"""simple docstring"""





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_UpperCamelCase     :		Tuple											=					logging.get_logger(__name__)

_UpperCamelCase     :		Optional[int]											=					{"vocab_file": "spiece.model"}

_UpperCamelCase     :		int											=					{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

_UpperCamelCase     :		List[Any]											=					{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
_UpperCamelCase     :		Optional[Any]											=					0
_UpperCamelCase     :		Tuple											=					1
_UpperCamelCase     :		Union[str, Any]											=					2
_UpperCamelCase     :		List[Any]											=					3
_UpperCamelCase     :		Tuple											=					4


class   UpperCAmelCase_			(					a__):
      lowerCamelCase__	:					Optional[Any]										=						VOCAB_FILES_NAMES
      lowerCamelCase__	:					Dict										=						PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__	:					Union[str, Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__	:					int										=						"left"


      def __init__(     self	,    a	,    a=False	,    a=True	,    a=False	,    a="<s>"	,    a="</s>"	,    a="<unk>"	,    a="<sep>"	,    a="<pad>"	,    a="<cls>"	,    a="<mask>"	,    a=["<eop>", "<eod>"]	,    a = None	,    **a	,    ) ->      Any:
             lowercase__    :	Optional[Any]     					=			AddedToken(_A	,    lstrip=_A	,    rstrip=_A							) if isinstance(_A	,    _A							) else mask_token

             lowercase__    :	Union[str, Any]     					=			{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 do_lower_case=_A	,    remove_space=_A	,    keep_accents=_A	,    bos_token=_A	,    eos_token=_A	,    unk_token=_A	,    sep_token=_A	,    pad_token=_A	,    cls_token=_A	,    mask_token=_A	,    additional_special_tokens=_A	,    sp_model_kwargs=self.sp_model_kwargs	,    **_A	,    )

             lowercase__    :	int     					=			3

             lowercase__    :	Union[str, Any]     					=			do_lower_case
             lowercase__    :	Dict     					=			remove_space
             lowercase__    :	int     					=			keep_accents
             lowercase__    :	List[str]     					=			vocab_file

             lowercase__    :	List[Any]     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(_A							)


      @property
      def 	_UpperCAmelCase (     self							) ->      Any:
             return len(self.sp_model							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[Any]     					=			{self.convert_ids_to_tokens(_A							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab


      def __getstate__(     self							) ->      List[str]:
             lowercase__    :	List[Any]     					=			self.__dict__.copy()
             lowercase__    :	List[str]     					=			None
             return state


      def __setstate__(     self	,    a							) ->      str:
             lowercase__    :	Union[str, Any]     					=			d

             # for backward compatibility
             if not hasattr(self	,    'sp_model_kwargs'							):
                    lowercase__    :	Union[str, Any]     					=			{}

             lowercase__    :	Optional[Any]     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(self.vocab_file							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:

             if self.remove_space:
                    lowercase__    :	str     					=			' '.join(inputs.strip().split()							)
             else:
                    lowercase__    :	Dict     					=			inputs
             lowercase__    :	List[str]     					=			outputs.replace('``'	,    '"'							).replace('\'\''	,    '"'							)

             if not self.keep_accents:
                    lowercase__    :	Optional[Any]     					=			unicodedata.normalize('NFKD'	,    _A							)
                    lowercase__    :	Dict     					=			''.join([c for c in outputs if not unicodedata.combining(_A							)]							)
             if self.do_lower_case:
                    lowercase__    :	Tuple     					=			outputs.lower()

             return outputs


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:
             lowercase__    :	Union[str, Any]     					=			self.preprocess_text(_A							)
             lowercase__    :	int     					=			self.sp_model.encode(_A	,    out_type=_A							)
             lowercase__    :	int     					=			[]
             for piece in pieces:
                    if len(_A							) > 1 and piece[-1] == str(','							) and piece[-2].isdigit():
                           lowercase__    :	Union[str, Any]     					=			self.sp_model.EncodeAsPieces(piece[:-1].replace(_A	,    ''							)							)
                           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                  if len(cur_pieces[0]							) == 1:
                                         lowercase__    :	int     					=			cur_pieces[1:]
                                  else:
                                         lowercase__    :	Any     					=			cur_pieces[0][1:]
                           cur_pieces.append(piece[-1]							)
                           new_pieces.extend(_A							)
                    else:
                           new_pieces.append(_A							)

             return new_pieces


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             return self.sp_model.PieceToId(_A							)


