


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


UpperCAmelCase_					:   Any 							=						16
UpperCAmelCase_					:   Any 							=						32






def 							_lowerCAmelCase (  _a   :					Accelerator					,   _a   :					int = 16					,   _a   :					str = "bert-base-cased"			)	->			int:
     lowerCAmelCase_					:       int    =		AutoTokenizer.from_pretrained(snake_case__			)
     lowerCAmelCase_					:       int    =		load_dataset("""glue"""					,   """mrpc"""			)

     def tokenize_function(_a   :					Tuple			):
          # max_length=None => use the model max length (it's actually the default)
          lowerCAmelCase_					:       Union[str, Any]    =		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
     lowerCAmelCase_					:       Optional[int]    =		datasets.map(
         snake_case__					,   batched=snake_case__					,   remove_columns=["""idx""", """sentence1""", """sentence2"""]					,   load_from_cache_file=snake_case__			)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     lowerCAmelCase_					:       List[Any]    =		tokenized_datasets.rename_column("""label"""					,   """labels"""			)

     def collate_fn(_a   :					List[str]			):
          # 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(snake_case__					,   padding="""max_length"""					,   max_length=1_28					,   return_tensors="""pt"""			)
          return tokenizer.pad(snake_case__					,   padding="""longest"""					,   return_tensors="""pt"""			)

     # Instantiate dataloaders.
     lowerCAmelCase_					:       Optional[Any]    =		DataLoader(
         tokenized_datasets["""train"""]					,   shuffle=snake_case__					,   collate_fn=snake_case__					,   batch_size=snake_case__			)
     lowerCAmelCase_					:       int    =		DataLoader(
         tokenized_datasets["""validation"""]					,   shuffle=snake_case__					,   collate_fn=snake_case__					,   batch_size=snake_case__			)

     return train_dataloader, eval_dataloader






def 							_lowerCAmelCase (  _a   :					List[Any]					,   _a   :					Union[str, Any]			)	->			Union[str, Any]:
     # Initialize accelerator
     lowerCAmelCase_					:       List[Any]    =		Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase_					:       List[str]    =		config["""lr"""]
     lowerCAmelCase_					:       Optional[Any]    =		int(config["""num_epochs"""]			)
     lowerCAmelCase_					:       Optional[Any]    =		int(config["""seed"""]			)
     lowerCAmelCase_					:       str    =		int(config["""batch_size"""]			)
     lowerCAmelCase_					:       str    =		args.model_name_or_path

     set_seed(snake_case__			)
     lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		get_dataloaders(snake_case__					,   snake_case__					,   snake_case__			)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase_					:       Union[str, Any]    =		AutoModelForSequenceClassification.from_pretrained(snake_case__					,   return_dict=snake_case__			)

     # Instantiate optimizer
     lowerCAmelCase_					:       Any    =		(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowerCAmelCase_					:       Optional[int]    =		optimizer_cls(params=model.parameters()					,   lr=snake_case__			)

     if accelerator.state.deepspeed_plugin is not None:
          lowerCAmelCase_					:       Optional[Any]    =		accelerator.state.deepspeed_plugin.deepspeed_config[
              """gradient_accumulation_steps"""
          ]
     else:
          lowerCAmelCase_					:       int    =		1
     lowerCAmelCase_					:       List[Any]    =		(len(snake_case__			) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          lowerCAmelCase_					:       Dict    =		get_linear_schedule_with_warmup(
              optimizer=snake_case__					,   num_warmup_steps=0					,   num_training_steps=snake_case__					,   )
     else:
          lowerCAmelCase_					:       Any    =		DummyScheduler(snake_case__					,   total_num_steps=snake_case__					,   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.
     lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       List[Any]    =		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
     lowerCAmelCase_					:       str    =		0
     # We also need to keep track of the stating epoch so files are named properly
     lowerCAmelCase_					:       Optional[int]    =		0

     # Now we train the model
     lowerCAmelCase_					:       List[Any]    =		evaluate.load("""glue"""					,   """mrpc"""			)
     lowerCAmelCase_					:       Optional[int]    =		0
     lowerCAmelCase_					:       str    =		{}
     for epoch in range(snake_case__					,   snake_case__			):
          model.train()
          for step, batch in enumerate(snake_case__			):
               lowerCAmelCase_					:       str    =		model(**snake_case__			)
               lowerCAmelCase_					:       List[str]    =		outputs.loss
               lowerCAmelCase_					:       Dict    =		loss / gradient_accumulation_steps
               accelerator.backward(snake_case__			)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

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

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

          if best_performance < eval_metric["accuracy"]:
               lowerCAmelCase_					:       Tuple    =		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(snake_case__					,   snake_case__			)






def 							_lowerCAmelCase (  )	->			List[str]:
     lowerCAmelCase_					:       Optional[Any]    =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""			)
     parser.add_argument(
         """--model_name_or_path"""					,   type=snake_case__					,   default="""bert-base-cased"""					,   help="""Path to pretrained model or model identifier from huggingface.co/models."""					,   required=snake_case__					,   )
     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(
         """--performance_lower_bound"""					,   type=snake_case__					,   default=snake_case__					,   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=snake_case__					,   default=3					,   help="""Number of train epochs."""					,   )
     lowerCAmelCase_					:       Optional[Any]    =		parser.parse_args()
     lowerCAmelCase_					:       int    =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
     training_function(snake_case__					,   snake_case__			)


if __name__ == "__main__":
      main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

UpperCAmelCase_					:   int 							=						{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  """bit"""
  __UpperCamelCase	     =  ["""preactivation""", """bottleneck"""]
  __UpperCamelCase	     =  ["""SAME""", """VALID"""]







  def __init__(	self    ,						_lowercase=3    ,						_lowercase=64    ,						_lowercase=[256, 512, 1_024, 2_048]    ,						_lowercase=[3, 4, 6, 3]    ,						_lowercase="preactivation"    ,						_lowercase="relu"    ,						_lowercase=None    ,						_lowercase=32    ,						_lowercase=0.0    ,						_lowercase=False    ,						_lowercase=32    ,						_lowercase=1    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)
       if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
       if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                 lowerCAmelCase_					:       int    =		global_padding.upper()
            else:
                 raise ValueError(F'Padding strategy {global_padding} not supported'						)
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       List[str]    =		embedding_size
       lowerCAmelCase_					:       Dict    =		hidden_sizes
       lowerCAmelCase_					:       Optional[int]    =		depths
       lowerCAmelCase_					:       List[Any]    =		layer_type
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       Optional[int]    =		global_padding
       lowerCAmelCase_					:       Optional[Any]    =		num_groups
       lowerCAmelCase_					:       Optional[Any]    =		drop_path_rate
       lowerCAmelCase_					:       Tuple    =		embedding_dynamic_padding
       lowerCAmelCase_					:       Any    =		output_stride
       lowerCAmelCase_					:       List[Any]    =		width_factor

       lowerCAmelCase_					:       Union[str, Any]    =		["""stem"""] + [F'stage{idx}' for idx in range(1    ,						len(_lowercase						) + 1						)]
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		get_aligned_output_features_output_indices(
           out_features=_lowercase    ,						out_indices=_lowercase    ,						stage_names=self.stage_names						)
