






import argparse
import gc
import json
import os

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


lowerCamelCase__       		=  16
lowerCamelCase__       		=  32






def 	UpperCamelCase	(							snake_case__  : Union[str, Any]	):




     '''simple docstring'''






     return int(x / 2**20	)




class       snake_case__ :

       '''simple docstring'''



       def __enter__(				self     )			->   Dict:






            '''simple docstring'''





            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
            __snake_case      :Union[str, Any] 							=    torch.cuda.memory_allocated()
            return self



       def __exit__(				self      ,    *a__     )			->   int:






            '''simple docstring'''





            gc.collect()
            torch.cuda.empty_cache()
            __snake_case      :List[str] 							=    torch.cuda.memory_allocated()
            __snake_case      :List[str] 							=    torch.cuda.max_memory_allocated()
            __snake_case      :Any 							=    bamb(self.end - self.begin     )
            __snake_case      :Any 							=    bamb(self.peak - self.begin     )
            # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 	UpperCamelCase	(							snake_case__  : Accelerator						,snake_case__  : int = 16						,snake_case__  : str = "bert-base-cased"						,snake_case__  : int = 320						,snake_case__  : int = 160						,):




     '''simple docstring'''






     __snake_case      :str 							=    AutoTokenizer.from_pretrained(snake_case__	)
     __snake_case      :Dict 							=    load_dataset(
         """glue"""						,"""mrpc"""						,split={"""train""": f'''train[:{n_train}]''', """validation""": f'''validation[:{n_val}]'''}	)

     def tokenize_function(snake_case__  : Optional[int]	):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case      :Optional[int] 							=    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
     __snake_case      :Optional[Any] 							=    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
     __snake_case      :Dict 							=    tokenized_datasets.rename_column("""label"""						,"""labels"""	)

     def collate_fn(snake_case__  : Tuple	):
          # 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=128						,return_tensors="""pt"""	)
          return tokenizer.pad(snake_case__						,padding="""longest"""						,return_tensors="""pt"""	)

     # Instantiate dataloaders.
     __snake_case      :Dict 							=    DataLoader(
         tokenized_datasets["""train"""]						,shuffle=snake_case__						,collate_fn=snake_case__						,batch_size=snake_case__	)
     __snake_case      :str 							=    DataLoader(
         tokenized_datasets["""validation"""]						,shuffle=snake_case__						,collate_fn=snake_case__						,batch_size=snake_case__	)

     return train_dataloader, eval_dataloader






def 	UpperCamelCase	(							snake_case__  : Any						,snake_case__  : Tuple	):




     '''simple docstring'''






     __snake_case      :Optional[int] 							=    Accelerator()

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

     set_seed(snake_case__	)
     __snake_case							,			__snake_case      :Union[str, Any] 							=    get_dataloaders(snake_case__						,snake_case__						,snake_case__						,args.n_train						,args.n_val	)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __snake_case      :Tuple 							=    AutoModelForSequenceClassification.from_pretrained(snake_case__						,return_dict=snake_case__	)

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

     if accelerator.state.deepspeed_plugin is not None:
          __snake_case      :Optional[int] 							=    accelerator.state.deepspeed_plugin.deepspeed_config[
              """gradient_accumulation_steps"""
          ]
     else:
          __snake_case      :Optional[int] 							=    1
     __snake_case      :Dict 							=    (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
     ):
          __snake_case      :Union[str, Any] 							=    get_linear_schedule_with_warmup(
              optimizer=snake_case__						,num_warmup_steps=0						,num_training_steps=snake_case__						,)
     else:
          __snake_case      :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.
     __snake_case							,			__snake_case							,			__snake_case							,			__snake_case							,			__snake_case      :str 							=    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
     __snake_case      :Dict 							=    0
     # We also need to keep track of the stating epoch so files are named properly
     __snake_case      :Any 							=    0

     # Now we train the model
     __snake_case      :Optional[int] 							=    {}
     for epoch in range(snake_case__						,snake_case__	):
          with TorchTracemalloc() as tracemalloc:
               model.train()
               for step, batch in enumerate(snake_case__	):
                    __snake_case      :Optional[int] 							=    model(**snake_case__	)
                    __snake_case      :Optional[Any] 							=    outputs.loss
                    __snake_case      :str 							=    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