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             return self.sp_model.IdToPiece(_A							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Dict:
             lowercase__    :	Optional[Any]     					=			''.join(_A							).replace(_A	,    ' '							).strip()
             return out_string


      def 	_UpperCAmelCase (     self	,    a	,    a = False	,    a = None	,    a = True	,    **a	,    ) ->      List[str]:
             lowercase__    :	Dict     					=			kwargs.pop('use_source_tokenizer'	,    _A							)

             lowercase__    :	Dict     					=			self.convert_ids_to_tokens(_A	,    skip_special_tokens=_A							)

             # To avoid mixing byte-level and unicode for byte-level BPT
             # we need to build string separately for added tokens and byte-level tokens
             # cf. https://github.com/huggingface/transformers/issues/1133
             lowercase__    :	Optional[Any]     					=			[]
             lowercase__    :	int     					=			[]
             for token in filtered_tokens:
                    if skip_special_tokens and token in self.all_special_ids:
                           continue
                    if token in self.added_tokens_encoder:
                           if current_sub_text:
                                  sub_texts.append(self.convert_tokens_to_string(_A							)							)
                                  lowercase__    :	Tuple     					=			[]
                           sub_texts.append(_A							)
                    else:
                           current_sub_text.append(_A							)
             if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(_A							)							)

             # Mimic the behavior of the Rust tokenizer:
             # By default, there are no spaces between special tokens
             lowercase__    :	List[Any]     					=			''.join(_A							)

             lowercase__    :	Tuple     					=			(
                 clean_up_tokenization_spaces
                 if clean_up_tokenization_spaces is not None
                 else self.clean_up_tokenization_spaces
             )
             if clean_up_tokenization_spaces:
                    lowercase__    :	int     					=			self.clean_up_tokenization(_A							)
                    return clean_text
             else:
                    return text


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      str:
             lowercase__    :	str     					=			[self.sep_token_id]
             lowercase__    :	Union[str, Any]     					=			[self.cls_token_id]
             if token_ids_a is None:
                    return token_ids_a + sep + cls
             return token_ids_a + sep + token_ids_a + sep + cls


      def 	_UpperCAmelCase (     self	,    a	,    a = None	,    a = False							) ->      Tuple:

             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=_A	,    token_ids_a=_A	,    already_has_special_tokens=_A							)

             if token_ids_a is not None:
                    return ([0] * len(_A							)) + [1] + ([0] * len(_A							)) + [1, 1]
             return ([0] * len(_A							)) + [1, 1]


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      Optional[Any]:
             lowercase__    :	str     					=			[self.sep_token_id]
             lowercase__    :	Any     					=			[2]

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


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[str]:

             if not os.path.isdir(_A							):
                    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                    return
             lowercase__    :	str     					=			os.path.join(
                 _A	,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(_A							) and os.path.isfile(self.vocab_file							):
                    copyfile(self.vocab_file	,    _A							)
             elif not os.path.isfile(self.vocab_file							):
                    with open(_A	,    'wb'							) as fi:
                           lowercase__    :	Optional[Any]     					=			self.sp_model.serialized_model_proto()
                           fi.write(_A							)

             return (out_vocab_file,)


"""simple docstring"""





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     :		int											=					16
_UpperCamelCase     :		Union[str, Any]											=					32





def        a_  (   _lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       return int(x / 2**20  )


class   UpperCAmelCase_			:


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


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





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





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			load_dataset(
           'glue'     ,				'mrpc'     ,				split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""}  )

       def tokenize_function(_lowerCAmelCase						:				int  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	List[str]     					=			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
       lowercase__    :	Union[str, 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
       lowercase__    :	Union[str, 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=128     ,				return_tensors='pt'  )
              return tokenizer.pad(_lowerCAmelCase     ,				padding='longest'     ,				return_tensors='pt'  )

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

       return train_dataloader, eval_dataloader





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			Accelerator()

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

       set_seed(_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	Tuple     					=			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)
       lowercase__    :	List[Any]     					=			AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase     ,				return_dict=_lowerCAmelCase  )

       # Instantiate optimizer
       lowercase__    :	List[Any]     					=			(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase__    :	Optional[Any]     					=			optimizer_cls(params=model.parameters()     ,				lr=_lowerCAmelCase  )

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

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

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





       '''simple docstring'''

       lowercase__    :	int     					=			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=320     ,				help='Number of training examples to use.'     ,				)
       parser.add_argument(
           '--n_val'     ,				type=_lowerCAmelCase     ,				default=160     ,				help='Number of validation examples to use.'     ,				)
       parser.add_argument(
           '--num_epochs'     ,				type=_lowerCAmelCase     ,				default=1     ,				help='Number of train epochs.'     ,				)
       lowercase__    :	Any     					=			parser.parse_args()
       lowercase__    :	Optional[Any]     					=			{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(_lowerCAmelCase     ,				_lowerCAmelCase  )


if __name__ == "__main__":
       main()

