmelCase__			,  lowerCAmelCase__						):
          model.train()
          for step, batch in enumerate(lowerCAmelCase__						):
               _a      =							model(**lowerCAmelCase__						)
               _a      =							outputs.loss
               _a      =							loss / gradient_accumulation_steps
               accelerator.backward(lowerCAmelCase__						)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          _a      =							f'epoch_{epoch}'
          _a      =							os.path.join(args.output_dir			,  lowerCAmelCase__						)
          accelerator.save_state(lowerCAmelCase__						)
          _a      =							evaluation_loop(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
          _a      =							accuracy
          _a      =							lr_scheduler.get_lr()[0]
          _a      =							optimizer.param_groups[0]['lr']
          _a      =							epoch
          _a      =							overall_step
          accelerator.print(f'epoch {epoch}:'			,  lowerCAmelCase__						)

          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               with open(os.path.join(args.output_dir			,  f'state_{epoch}.json'						)			,  'w'						) as f:
                    json.dump(lowerCAmelCase__			,  lowerCAmelCase__						)






def 		_A			()				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'						)
     parser.add_argument(
         '--model_name_or_path'			,  type=lowerCAmelCase__			,  default='bert-base-cased'			,  help='Path to pretrained model or model identifier from huggingface.co/models.'			,  required=lowerCAmelCase__			,  )
     parser.add_argument(
         '--output_dir'			,  type=lowerCAmelCase__			,  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=lowerCAmelCase__			,  default=lowerCAmelCase__			,  help='If the training should continue from a checkpoint folder.'			,  )
     parser.add_argument(
         '--partial_train_epoch'			,  type=lowerCAmelCase__			,  default=lowerCAmelCase__			,  help='If passed, the training will stop after this number of epochs.'			,  )
     parser.add_argument(
         '--num_epochs'			,  type=lowerCAmelCase__			,  default=2			,  help='Number of train epochs.'			,  )
     _a      =							parser.parse_args()
     _a      =							{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

     training_function(lowerCAmelCase__			,  lowerCAmelCase__						)


if __name__ == "__main__":
 main()





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a_   :      List[str]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			**__magic_name__  )   ->							Any:
      _a      =							feature_size
      _a      =							sampling_rate
      _a      =							padding_value

      _a      =							kwargs.pop('padding_side' ,			'right'  )
      _a      =							kwargs.pop('return_attention_mask' ,			__magic_name__  )

      super().__init__(**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = False ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(__magic_name__ ,			(list, tuple)  ) and isinstance(processed_features[0] ,			(dict, BatchFeature)  ):
           _a      =							{
               key: [example[key] for example in processed_features] for key in processed_features[0].keys()
           }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
           raise ValueError(
               'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
               f' to this method that includes {self.model_input_names[0]}, but you provided'
               f' {list(processed_features.keys()  )}'  )

      _a      =							processed_features[self.model_input_names[0]]
      _a      =							(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(__magic_name__  ) == 0:
           if return_attention_mask:
                _a      =							[]
           return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      _a      =							required_input[0]
      if isinstance(__magic_name__ ,			(list, tuple)  ):
           # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
           _a      =							0
           while len(required_input[index]  ) == 0:
                index += 1
           if index < len(__magic_name__  ):
                _a      =							required_input[index][0]

      if return_tensors is None:
           if is_tf_tensor(__magic_name__  ):
                _a      =							'tf'
           elif is_torch_tensor(__magic_name__  ):
                _a      =							'pt'
           elif isinstance(__magic_name__ ,			(int, float, list, tuple, np.ndarray)  ):
                _a      =							'np'
           else:
                raise ValueError(
                    f'type of {first_element} unknown: {type(__magic_name__  )}. '
                    'Should be one of a python, numpy, pytorch or tensorflow object.'  )

      for key, value in processed_features.items():
           if isinstance(value[0] ,			(int, float)  ):
                _a      =							to_numpy(__magic_name__  )
           else:
                _a      =							[to_numpy(__magic_name__  ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      _a      =							self._get_padding_strategies(padding=__magic_name__ ,			max_length=__magic_name__  )

      _a      =							processed_features[self.model_input_names[0]]

      _a      =							len(__magic_name__  )
      if not all(len(__magic_name__  ) == batch_size for v in processed_features.values()  ):
           raise ValueError('Some items in the output dictionary have a different batch size than others.'  )

      _a      =							[]
      for i in range(__magic_name__  ):
           _a      =							{k: v[i] for k, v in processed_features.items()}
           # truncation
           _a      =							self._truncate(
               __magic_name__ ,			max_length=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			truncation=__magic_name__ ,			)
           truncated_inputs.append(__magic_name__  )

      if padding_strategy == PaddingStrategy.LONGEST:
           # make sure that `max_length` cannot be longer than the longest truncated length
           _a      =							max(len(input_slice[self.model_input_names[0]]  ) for input_slice in truncated_inputs  )
           _a      =							PaddingStrategy.MAX_LENGTH

      _a      =							{}
      for i in range(__magic_name__  ):
           # padding
           _a      =							self._pad(
               truncated_inputs[i] ,			max_length=__magic_name__ ,			padding_strategy=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=__magic_name__ ,			)

           for key, value in outputs.items():
                if key not in batch_outputs:
                     _a      =							[]
                if value.dtype is np.dtype(np.floataa  ):
                     _a      =							value.astype(np.floataa  )
                batch_outputs[key].append(__magic_name__  )

      return BatchFeature(__magic_name__ ,			tensor_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = PaddingStrategy.DO_NOT_PAD ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							dict:
      _a      =							processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
           _a      =							len(__magic_name__  )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           _a      =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _a      =							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__magic_name__  ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
           _a      =							np.ones(len(__magic_name__  ) ,			dtype=np.intaa  )

      if needs_to_be_padded:
           _a      =							max_length - len(__magic_name__  )
           if self.padding_side == "right":
                if return_attention_mask:
                     _a      =							np.pad(
                         processed_features['attention_mask'] ,			(0, difference)  )
                _a      =							((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                _a      =							np.pad(
                    __magic_name__ ,			__magic_name__ ,			'constant' ,			constant_values=self.padding_value  )
           elif self.padding_side == "left":
                if return_attention_mask:
                     _a      =							np.pad(
                         processed_features['attention_mask'] ,			(difference, 0)  )
                _a      =							((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                _a      =							np.pad(
                    __magic_name__ ,			__magic_name__ ,			'constant' ,			constant_values=self.padding_value  )
           else:
                raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

      return processed_features
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							List[str]:
      if not truncation:
           return processed_features
      elif truncation and max_length is None:
           raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'  )

      _a      =							processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           _a      =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _a      =							len(__magic_name__  ) > max_length

      if needs_to_be_truncated:
           _a      =							processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
                _a      =							processed_features['attention_mask'][:max_length]

      return processed_features




 def 							__UpperCAmelCase						(	self ,			__magic_name__=False ,			__magic_name__=None  )   ->							Dict:

      # Get padding strategy
      if padding is not False:
           if padding is True:
                _a      =							PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(__magic_name__ ,			__magic_name__  ):
                _a      =							PaddingStrategy(__magic_name__  )
           elif isinstance(__magic_name__ ,			__magic_name__  ):
                _a      =							padding
      else:
           _a      =							PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
           if padding_strategy == PaddingStrategy.MAX_LENGTH:
                raise ValueError(
                    f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'  )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
           raise ValueError(
               'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
               ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'  )

      return padding_strategy

