




"""simple docstring"""


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 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					Tuple		,   snake_case__							:					Any		,   snake_case__							:					int		,   snake_case__							:					Optional[int]=True		,   snake_case__							:					Optional[int]="pt"    ):
  """simple docstring"""

  _snake_case							:		Dict								=  {"""add_prefix_space""": True} if isinstance(snake_case__		,   snake_case__    ) and not line.startswith(""" """    ) else {}
  _snake_case							:		Optional[Any]								=  padding_side
  return tokenizer(
      [line]		,   max_length=snake_case__		,   padding="""max_length""" if pad_to_max_length else None		,   truncation=snake_case__		,   return_tensors=snake_case__		,   add_special_tokens=snake_case__		,   **snake_case__		,   )


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Any=None		,   ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  input_ids.ne(snake_case__    ).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 					lowercase(     __a ):







 '''simple docstring'''





 def __init__(				self:		str,      a_:		Any,      a_:		Union[str, Any],      a_:		Dict,      a_:		Dict,      a_:		int="train",      a_:		Any=None,      a_:		Any=None,      a_:		Any=None,      a_:		Tuple="",      ):




   '''simple docstring'''



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


 def __len__(				self:		Any		):




   '''simple docstring'''



   return len(self.src_lens		)


 def __getitem__(				self:		Union[str, Any],      a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  index + 1  # linecache starts at 1
   _snake_case							:		Optional[Any]								=  self.prefix + linecache.getline(str(self.src_file		),      a_		).rstrip("""\n"""		)
   _snake_case							:		int								=  linecache.getline(str(self.tgt_file		),      a_		).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,      a_		):
     source_line += self.tokenizer.eos_token
     tgt_line += self.tokenizer.eos_token

   # Pad source and target to the right
   _snake_case							:		Dict								=  (
       self.tokenizer.question_encoder if isinstance(self.tokenizer,      a_		) else self.tokenizer
   )
   _snake_case							:		str								=  self.tokenizer.generator if isinstance(self.tokenizer,      a_		) else self.tokenizer

   _snake_case							:		Tuple								=  encode_line(a_,      a_,      self.max_source_length,      """right"""		)
   _snake_case							:		Optional[Any]								=  encode_line(a_,      a_,      self.max_target_length,      """right"""		)

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


 @staticmethod
 def  UpperCamelCase_   (				a_:		Union[str, Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		List[str]		):




   '''simple docstring'''



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







A_						=				getLogger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					List[List]    ):
  """simple docstring"""

  return list(itertools.chain.from_iterable(snake_case__    )    )


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Any								=  get_git_info()
  save_json(snake_case__		,   os.path.join(snake_case__		,   """git_log.json"""    )    )


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[Any]		,   snake_case__							:					Any=4		,   **snake_case__							:					str    ):
  """simple docstring"""

  with open(snake_case__		,   """w"""    ) as f:
    json.dump(snake_case__		,   snake_case__		,   indent=snake_case__		,   **snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Tuple    ):
  """simple docstring"""

  with open(snake_case__    ) as f:
    return json.load(snake_case__    )


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		int								=  git.Repo(search_parent_directories=snake_case__    )
  _snake_case							:		List[str]								=  {
      """repo_id""": str(snake_case__    ),
      """repo_sha""": str(repo.head.object.hexsha    ),
      """repo_branch""": str(repo.active_branch    ),
      """hostname""": str(socket.gethostname()    ),
  }
  return repo_infos


def 					UpperCAmelCase__       (snake_case__							:					Callable		,   snake_case__							:					Iterable    ):
  """simple docstring"""

  return list(map(snake_case__		,   snake_case__    )    )


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  with open(snake_case__		,   """wb"""    ) as f:
    return pickle.dump(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  def remove_articles(snake_case__							:					Optional[Any]    ):
    return re.sub(R"""\b(a|an|the)\b"""		,   """ """		,   snake_case__    )

  def white_space_fix(snake_case__							:					Dict    ):
    return " ".join(text.split()    )

  def remove_punc(snake_case__							:					Any    ):
    _snake_case							:		List[str]								=  set(string.punctuation    )
    return "".join(ch for ch in text if ch not in exclude    )

  def lower(snake_case__							:					Tuple    ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(snake_case__    )    )    )    )


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  normalize_answer(snake_case__    ).split()
  _snake_case							:		Tuple								=  normalize_answer(snake_case__    ).split()
  _snake_case							:		List[str]								=  Counter(snake_case__    ) & Counter(snake_case__    )
  _snake_case							:		Dict								=  sum(common.values()    )
  if num_same == 0:
    return 0
  _snake_case							:		List[str]								=  1.0 * num_same / len(snake_case__    )
  _snake_case							:		Optional[int]								=  1.0 * num_same / len(snake_case__    )
  _snake_case							:		List[Any]								=  (2 * precision * recall) / (precision + recall)
  return fa


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					Tuple    ):
  """simple docstring"""

  return normalize_answer(snake_case__    ) == normalize_answer(snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  assert len(snake_case__    ) == len(snake_case__    )
  _snake_case							:		Union[str, Any]								=  0
  for hypo, pred in zip(snake_case__		,   snake_case__    ):
    em += exact_match_score(snake_case__		,   snake_case__    )
  if len(snake_case__    ) > 0:
    em /= len(snake_case__    )
  return {"em": em}


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  return model_prefix.startswith("""rag"""    )






def 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					int		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		int								=  {p: p for p in extra_params}
  # T5 models don't have `dropout` param, they have `dropout_rate` instead
  _snake_case							:		List[Any]								=  """dropout_rate"""
  for p in extra_params:
    if getattr(snake_case__		,   snake_case__		,   snake_case__    ):
      if not hasattr(snake_case__		,   snake_case__    ) and not hasattr(snake_case__		,   equivalent_param[p]    ):
        logger.info("""config doesn't have a `{}` attribute""".format(snake_case__    )    )
        delattr(snake_case__		,   snake_case__    )
        continue
      _snake_case							:		str								=  p if hasattr(snake_case__		,   snake_case__    ) else equivalent_param[p]
      setattr(snake_case__		,   snake_case__		,   getattr(snake_case__		,   snake_case__    )    )
      delattr(snake_case__		,   snake_case__    )
  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


_SCREAMING_SNAKE_CASE										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






 def __enter__(					self							)  ->					List[str]:
        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		,				*lowerCAmelCase_							)  ->					Optional[int]:
        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  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _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__			:Optional[int]):
              # 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  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _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   ,  _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   ,  _A   ,  _A   ,  _A   ,  _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  snake_case  (       )						->   Optional[int]:
       _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()




