





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def A_     (		A__		,				A__		,				A__		,				A__		,				A__=True		,				A__="pt"    )  ->		int:
       a__						:     Optional[int]			=    {'add_prefix_space': True} if isinstance(A__		,				A__    ) and not line.startswith(' '    ) else {}
       a__						:     List[Any]			=    padding_side
       return tokenizer(
           [line]		,				max_length=A__		,				padding='max_length' if pad_to_max_length else None		,				truncation=A__		,				return_tensors=A__		,				add_special_tokens=A__		,				**A__		,				)



def A_     (		A__		,				A__		,				A__=None		,				)  ->		int:
       a__						:     str			=    input_ids.ne(A__    ).any(dim=0    )
       if attention_mask is None:
              return input_ids[:, keep_column_mask]
       else:
              return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase="train" ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase="" ,  )      ->						Optional[Any]:







          '''simple docstring'''

          super().__init__()
          a__						:     Any			=    Path(lowercase).joinpath(type_path + '.source')
          a__						:     Tuple			=    Path(lowercase).joinpath(type_path + '.target')
          a__						:     Union[str, Any]			=    self.get_char_lens(self.src_file)
          a__						:     List[Any]			=    max_source_length
          a__						:     List[str]			=    max_target_length
          assert min(self.src_lens) > 0, F'found empty line in {self.src_file}'
          a__						:     Dict			=    tokenizer
          a__						:     List[Any]			=    prefix
          if n_obs is not None:
                 a__						:     Dict			=    self.src_lens[:n_obs]
          a__						:     Tuple			=    src_lang
          a__						:     Union[str, Any]			=    tgt_lang

   def __len__( self)      ->						Optional[Any]:







          '''simple docstring'''

          return len(self.src_lens)

   def __getitem__( self ,  lowercase)      ->						Dict[str, torch.Tensor]:







          '''simple docstring'''

          a__						:     Dict			=    index + 1  # linecache starts at 1
          a__						:     List[Any]			=    self.prefix + linecache.getline(str(self.src_file) ,  lowercase).rstrip('\n')
          a__						:     Dict			=    linecache.getline(str(self.tgt_file) ,  lowercase).rstrip('\n')
          assert source_line, F'empty source line for index {index}'
          assert tgt_line, F'empty tgt line for index {index}'

          # Need to add eos token manually for T5
          if isinstance(self.tokenizer ,  lowercase):
                 source_line += self.tokenizer.eos_token
                 tgt_line += self.tokenizer.eos_token

          # Pad source and target to the right
          a__						:     Union[str, Any]			=    (
              self.tokenizer.question_encoder if isinstance(self.tokenizer ,  lowercase) else self.tokenizer
          )
          a__						:     int			=    self.tokenizer.generator if isinstance(self.tokenizer ,  lowercase) else self.tokenizer

          a__						:     Optional[int]			=    encode_line(lowercase ,  lowercase ,  self.max_source_length ,  'right')
          a__						:     Optional[int]			=    encode_line(lowercase ,  lowercase ,  self.max_target_length ,  'right')

          a__						:     Any			=    source_inputs['input_ids'].squeeze()
          a__						:     Optional[int]			=    target_inputs['input_ids'].squeeze()
          a__						:     Tuple			=    source_inputs['attention_mask'].squeeze()
          return {
              "input_ids": source_ids,
              "attention_mask": src_mask,
              "decoder_input_ids": target_ids,
          }

   @staticmethod
   def 							__lowercase		( lowercase)      ->						Dict:







          '''simple docstring'''

          return [len(lowercase) for x in Path(lowercase).open().readlines()]

   def 							__lowercase		( self ,  lowercase)      ->						Dict[str, torch.Tensor]:







