






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


__magic_name__   :  Any        =							16
__magic_name__   :  Dict        =							32
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase = 16       ,							_UpperCamelCase = "bert-base-cased") ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  AutoTokenizer.from_pretrained(_UpperCamelCase)
      UpperCamelCase					       =  load_dataset('glue'       ,							'mrpc')

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

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      UpperCamelCase					       =  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.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(_UpperCamelCase       ,							padding='max_length'       ,							max_length=1_28       ,							return_tensors='pt')
            return tokenizer.pad(_UpperCamelCase       ,							padding='longest'       ,							return_tensors='pt')

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

      return train_dataloader, eval_dataloader
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			str:
      """simple docstring"""

      UpperCamelCase					       =  Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase					       =  config["lr"]
      UpperCamelCase					       =  int(config['num_epochs'])
      UpperCamelCase					       =  int(config['seed'])
      UpperCamelCase					       =  int(config['batch_size'])
      UpperCamelCase					       =  args.model_name_or_path

      set_seed(_UpperCamelCase)
      UpperCamelCase					       =  get_dataloaders(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

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

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

      if accelerator.state.deepspeed_plugin is not None:
            UpperCamelCase					       =  accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            UpperCamelCase					       =  1
      UpperCamelCase					       =  (len(_UpperCamelCase) * num_epochs) // gradient_accumulation_steps

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

      # We need to keep track of how many total steps we have iterated over
      UpperCamelCase					       =  0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCamelCase					       =  0

      # Now we train the model
      UpperCamelCase					       =  evaluate.load('glue'       ,							'mrpc')
      UpperCamelCase					       =  0
      UpperCamelCase					       =  {}
      for epoch in range(_UpperCamelCase       ,							_UpperCamelCase):
            model.train()
            for step, batch in enumerate(_UpperCamelCase):
                  UpperCamelCase					       =  model(**_UpperCamelCase)
                  UpperCamelCase					       =  outputs.loss
                  UpperCamelCase					       =  loss / gradient_accumulation_steps
                  accelerator.backward(_UpperCamelCase)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

            model.eval()
            UpperCamelCase					       =  0
            for step, batch in enumerate(_UpperCamelCase):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device)
                  with torch.no_grad():
                        UpperCamelCase					       =  model(**_UpperCamelCase)
                  UpperCamelCase					       =  outputs.logits.argmax(dim=-1)
                  # It is slightly faster to call this once, than multiple times
                  UpperCamelCase					       =  accelerator.gather(
                      (predictions, batch['labels']))  # If we are in a multiprocess environment, the last batch has duplicates
                  if accelerator.use_distributed:
                        if step == len(_UpperCamelCase) - 1:
                              UpperCamelCase					       =  predictions[: len(eval_dataloader.dataset) - samples_seen]
                              UpperCamelCase					       =  references[: len(eval_dataloader.dataset) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=_UpperCamelCase       ,							references=_UpperCamelCase       ,							)

            UpperCamelCase					       =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'       ,							_UpperCamelCase)
            UpperCamelCase					       =  eval_metric["accuracy"]

            if best_performance < eval_metric["accuracy"]:
                  UpperCamelCase					       =  eval_metric["accuracy"]

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir       ,							'all_results.json')       ,							'w') as f:
                  json.dump(_UpperCamelCase       ,							_UpperCamelCase)
def 		lowercase__  (				) ->			Union[str, Any]:
      """simple docstring"""

      UpperCamelCase					       =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.')
      parser.add_argument(
          '--model_name_or_path'       ,							type=_UpperCamelCase       ,							default='bert-base-cased'       ,							help='Path to pretrained model or model identifier from huggingface.co/models.'       ,							required=_UpperCamelCase       ,							)
      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(
          '--performance_lower_bound'       ,							type=_UpperCamelCase       ,							default=_UpperCamelCase       ,							help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'       ,							)
      parser.add_argument(
          '--num_epochs'       ,							type=_UpperCamelCase       ,							default=3       ,							help='Number of train epochs.'       ,							)
      UpperCamelCase					       =  parser.parse_args()
      UpperCamelCase					       =  {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(_UpperCamelCase       ,							_UpperCamelCase)


if __name__ == "__main__":
       main()






from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=0						):
						UpperCAmelCase__:	Any									= 1.0 if scale is None else scale
						UpperCAmelCase__:	int									= 0.0 if loc is None else loc

						super().__init__(lowerCamelCase__				,      [AffineTransform(loc=self.loc				,      scale=self.scale				,      event_dim=lowerCamelCase__						)]						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self.base_dist.mean * self.scale + self.loc





