






"""simple docstring"""







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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowerCAmelCase_		:			List[Any]				   =					1_6
lowerCAmelCase_		:			Dict				   =					3_2






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase = 16						):







       '''simple docstring'''
       UpperCAmelCase					=	AutoTokenizer.from_pretrained("""bert-base-cased"""						)
       UpperCAmelCase					=	load_dataset("""glue"""		,					"""mrpc"""						)

       def tokenize_function(lowerCAmelCase						):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase					=	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
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCAmelCase					=	datasets.map(
                  lowerCAmelCase		,					batched=lowerCAmelCase		,					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					=	tokenized_datasets.rename_column("""label"""		,					"""labels"""						)

       def collate_fn(lowerCAmelCase						):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCAmelCase					=	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					=	16
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase					=	8
              else:
                     UpperCAmelCase					=	None

              return tokenizer.pad(
                  lowerCAmelCase		,					padding="""longest"""		,					max_length=lowerCAmelCase		,					pad_to_multiple_of=lowerCAmelCase		,					return_tensors="""pt"""		,					)

       # Instantiate dataloaders.
       UpperCAmelCase					=	DataLoader(
           tokenized_datasets["""train"""]		,					shuffle=lowerCAmelCase		,					collate_fn=lowerCAmelCase		,					batch_size=lowerCAmelCase						)
       UpperCAmelCase					=	DataLoader(
           tokenized_datasets["""validation"""]		,					shuffle=lowerCAmelCase		,					collate_fn=lowerCAmelCase		,					batch_size=lowerCAmelCase						)

       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

 lowerCAmelCase_		:			List[Any]				   =					mocked_dataloaders  # noqa: F811






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,					lowerCAmelCase						) == "1":
              UpperCAmelCase					=	2
       # Initialize accelerator
       UpperCAmelCase					=	Accelerator(cpu=args.cpu		,					mixed_precision=args.mixed_precision						)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase					=	config["""lr"""]
       UpperCAmelCase					=	int(config["""num_epochs"""]						)
       UpperCAmelCase					=	int(config["""seed"""]						)
       UpperCAmelCase					=	int(config["""batch_size"""]						)

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps		,					"""isdigit"""						):
              if args.checkpointing_steps == "epoch":
                     UpperCAmelCase					=	args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCAmelCase					=	int(args.checkpointing_steps						)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
       else:
              UpperCAmelCase					=	None

       set_seed(lowerCAmelCase						)

       UpperCAmelCase			,				UpperCAmelCase					=	get_dataloaders(lowerCAmelCase		,					lowerCAmelCase						)
       UpperCAmelCase					=	evaluate.load("""glue"""		,					"""mrpc"""						)

       # If the batch size is too big we use gradient accumulation
       UpperCAmelCase					=	1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCAmelCase					=	batch_size // MAX_GPU_BATCH_SIZE
              UpperCAmelCase					=	MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCAmelCase					=	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,					return_dict=lowerCAmelCase						)

       # 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					=	model.to(accelerator.device						)

       # Instantiate optimizer
       UpperCAmelCase					=	AdamW(params=model.parameters()		,					lr=lowerCAmelCase						)

       # Instantiate scheduler
       UpperCAmelCase					=	get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase		,					num_warmup_steps=100		,					num_training_steps=(len(lowerCAmelCase						) * num_epochs) // gradient_accumulation_steps		,					)

       # 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					=	accelerator.prepare(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCAmelCase					=	0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCAmelCase					=	0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''						)
                     accelerator.load_state(args.resume_from_checkpoint						)
                     UpperCAmelCase					=	os.path.basename(args.resume_from_checkpoint						)
              else:
                     # Get the most recent checkpoint
                     UpperCAmelCase					=	[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
                     dirs.sort(key=os.path.getctime						)
                     UpperCAmelCase					=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCAmelCase					=	os.path.splitext(lowerCAmelCase						)[0]

              if "epoch" in training_difference:
                     UpperCAmelCase					=	int(training_difference.replace("""epoch_"""		,					""""""						)						) + 1
                     UpperCAmelCase					=	None
              else:
                     UpperCAmelCase					=	int(training_difference.replace("""step_"""		,					""""""						)						)
                     UpperCAmelCase					=	resume_step // len(lowerCAmelCase						)
                     resume_step -= starting_epoch * len(lowerCAmelCase						)

    # Now we train the model
       for epoch in range(lowerCAmelCase		,					lowerCAmelCase						):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     UpperCAmelCase					=	accelerator.skip_first_batches(lowerCAmelCase		,					lowerCAmelCase						)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCAmelCase					=	train_dataloader
              for step, batch in enumerate(lowerCAmelCase						):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device						)
                     UpperCAmelCase					=	model(**lowerCAmelCase						)
                     UpperCAmelCase					=	outputs.loss
                     UpperCAmelCase					=	loss / gradient_accumulation_steps
                     accelerator.backward(lowerCAmelCase						)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(lowerCAmelCase		,					lowerCAmelCase						):
                            UpperCAmelCase					=	F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCAmelCase					=	os.path.join(args.output_dir		,					lowerCAmelCase						)
                                   accelerator.save_state(lowerCAmelCase						)