          '''simple docstring'''

          a__						:     Tuple			=    torch.stack([x['input_ids'] for x in batch])
          a__						:     int			=    torch.stack([x['attention_mask'] for x in batch])
          a__						:     Tuple			=    torch.stack([x['decoder_input_ids'] for x in batch])
          a__						:     Union[str, Any]			=    (
              self.tokenizer.generator.pad_token_id
              if isinstance(self.tokenizer ,  lowercase)
              else self.tokenizer.pad_token_id
          )
          a__						:     Optional[int]			=    (
              self.tokenizer.question_encoder.pad_token_id
              if isinstance(self.tokenizer ,  lowercase)
              else self.tokenizer.pad_token_id
          )
          a__						:     List[Any]			=    trim_batch(lowercase ,  lowercase)
          a__			,							a__						:     Optional[int]			=    trim_batch(lowercase ,  lowercase ,  attention_mask=lowercase)
          a__						:     int			=    {
              'input_ids': source_ids,
              'attention_mask': source_mask,
              'decoder_input_ids': y,
          }
          return batch

lowercase				:	Dict      						=  getLogger(__name__)



def A_     (		A__    )  ->		List[Any]:
       return list(itertools.chain.from_iterable(A__    )    )



def A_     (		A__    )  ->		None:
       a__						:     Any			=    get_git_info()
       save_json(A__		,				os.path.join(A__		,				'git_log.json'    )    )



def A_     (		A__		,				A__		,				A__=4		,				**A__    )  ->		List[Any]:
       with open(A__		,				'w'    ) as f:
              json.dump(A__		,				A__		,				indent=A__		,				**A__    )



def A_     (		A__    )  ->		str:
       with open(A__    ) as f:
              return json.load(A__    )



def A_     (		)  ->		List[Any]:
       a__						:     Any			=    git.Repo(search_parent_directories=A__    )
       a__						:     Tuple			=    {
           'repo_id': str(A__    ),
           'repo_sha': str(repo.head.object.hexsha    ),
           'repo_branch': str(repo.active_branch    ),
           'hostname': str(socket.gethostname()    ),
       }
       return repo_infos



def A_     (		A__		,				A__    )  ->		List:
       return list(map(A__		,				A__    )    )



def A_     (		A__		,				A__    )  ->		List[str]:
       with open(A__		,				'wb'    ) as f:
              return pickle.dump(A__		,				A__    )



def A_     (		A__    )  ->		int:

       def remove_articles(A__    ):
              return re.sub(R'\b(a|an|the)\b'		,				' '		,				A__    )

       def white_space_fix(A__    ):
              return " ".join(text.split()    )

       def remove_punc(A__    ):
              a__						:     str			=    set(string.punctuation    )
              return "".join(ch for ch in text if ch not in exclude    )

       def lower(A__    ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(A__    )    )    )    )



def A_     (		A__		,				A__    )  ->		Dict:
       a__						:     Optional[Any]			=    normalize_answer(A__    ).split()
       a__						:     Dict			=    normalize_answer(A__    ).split()
       a__						:     Any			=    Counter(A__    ) & Counter(A__    )
       a__						:     Union[str, Any]			=    sum(common.values()    )
       if num_same == 0:
              return 0
       a__						:     Any			=    1.0 * num_same / len(A__    )
       a__						:     List[str]			=    1.0 * num_same / len(A__    )
       a__						:     Union[str, Any]			=    (2 * precision * recall) / (precision + recall)
       return fa



def A_     (		A__		,				A__    )  ->		List[Any]:
       return normalize_answer(A__    ) == normalize_answer(A__    )



def A_     (		A__		,				A__    )  ->		Dict:
       assert len(A__    ) == len(A__    )
       a__						:     int			=    0
       for hypo, pred in zip(A__		,				A__    ):
              em += exact_match_score(A__		,				A__    )
       if len(A__    ) > 0:
              em /= len(A__    )
       return {"em": em}



def A_     (		A__    )  ->		List[str]:
       return model_prefix.startswith('rag'    )



def A_     (		A__		,				A__		,				A__    )  ->		List[str]:
       a__						:     str			=    {p: p for p in extra_params}
       # T5 models don't have `dropout` param, they have `dropout_rate` instead
       a__						:     List[str]			=    'dropout_rate'
       for p in extra_params:
              if getattr(A__		,				A__		,				A__    ):
                     if not hasattr(A__		,				A__    ) and not hasattr(A__		,				equivalent_param[p]    ):
                            logger.info('config doesn\'t have a `{}` attribute'.format(A__    )    )
                            delattr(A__		,				A__    )
                            continue
                     a__						:     Tuple			=    p if hasattr(A__		,				A__    ) else equivalent_param[p]
                     setattr(A__		,				A__		,				getattr(A__		,				A__    )    )
                     delattr(A__		,				A__    )
       return hparams, config