					@property
					def    _UpperCAmelCase    (				self						):
						return self.base_dist.variance * self.scale**2





					@property
					def    _UpperCAmelCase    (				self						):
						return self.variance.sqrt()

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						super().__init__(**lowerCamelCase__						)
						UpperCAmelCase__:	Any									= args_dim
						UpperCAmelCase__:	List[str]									= nn.ModuleList([nn.Linear(lowerCamelCase__				,      lowerCamelCase__						) for dim in args_dim.values()]						)
						UpperCAmelCase__:	Any									= domain_map





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= [proj(lowerCamelCase__						) for proj in self.proj]

						return self.domain_map(*lowerCamelCase__						)

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						super().__init__()
						UpperCAmelCase__:	str									= function





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      *lowerCamelCase__						):
						return self.function(lowerCamelCase__				,      *lowerCamelCase__						)

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   42
					__magic_name__        =   42
					__magic_name__        =   42





					def __init__(				self				,      lowerCamelCase__ = 1						):
						UpperCAmelCase__:	str									= dim
						UpperCAmelCase__:	Any									= {k: dim * self.args_dim[k] for k in self.args_dim}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						if self.dim == 1:
							return self.distribution_class(*lowerCamelCase__						)
						else:
							return Independent(self.distribution_class(*lowerCamelCase__						)				,      1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      ):
						UpperCAmelCase__:	Optional[Any]									= self._base_distribution(lowerCamelCase__						)
						if loc is None and scale is None:
							return distr
						else:
							return AffineTransformed(lowerCamelCase__				,      loc=lowerCamelCase__				,      scale=lowerCamelCase__				,      event_dim=self.event_dim						)





					@property
					def    _UpperCAmelCase    (				self						):
						return () if self.dim == 1 else (self.dim,)





					@property
					def    _UpperCAmelCase    (				self						):
						return len(self.event_shape						)





					@property
					def    _UpperCAmelCase    (				self						):
						return 0.0





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						return ParameterProjection(
						    in_features=lowerCamelCase__				,      args_dim=self.args_dim				,      domain_map=LambdaLayer(self.domain_map						)				,      )





					def    _UpperCAmelCase    (				self				,      *lowerCamelCase__						):
						raise NotImplementedError()





					@staticmethod
					def    _UpperCAmelCase    (				lowerCamelCase__						):
						return (x + torch.sqrt(torch.square(lowerCamelCase__						) + 4.0						)) / 2.0

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"df": 1, "loc": 1, "scale": 1}
					__magic_name__        =   StudentT





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= cls.squareplus(lowerCamelCase__						).clamp_min(torch.finfo(scale.dtype						).eps						)
						UpperCAmelCase__:	List[Any]									= 2.0 + cls.squareplus(lowerCamelCase__						)
						return df.squeeze(-1						), loc.squeeze(-1						), scale.squeeze(-1						)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"loc": 1, "scale": 1}
					__magic_name__        =   Normal





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= cls.squareplus(lowerCamelCase__						).clamp_min(torch.finfo(scale.dtype						).eps						)
						return loc.squeeze(-1						), scale.squeeze(-1						)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"total_count": 1, "logits": 1}
					__magic_name__        =   NegativeBinomial





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= cls.squareplus(lowerCamelCase__						)
						return total_count.squeeze(-1						), logits.squeeze(-1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__:	str									= distr_args
						if self.dim == 1:
							return self.distribution_class(total_count=lowerCamelCase__				,      logits=lowerCamelCase__						)
						else:
							return Independent(self.distribution_class(total_count=lowerCamelCase__				,      logits=lowerCamelCase__						)				,      1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None						):
						UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= distr_args

						if scale is not None:
							# See scaling property of Gamma.
							logits += scale.log()

						return self._base_distribution((total_count, logits)						)