



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__	:   Optional[Any]	,					snake_case__	:   List[str]	,					snake_case__	:   int	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Dict=True	,					snake_case__	:   Union[str, Any]="pt"     )     ->					int:
   UpperCamelCase   :  str						     =     {'add_prefix_space': True} if isinstance(snake_case__	,					snake_case__     ) and not line.startswith(' '     ) else {}
   UpperCamelCase   :  str						     =     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__	:   List[str]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]=None	,					)     ->					Optional[int]:
   UpperCamelCase   :  str						     =     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     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_="train",      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_="",      )					->		Optional[int]:
          super().__init__()
          UpperCamelCase   :  Any						     =     Path(SCREAMING_SNAKE_CASE_	).joinpath(type_path + '.source'	)
          UpperCamelCase   :  Tuple						     =     Path(SCREAMING_SNAKE_CASE_	).joinpath(type_path + '.target'	)
          UpperCamelCase   :  str						     =     self.get_char_lens(self.src_file	)
          UpperCamelCase   :  str						     =     max_source_length
          UpperCamelCase   :  Any						     =     max_target_length
          assert min(self.src_lens	) > 0, F"""found empty line in {self.src_file}"""
          UpperCamelCase   :  Union[str, Any]						     =     tokenizer
          UpperCamelCase   :  List[str]						     =     prefix
          if n_obs is not None:
             UpperCamelCase   :  Any						     =     self.src_lens[:n_obs]
          UpperCamelCase   :  Optional[int]						     =     src_lang
          UpperCamelCase   :  Any						     =     tgt_lang
       def __len__(      self	)					->		int:
          return len(self.src_lens	)
       def __getitem__(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict[str, torch.Tensor]:
          UpperCamelCase   :  str						     =     index + 1  # linecache starts at 1
          UpperCamelCase   :  List[str]						     =     self.prefix + linecache.getline(str(self.src_file	),      SCREAMING_SNAKE_CASE_	).rstrip('\n'	)
          UpperCamelCase   :  str						     =     linecache.getline(str(self.tgt_file	),      SCREAMING_SNAKE_CASE_	).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,      SCREAMING_SNAKE_CASE_	):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

          # Pad source and target to the right
          UpperCamelCase   :  List[str]						     =     (
              self.tokenizer.question_encoder if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	) else self.tokenizer
          )
          UpperCamelCase   :  str						     =     self.tokenizer.generator if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	) else self.tokenizer

          UpperCamelCase   :  Dict						     =     encode_line(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      self.max_source_length,      'right'	)
          UpperCamelCase   :  str						     =     encode_line(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      self.max_target_length,      'right'	)

          UpperCamelCase   :  List[Any]						     =     source_inputs['input_ids'].squeeze()
          UpperCamelCase   :  List[str]						     =     target_inputs['input_ids'].squeeze()
          UpperCamelCase   :  str						     =     source_inputs['attention_mask'].squeeze()
          return {
              "input_ids": source_ids,
              "attention_mask": src_mask,
              "decoder_input_ids": target_ids,
          }
       @staticmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		str:
          return [len(SCREAMING_SNAKE_CASE_	) for x in Path(SCREAMING_SNAKE_CASE_	).open().readlines()]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict[str, torch.Tensor]:
          UpperCamelCase   :  List[str]						     =     torch.stack([x['input_ids'] for x in batch]	)
          UpperCamelCase   :  Any						     =     torch.stack([x['attention_mask'] for x in batch]	)
          UpperCamelCase   :  List[Any]						     =     torch.stack([x['decoder_input_ids'] for x in batch]	)
          UpperCamelCase   :  List[Any]						     =     (
              self.tokenizer.generator.pad_token_id
              if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	)
              else self.tokenizer.pad_token_id
          )
          UpperCamelCase   :  str						     =     (
              self.tokenizer.question_encoder.pad_token_id
              if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	)
              else self.tokenizer.pad_token_id
          )
          UpperCamelCase   :  str						     =     trim_batch(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase     ,					UpperCamelCase   :  str						     =     trim_batch(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     {
              'input_ids': source_ids,
              'attention_mask': source_mask,
              'decoder_input_ids': y,
          }
          return batch