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				:	Optional[int]      						=  1_6
lowercase				:	Optional[int]      						=  3_2



def A_     (		A__    )  ->		Tuple:
       return int(x / 2**20    )





class 			A__   :




   """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
          a__						:     Union[str, Any]			=    torch.cuda.memory_allocated()
          return self

   def __exit__( self ,  *lowercase)      ->						List[Any]:







          '''simple docstring'''

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



def A_     (		A__		,				A__ = 16		,				A__ = "bert-base-cased"		,				A__ = 320		,				A__ = 160		,				)  ->		Dict:
       a__						:     int			=    AutoTokenizer.from_pretrained(A__    )
       a__						:     str			=    load_dataset(
           'glue'		,				'mrpc'		,				split={'train': F'train[:{n_train}]', 'validation': F'validation[:{n_val}]'}    )

       def tokenize_function(A__    ):
              # max_length=None => use the model max length (it's actually the default)
              a__						:     int			=    tokenizer(examples['sentence1']		,				examples['sentence2']		,				truncation=A__		,				max_length=A__    )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       a__						:     List[str]			=    datasets.map(
           A__		,				batched=A__		,				remove_columns=['idx', 'sentence1', 'sentence2']		,				load_from_cache_file=A__    )

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

       def collate_fn(A__    ):
              # 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(A__		,				padding='max_length'		,				max_length=128		,				return_tensors='pt'    )
              return tokenizer.pad(A__		,				padding='longest'		,				return_tensors='pt'    )

       # Instantiate dataloaders.
       a__						:     Dict			=    DataLoader(
           tokenized_datasets['train']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )
       a__						:     Optional[int]			=    DataLoader(
           tokenized_datasets['validation']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

       return train_dataloader, eval_dataloader



def A_     (		A__		,				A__    )  ->		Any:
       # Initialize accelerator
       a__						:     Optional[int]			=    Accelerator()

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

       set_seed(A__    )
       a__			,							a__						:     Any			=    get_dataloaders(A__		,				A__		,				A__		,				args.n_train		,				args.n_val    )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__						:     Optional[int]			=    AutoModelForSequenceClassification.from_pretrained(A__		,				return_dict=A__    )

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

       if accelerator.state.deepspeed_plugin is not None:
              a__						:     str			=    accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              a__						:     str			=    1
       a__						:     Optional[int]			=    (len(A__    ) * 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__						:     List[Any]			=    get_linear_schedule_with_warmup(
                  optimizer=A__		,				num_warmup_steps=0		,				num_training_steps=A__		,				)
       else:
              a__						:     Any			=    DummyScheduler(A__		,				total_num_steps=A__		,				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__			,							a__			,							a__			,							a__			,							a__						:     Optional[int]			=    accelerator.prepare(
           A__		,				A__		,				A__		,				A__		,				A__    )

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

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



def A_     (		)  ->		int:
       a__						:     Union[str, Any]			=    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
       parser.add_argument(
           '--model_name_or_path'		,				type=A__		,				default='bert-base-cased'		,				help='Path to pretrained model or model identifier from huggingface.co/models.'		,				required=A__		,				)
       parser.add_argument(
           '--output_dir'		,				type=A__		,				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=A__		,				default=A__		,				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=A__		,				default=320		,				help='Number of training examples to use.'		,				)
       parser.add_argument(
           '--n_val'		,				type=A__		,				default=160		,				help='Number of validation examples to use.'		,				)
       parser.add_argument(
           '--num_epochs'		,				type=A__		,				default=1		,				help='Number of train epochs.'		,				)
       a__						:     Dict			=    parser.parse_args()
       a__						:     List[str]			=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(A__		,				A__    )


if __name__ == "__main__":
    main()