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin	)	)	)
          accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used	)	)
          accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked	)	)
          accelerator.print(
              """Total Peak Memory consumed during the train (max): {}""".format(
                  tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
          __snake_case      :Optional[Any] 							=    tracemalloc.peaked + bamb(tracemalloc.begin	)
          if args.peak_memory_upper_bound is not None:
               assert (
                   train_total_peak_memory[f'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
               ), "Peak memory usage exceeded the upper bound"

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir						,"""peak_memory_utilization.json"""	)						,"""w"""	) as f:
               json.dump(snake_case__						,snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[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(
         """--peak_memory_upper_bound"""						,type=snake_case__						,default=snake_case__						,help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""						,)
     parser.add_argument(
         """--n_train"""						,type=snake_case__						,default=320						,help="""Number of training examples to use."""						,)
     parser.add_argument(
         """--n_val"""						,type=snake_case__						,default=160						,help="""Number of validation examples to use."""						,)
     parser.add_argument(
         """--num_epochs"""						,type=snake_case__						,default=1						,help="""Number of train epochs."""						,)
     __snake_case      :Tuple 							=    parser.parse_args()
     __snake_case      :Dict 							=    {"""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


lowerCamelCase__       		=  {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Tuple								=			"tapas"



       def __init__(				self      ,    a__=3_05_22      ,    a__=7_68      ,    a__=12      ,    a__=12      ,    a__=30_72      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=10_24      ,    a__=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]      ,    a__=0.02      ,    a__=1e-12      ,    a__=0      ,    a__=10.0      ,    a__=0      ,    a__=1.0      ,    a__=None      ,    a__=1.0      ,    a__=False      ,    a__=None      ,    a__=1.0      ,    a__=1.0      ,    a__=False      ,    a__=False      ,    a__="ratio"      ,    a__=None      ,    a__=None      ,    a__=64      ,    a__=32      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__      ,    **a__     )

            # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
            __snake_case      :Optional[Any] 							=    vocab_size
            __snake_case      :Tuple 							=    hidden_size
            __snake_case      :Tuple 							=    num_hidden_layers
            __snake_case      :Any 							=    num_attention_heads
            __snake_case      :List[str] 							=    hidden_act
            __snake_case      :List[str] 							=    intermediate_size
            __snake_case      :int 							=    hidden_dropout_prob
            __snake_case      :List[Any] 							=    attention_probs_dropout_prob
            __snake_case      :Any 							=    max_position_embeddings
            __snake_case      :Optional[int] 							=    type_vocab_sizes
            __snake_case      :Tuple 							=    initializer_range
            __snake_case      :Any 							=    layer_norm_eps

            # Fine-tuning task hyperparameters
            __snake_case      :Optional[int] 							=    positive_label_weight
            __snake_case      :Optional[Any] 							=    num_aggregation_labels
            __snake_case      :Dict 							=    aggregation_loss_weight
            __snake_case      :str 							=    use_answer_as_supervision
            __snake_case      :int 							=    answer_loss_importance
            __snake_case      :List[Any] 							=    use_normalized_answer_loss
            __snake_case      :List[str] 							=    huber_loss_delta
            __snake_case      :Any 							=    temperature
            __snake_case      :Optional[int] 							=    aggregation_temperature
            __snake_case      :List[str] 							=    use_gumbel_for_cells
            __snake_case      :Optional[int] 							=    use_gumbel_for_aggregation
            __snake_case      :Optional[Any] 							=    average_approximation_function
            __snake_case      :Union[str, Any] 							=    cell_selection_preference
            __snake_case      :Tuple 							=    answer_loss_cutoff
            __snake_case      :Union[str, Any] 							=    max_num_rows
            __snake_case      :Optional[int] 							=    max_num_columns
            __snake_case      :str 							=    average_logits_per_cell
            __snake_case      :Union[str, Any] 							=    select_one_column
            __snake_case      :Optional[int] 							=    allow_empty_column_selection
            __snake_case      :Optional[Any] 							=    init_cell_selection_weights_to_zero
            __snake_case      :Tuple 							=    reset_position_index_per_cell
            __snake_case      :Any 							=    disable_per_token_loss

            # Aggregation hyperparameters
            __snake_case      :List[str] 							=    aggregation_labels
            __snake_case      :int 							=    no_aggregation_label_index

            if isinstance(self.aggregation_labels      ,    a__     ):
                 __snake_case      :Any 							=    {int(a__     ): v for k, v in aggregation_labels.items()}