__UpperCAmelCase	       =	getLogger(__name__)
def 		UpperCamelCase     (       snake_case__	:   List[List]     )     ->					Any:
   return list(itertools.chain.from_iterable(snake_case__     )     )
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					None:
   UpperCamelCase   :  Optional[int]						     =     get_git_info()
   save_json(snake_case__	,					os.path.join(snake_case__	,					'git_log.json'     )     )
def 		UpperCamelCase     (       snake_case__	:   int	,					snake_case__	:   int	,					snake_case__	:   Tuple=4	,					**snake_case__	:   List[str]     )     ->					int:
   with open(snake_case__	,					'w'     ) as f:
      json.dump(snake_case__	,					snake_case__	,					indent=snake_case__	,					**snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					int:
   with open(snake_case__     ) as f:
      return json.load(snake_case__     )
def 		UpperCamelCase     (       )     ->					List[str]:
   UpperCamelCase   :  List[Any]						     =     git.Repo(search_parent_directories=snake_case__     )
   UpperCamelCase   :  Dict						     =     {
       '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     )     ->					List:
   return list(map(snake_case__	,					snake_case__     )     )
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]     )     ->					List[Any]:
   with open(snake_case__	,					'wb'     ) as f:
      return pickle.dump(snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					Optional[Any]:

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

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

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

   def lower(snake_case__	:   Union[str, Any]     ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(snake_case__     )     )     )     )
def 		UpperCamelCase     (       snake_case__	:   Any	,					snake_case__	:   Dict     )     ->					Tuple:
   UpperCamelCase   :  Tuple						     =     normalize_answer(snake_case__     ).split()
   UpperCamelCase   :  int						     =     normalize_answer(snake_case__     ).split()
   UpperCamelCase   :  List[Any]						     =     Counter(snake_case__     ) & Counter(snake_case__     )
   UpperCamelCase   :  List[str]						     =     sum(common.values()     )
   if num_same == 0:
      return 0
   UpperCamelCase   :  int						     =     1.0 * num_same / len(snake_case__     )
   UpperCamelCase   :  Optional[Any]						     =     1.0 * num_same / len(snake_case__     )
   UpperCamelCase   :  str						     =     (2 * precision * recall) / (precision + recall)
   return fa
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   str     )     ->					List[str]:
   return normalize_answer(snake_case__     ) == normalize_answer(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]     )     ->					Dict:
   assert len(snake_case__     ) == len(snake_case__     )
   UpperCamelCase   :  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[int]     )     ->					List[str]:
   return model_prefix.startswith('rag'     )


def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple     )     ->					str:
   UpperCamelCase   :  List[Any]						     =     {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   UpperCamelCase   :  Optional[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
         UpperCamelCase   :  List[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 os

import evaluate
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase	       =	16
__UpperCAmelCase	       =	32
def 		UpperCamelCase     (       snake_case__	:   Accelerator	,					snake_case__	:   int = 16     )     ->					Dict:
   UpperCamelCase   :  str						     =     AutoTokenizer.from_pretrained('bert-base-cased'     )
   UpperCamelCase   :  str						     =     load_dataset('glue'	,					'mrpc'     )

   def tokenize_function(snake_case__	:   Dict     ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase   :  Optional[Any]						     =     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
   # starting with the main process first:
   with accelerator.main_process_first():
      UpperCamelCase   :  List[str]						     =     datasets.map(
          snake_case__	,					batched=snake_case__	,					remove_columns=['idx', 'sentence1', 'sentence2']	,					)

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

   def collate_fn(snake_case__	:   Tuple     ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCamelCase   :  List[str]						     =     128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         UpperCamelCase   :  str						     =     16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase   :  Dict						     =     8
      else:
         UpperCamelCase   :  Union[str, Any]						     =     None

      return tokenizer.pad(
          snake_case__	,					padding='longest'	,					max_length=snake_case__	,					pad_to_multiple_of=snake_case__	,					return_tensors='pt'	,					)