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

              UpperCAmelCase					=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''		,					lowerCAmelCase						)

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCAmelCase					=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCAmelCase					=	os.path.join(args.output_dir		,					lowerCAmelCase						)
                     accelerator.save_state(lowerCAmelCase						)






def 					_lowerCAmelCase				(					):







       '''simple docstring'''
       UpperCAmelCase					=	argparse.ArgumentParser(description="""Simple example of training script."""						)
       parser.add_argument(
           """--mixed_precision"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					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."""		,					)
       parser.add_argument("""--cpu"""		,					action="""store_true"""		,					help="""If passed, will train on the CPU."""						)
       parser.add_argument(
           """--checkpointing_steps"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""		,					)
       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(
           """--resume_from_checkpoint"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					help="""If the training should continue from a checkpoint folder."""		,					)
       UpperCAmelCase					=	parser.parse_args()
       UpperCAmelCase					=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(lowerCAmelCase		,					lowerCAmelCase						)


if __name__ == "__main__":
 main()








"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 lowerCAmelCase_		:			int				   =					None


lowerCAmelCase_		:			Tuple				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			List[Any]				   =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_		:			List[Any]				   =					{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase_		:			int				   =					{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
lowerCAmelCase_		:			List[Any]				   =					['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']





class      UpperCamelCase_       (				a_     ):
  _A				:    Union[str, Any] =		VOCAB_FILES_NAMES
  _A				:    Optional[int] =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A				:    Tuple =		PRETRAINED_VOCAB_FILES_MAP
  _A				:    Dict =		['input_ids', 'attention_mask']
  _A				:    Any =		MBartaaTokenizer

  _A				:    List[int] =		[]
  _A				:    List[int] =		[]



  def __init__(					self       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__="</s>"       ,						snake_case__="</s>"       ,						snake_case__="<s>"       ,						snake_case__="<unk>"       ,						snake_case__="<pad>"       ,						snake_case__="<mask>"       ,						**snake_case__       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	AddedToken(snake_case__       ,						lstrip=snake_case__       ,						rstrip=snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) else mask_token

         UpperCAmelCase					=	kwargs.get("""additional_special_tokens"""       ,						[] )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             snake_case__       ,						src_lang=snake_case__       ,						tgt_lang=snake_case__       ,						tokenizer_file=snake_case__       ,						eos_token=snake_case__       ,						sep_token=snake_case__       ,						cls_token=snake_case__       ,						unk_token=snake_case__       ,						pad_token=snake_case__       ,						mask_token=snake_case__       ,						**snake_case__       ,						)

         UpperCAmelCase					=	vocab_file
         UpperCAmelCase					=	False if not self.vocab_file else True

         UpperCAmelCase					=	{
             lang_code: self.convert_tokens_to_ids(snake_case__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         UpperCAmelCase					=	src_lang if src_lang is not None else """en_XX"""
         UpperCAmelCase					=	tgt_lang
         UpperCAmelCase					=	self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang )



  @property
  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         return self._src_lang



  @src_lang.setter
  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	List[int]:



         """simple docstring"""





         if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = "en_XX"       ,						snake_case__ = None       ,						snake_case__ = "ro_RO"       ,						**snake_case__       ,						)	->	BatchEncoding:



         """simple docstring"""





         UpperCAmelCase					=	src_lang
         UpperCAmelCase					=	tgt_lang
         return super().prepare_seqaseq_batch(snake_case__       ,						snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self )	->	int:



         """simple docstring"""





         return self.set_src_lang_special_tokens(self.src_lang )



  def 						UpperCamelCase_ (					self )	->	Optional[int]:



         """simple docstring"""





         return self.set_tgt_lang_special_tokens(self.tgt_lang )



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         UpperCAmelCase					=	[self.cur_lang_code_id]
         UpperCAmelCase					=	[self.eos_token_id]

         UpperCAmelCase					=	self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCAmelCase					=	self.convert_ids_to_tokens(self.suffix_tokens )

         UpperCAmelCase					=	processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,						self.prefix_tokens + self.suffix_tokens ) )       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         UpperCAmelCase					=	[self.cur_lang_code_id]
         UpperCAmelCase					=	[self.eos_token_id]

         UpperCAmelCase					=	self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCAmelCase					=	self.convert_ids_to_tokens(self.suffix_tokens )

         UpperCAmelCase					=	processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,						self.prefix_tokens + self.suffix_tokens ) )       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						**snake_case__ )	->	Dict:



         """simple docstring"""





         if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
         UpperCAmelCase					=	src_lang
         UpperCAmelCase					=	self(snake_case__       ,						add_special_tokens=snake_case__       ,						return_tensors=snake_case__       ,						**snake_case__ )
         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         UpperCAmelCase					=	tgt_lang_id
         return inputs



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	Tuple[str]:



         """simple docstring"""





         if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer.""" )

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

         if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
                copyfile(self.vocab_file       ,						snake_case__ )

         return (out_vocab_file,)
