
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


lowercase__          =16
lowercase__          =32




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ):
	return int(x / 2**2_0  )



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







			def __exit__(self			: Optional[int] ,    *snake_case_			: int       ):
				gc.collect()
				torch.cuda.empty_cache()
				__a			:  List[str]		    =  torch.cuda.memory_allocated()
				__a			:  str		    =  torch.cuda.max_memory_allocated()
				__a			:  Optional[Any]		    =  bamb(self.end - self.begin       )
				__a			:  Any		    =  bamb(self.peak - self.begin       )
				# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def        __UpperCamelCase					(    lowerCAmelCase__							:    Accelerator  ,       lowerCAmelCase__							:    int = 1_6  ,       lowerCAmelCase__							:    str = "bert-base-cased"  ,       lowerCAmelCase__							:    int = 3_2_0  ,       lowerCAmelCase__							:    int = 1_6_0  ,       ):
	__a			:  Dict		    =  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
	__a			:  str		    =  load_dataset(
	    '''glue'''  ,       '''mrpc'''  ,       split={'''train''': f"train[:{n_train}]", '''validation''': f"validation[:{n_val}]"}  )

	def tokenize_function(lowerCAmelCase__							:    Dict  ):
		# max_length=None => use the model max length (it's actually the default)
		__a			:  Any		    =  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			:  List[Any]		    =  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			:  Any		    =  tokenized_datasets.rename_column('''label'''  ,       '''labels'''  )

	def collate_fn(lowerCAmelCase__							:    Any  ):
		# 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_2_8  ,       return_tensors='''pt'''  )
		return tokenizer.pad(lowerCAmelCase__  ,       padding='''longest'''  ,       return_tensors='''pt'''  )

	# Instantiate dataloaders.
	__a			:  Dict		    =  DataLoader(
	    tokenized_datasets['''train''']  ,       shuffle=lowerCAmelCase__  ,       collate_fn=lowerCAmelCase__  ,       batch_size=lowerCAmelCase__  )
	__a			:  str		    =  DataLoader(
	    tokenized_datasets['''validation''']  ,       shuffle=lowerCAmelCase__  ,       collate_fn=lowerCAmelCase__  ,       batch_size=lowerCAmelCase__  )

	return train_dataloader, eval_dataloader




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Tuple  ):
	# Initialize accelerator
	__a			:  List[Any]		    =  Accelerator()

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

	set_seed(lowerCAmelCase__  )
	__a			:  int		    =  get_dataloaders(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       args.n_train  ,       args.n_val  )

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

	# Instantiate optimizer
	__a			:  Tuple		    =  (
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	__a			:  Union[str, Any]		    =  optimizer_cls(params=model.parameters()  ,       lr=lowerCAmelCase__  )

	if accelerator.state.deepspeed_plugin is not None:
		__a			:  Dict		    =  accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		__a			:  List[str]		    =  1
	__a			:  Tuple		    =  (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			:  Any		    =  get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase__  ,       num_warmup_steps=0  ,       num_training_steps=lowerCAmelCase__  ,       )
	else:
		__a			:  List[str]		    =  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			:  List[Any]		    =  accelerator.prepare(
	    lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

	# We need to keep track of how many total steps we have iterated over
	__a			:  List[Any]		    =  0
	# We also need to keep track of the stating epoch so files are named properly
	__a			:  List[Any]		    =  0

	# Now we train the model
	__a			:  Dict		    =  {}
	for epoch in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
		with TorchTracemalloc() as tracemalloc:
			model.train()
			for step, batch in enumerate(lowerCAmelCase__  ):
				__a			:  Union[str, Any]		    =  model(**lowerCAmelCase__  )
				__a			:  Optional[int]		    =  outputs.loss
				__a			:  Dict		    =  loss / gradient_accumulation_steps
				accelerator.backward(lowerCAmelCase__  )
				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			:  List[str]		    =  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(lowerCAmelCase__  ,       lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
	__a			:  List[Any]		    =  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(
	    '''--peak_memory_upper_bound'''  ,       type=lowerCAmelCase__  ,       default=lowerCAmelCase__  ,       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=lowerCAmelCase__  ,       default=3_2_0  ,       help='''Number of training examples to use.'''  ,       )
	parser.add_argument(
	    '''--n_val'''  ,       type=lowerCAmelCase__  ,       default=1_6_0  ,       help='''Number of validation examples to use.'''  ,       )
	parser.add_argument(
	    '''--num_epochs'''  ,       type=lowerCAmelCase__  ,       default=1  ,       help='''Number of train epochs.'''  ,       )
	__a			:  Optional[int]		    =  parser.parse_args()
	__a			:  Tuple		    =  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
	training_function(lowerCAmelCase__  ,       lowerCAmelCase__  )


if __name__ == "__main__":
						main()


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