   # Instantiate dataloaders.
   UpperCamelCase   :  Optional[int]						     =     DataLoader(
       tokenized_datasets['train']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )
   UpperCamelCase   :  List[Any]						     =     DataLoader(
       tokenized_datasets['validation']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    __UpperCAmelCase	       =	mocked_dataloaders  # noqa: F811
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   List[Any]     )     ->					List[str]:
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'	,					snake_case__     ) == "1":
      UpperCamelCase   :  List[str]						     =     2
   # New Code #
   UpperCamelCase   :  Optional[int]						     =     int(args.gradient_accumulation_steps     )
   UpperCamelCase   :  List[str]						     =     int(args.local_sgd_steps     )
   # Initialize accelerator
   UpperCamelCase   :  Optional[Any]						     =     Accelerator(
       cpu=args.cpu	,					mixed_precision=args.mixed_precision	,					gradient_accumulation_steps=snake_case__     )
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCamelCase   :  Union[str, Any]						     =     config['lr']
   UpperCamelCase   :  Optional[int]						     =     int(config['num_epochs']     )
   UpperCamelCase   :  List[Any]						     =     int(config['seed']     )
   UpperCamelCase   :  List[str]						     =     int(config['batch_size']     )

   UpperCamelCase   :  Optional[Any]						     =     evaluate.load('glue'	,					'mrpc'     )

   set_seed(snake_case__     )
   UpperCamelCase     ,					UpperCamelCase   :  int						     =     get_dataloaders(snake_case__	,					snake_case__     )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase   :  Tuple						     =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,					return_dict=snake_case__     )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   UpperCamelCase   :  Dict						     =     model.to(accelerator.device     )

   # Instantiate optimizer
   UpperCamelCase   :  List[str]						     =     AdamW(params=model.parameters()	,					lr=snake_case__     )

   # Instantiate scheduler
   UpperCamelCase   :  Any						     =     get_linear_schedule_with_warmup(
       optimizer=snake_case__	,					num_warmup_steps=100	,					num_training_steps=(len(snake_case__     ) * num_epochs)	,					)

   # 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.
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  str						     =     accelerator.prepare(
       snake_case__	,					snake_case__	,					snake_case__	,					snake_case__	,					snake_case__     )

   # Now we train the model
   for epoch in range(snake_case__     ):
      model.train()
      with LocalSGD(
          accelerator=snake_case__	,					model=snake_case__	,					local_sgd_steps=snake_case__	,					enabled=local_sgd_steps is not None     ) as local_sgd:
         for step, batch in enumerate(snake_case__     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(snake_case__     ):
               UpperCamelCase   :  int						     =     model(**snake_case__     )
               UpperCamelCase   :  Union[str, Any]						     =     output.loss
               accelerator.backward(snake_case__     )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      model.eval()
      for step, batch in enumerate(snake_case__     ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device     )
         with torch.no_grad():
            UpperCamelCase   :  Dict						     =     model(**snake_case__     )
         UpperCamelCase   :  Any						     =     outputs.logits.argmax(dim=-1     )
         UpperCamelCase     ,					UpperCamelCase   :  Any						     =     accelerator.gather_for_metrics((predictions, batch['labels'])     )
         metric.add_batch(
             predictions=snake_case__	,					references=snake_case__	,					)

      UpperCamelCase   :  List[Any]						     =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""	,					snake_case__     )
def 		UpperCamelCase     (       )     ->					Dict:
   UpperCamelCase   :  Any						     =     argparse.ArgumentParser(description='Simple example of training script.'     )
   parser.add_argument(
       '--mixed_precision'	,					type=snake_case__	,					default=snake_case__	,					choices=['no', 'fp16', 'bf16', 'fp8']	,					help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.'	,					)
   # New Code #
   parser.add_argument(
       '--gradient_accumulation_steps'	,					type=snake_case__	,					default=1	,					help='The number of minibatches to be ran before gradients are accumulated.'	,					)
   parser.add_argument(
       '--local_sgd_steps'	,					type=snake_case__	,					default=8	,					help='Number of local SGD steps or None to disable local SGD'     )
   parser.add_argument('--cpu'	,					action='store_true'	,					help='If passed, will train on the CPU.'     )
   UpperCamelCase   :  str						     =     parser.parse_args()
   UpperCamelCase   :  Tuple						     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(snake_case__	,					snake_case__     )


if __name__ == "__main__":
    main()



