



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


UpperCAmelCase_		 =					1_6
UpperCAmelCase_		 =					3_2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]				)  ->     Dict:
   return int(x / 2**20				)


class 	lowerCamelCase__	:



 """simple docstring"""

 def __enter__(    self					:					Tuple      )   ->	Dict:
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
    _A      =       torch.cuda.memory_allocated()
    return self

 def __exit__(    self					:					Tuple			,						*__lowerCAmelCase					:					List[str]      )   ->	Dict:
    gc.collect()
    torch.cuda.empty_cache()
    _A      =       torch.cuda.memory_allocated()
    _A      =       torch.cuda.max_memory_allocated()
    _A      =       bamb(self.end - self.begin      )
    _A      =       bamb(self.peak - self.begin      )
    # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:Tuple = 16	,   _snake_case	:Dict = "bert-base-cased"	,   _snake_case	:Union[str, Any] = 320	,   _snake_case	:int = 160	,   )  ->     str:
   _A      =       AutoTokenizer.from_pretrained(snake_case__				)
   _A      =       load_dataset(
       '''glue'''	,   '''mrpc'''	,   split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''}				)

   def tokenize_function(_snake_case	:Dict				):
      # max_length=None => use the model max length (it's actually the default)
      _A      =       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
   _A      =       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
   _A      =       tokenized_datasets.rename_column('''label'''	,   '''labels'''				)

   def collate_fn(_snake_case	: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=128	,   return_tensors='''pt'''				)
      return tokenizer.pad(snake_case__	,   padding='''longest'''	,   return_tensors='''pt'''				)

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

   return train_dataloader, eval_dataloader

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:List[Any]				)  ->     Optional[int]:
   _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(snake_case__				)
   _A      =       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)
   _A      =       AutoModelForSequenceClassification.from_pretrained(snake_case__	,   return_dict=snake_case__				)

   # 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=snake_case__				)

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

   # Now we train the model
   _A      =       {}
   for epoch in range(snake_case__	,   snake_case__				):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(snake_case__				):
            _A      =       model(**snake_case__				)
            _A      =       outputs.loss
            _A      =       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				)				)				)
      _A      =       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 	SCREAMING_SNAKE_CASE_							(	)  ->     str:
   _A      =       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.'''	,   )
   _A      =       parser.parse_args()
   _A      =       {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
   training_function(snake_case__	,   snake_case__				)


if __name__ == "__main__":
   main()





'''simple docstring'''




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


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  Any   =		num_attention_heads
         A			:  Optional[Any]   =		intermediate_size
         A			:  Union[str, Any]   =		hidden_act
         A			:  Tuple   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Tuple   =		initializer_range
         A			:  List[Any]   =		layer_norm_eps
         A			:  Union[str, Any]   =		qkv_bias

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




