






"""simple docstring"""





import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE					:				Any					    =						16
_SCREAMING_SNAKE_CASE					:				Tuple					    =						32







def      lowerCamelCase__       (							_lowerCamelCase       :		Accelerator		,				_lowerCamelCase       :		int = 16 )      ->				Union[str, Any]:
  lowerCamelCase_         =						AutoTokenizer.from_pretrained('bert-base-cased' )
  lowerCamelCase_         =						load_dataset('glue'		,				'mrpc' )

  def tokenize_function(_lowerCamelCase       :		Optional[Any] ):
    # max_length=None => use the model max length (it's actually the default)
    lowerCamelCase_         =						tokenizer(examples['sentence1']		,				examples['sentence2']		,				truncation=UpperCAmelCase_		,				max_length=UpperCAmelCase_ )
    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():
    lowerCamelCase_         =						datasets.map(
        UpperCAmelCase_		,				batched=UpperCAmelCase_		,				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
  lowerCamelCase_         =						tokenized_datasets.rename_column('label'		,				'labels' )

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

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

  # Instantiate dataloaders.
  lowerCamelCase_         =						DataLoader(
      tokenized_datasets['train']		,				shuffle=UpperCAmelCase_		,				collate_fn=UpperCAmelCase_		,				batch_size=UpperCAmelCase_ )
  lowerCamelCase_         =						DataLoader(
      tokenized_datasets['validation']		,				shuffle=UpperCAmelCase_		,				collate_fn=UpperCAmelCase_		,				batch_size=UpperCAmelCase_ )

  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

  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						mocked_dataloaders  # noqa: F811







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Union[str, Any] )      ->				Any:
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,				UpperCAmelCase_ ) == "1":
    lowerCamelCase_         =						2
  # Initialize accelerator
  lowerCamelCase_         =						Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCamelCase_         =						config['lr']
  lowerCamelCase_         =						int(config['num_epochs'] )
  lowerCamelCase_         =						int(config['seed'] )
  lowerCamelCase_         =						int(config['batch_size'] )

  lowerCamelCase_         =						evaluate.load('glue'		,				'mrpc' )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=UpperCAmelCase_ )
  def inner_training_loop(_lowerCamelCase       :		Any ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(UpperCAmelCase_ )

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

    # 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).
    lowerCamelCase_         =						model.to(accelerator.device )

    # Instantiate optimizer
    lowerCamelCase_         =						AdamW(params=model.parameters()		,				lr=UpperCAmelCase_ )
    lowerCamelCase_         =						get_dataloaders(UpperCAmelCase_		,				UpperCAmelCase_ )

    # Instantiate scheduler
    lowerCamelCase_         =						get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase_		,				num_warmup_steps=100		,				num_training_steps=(len(UpperCAmelCase_ ) * 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.
    lowerCamelCase_         =						accelerator.prepare(
        UpperCAmelCase_		,				UpperCAmelCase_		,				UpperCAmelCase_		,				UpperCAmelCase_		,				UpperCAmelCase_ )

    # Now we train the model
    for epoch in range(UpperCAmelCase_ ):
      model.train()
      for step, batch in enumerate(UpperCAmelCase_ ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device )
        lowerCamelCase_         =						model(**UpperCAmelCase_ )
        lowerCamelCase_         =						outputs.loss
        accelerator.backward(UpperCAmelCase_ )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()







def      lowerCamelCase__       (							)      ->				List[Any]:
  lowerCamelCase_         =						argparse.ArgumentParser(description='Simple example of training script.' )
  parser.add_argument(
      '--mixed_precision'		,				type=UpperCAmelCase_		,				default=UpperCAmelCase_		,				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.' )
  lowerCamelCase_         =						parser.parse_args()
  lowerCamelCase_         =						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(UpperCAmelCase_		,				UpperCAmelCase_ )


if __name__ == "__main__":
  main()








'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__		:					Tuple												=  logging.get_logger(__name__)

A__		:					List[str]												=  {"""vocab_file""": """spm_char.model"""}

A__		:					str												=  {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

A__		:					List[Any]												=  {
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[Any]							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Any							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Union[str, Any]							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					int							     =			['input_ids', 'attention_mask']







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     None:
         __lowerCamelCase   :	Any								=		{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE_			,     eos_token=SCREAMING_SNAKE_CASE_			,     unk_token=SCREAMING_SNAKE_CASE_			,     pad_token=SCREAMING_SNAKE_CASE_			,     sp_model_kwargs=self.sp_model_kwargs			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	List[Any]								=		vocab_file

         __lowerCamelCase   :	str								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_	)







    @property
    def       lowercase_				(							self	)				->     List[str]:
         return self.sp_model.get_piece_size()







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab







    def __getstate__(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Dict								=		self.__dict__.copy()
         __lowerCamelCase   :	int								=		None
         return state







    def __setstate__(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         __lowerCamelCase   :	Union[str, Any]								=		d

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

         __lowerCamelCase   :	Dict								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE_			,     out_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     str:
         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
         return token







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		[]
         __lowerCamelCase   :	str								=		''
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                   __lowerCamelCase   :	int								=		[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
         return out_string.strip()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[int]:
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = False	)				->     List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=SCREAMING_SNAKE_CASE_			,     token_ids_a=SCREAMING_SNAKE_CASE_			,     already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		[1]
         if token_ids_a is None:
              return ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones
         return ([0] * len(SCREAMING_SNAKE_CASE_	)) + ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
              copyfile(self.vocab_file			,     SCREAMING_SNAKE_CASE_	)
         elif not os.path.isfile(self.vocab_file	):
              with open(SCREAMING_SNAKE_CASE_			,     'wb'	) as fi:
                   __lowerCamelCase   :	str								=		self.sp_model.serialized_model_proto()
                   fi.write(SCREAMING_SNAKE_CASE_	)

         return (out_vocab_file,)






