





"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__A											=						logging.getLogger(__name__)






@dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   42
       __magic_name__	:Dict						    =   42
       __magic_name__	:List[str]						    =   42






@dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:List[Any]						    =   42
       __magic_name__	:int						    =   42
       __magic_name__	:List[Any]						    =   None
       __magic_name__	:Any						    =   None






class    _lowerCAmelCase  (	_a			):
       """simple docstring"""




       __magic_name__	:List[str]						    =   """train"""
       __magic_name__	:Tuple						    =   """dev"""
       __magic_name__	:str						    =   """test"""






class    _lowerCAmelCase  :
       """simple docstring"""




       @staticmethod
       def 	snake_case		(       __UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         raise NotImplementedError






       @staticmethod
       def 	snake_case		(       __UpperCAmelCase ):



         '''simple docstring'''

         raise NotImplementedError






       @staticmethod
       def 	snake_case		(       __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase="[CLS]"       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase="[SEP]"       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=-1_0_0       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				{label: i for i, label in enumerate(snake_case_ )}

         lowerCAmelCase__     :List[Any]											=				[]
         for ex_index, example in enumerate(snake_case_ ):
           if ex_index % 1_0_0_0_0 == 0:
             logger.info('Writing example %d of %d'       ,					snake_case_       ,					len(snake_case_ ) )

           lowerCAmelCase__     :Tuple											=				[]
           lowerCAmelCase__     :Dict											=				[]
           for word, label in zip(example.words       ,					example.labels ):
             lowerCAmelCase__     :Any											=				tokenizer.tokenize(snake_case_ )

             # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
             if len(snake_case_ ) > 0:
               tokens.extend(snake_case_ )
               # Use the real label id for the first token of the word, and padding ids for the remaining tokens
               label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case_ ) - 1) )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
           lowerCAmelCase__     :Dict											=				tokenizer.num_special_tokens_to_add()
           if len(snake_case_ ) > max_seq_length - special_tokens_count:
             lowerCAmelCase__     :Tuple											=				tokens[: (max_seq_length - special_tokens_count)]
             lowerCAmelCase__     :Tuple											=				label_ids[: (max_seq_length - special_tokens_count)]

           # The convention in BERT is:
           # (a) For sequence pairs:
           #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
           #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
           # (b) For single sequences:
           #  tokens:   [CLS] the dog is hairy . [SEP]
           #  type_ids:   0   0   0   0  0     0   0
           #
           # Where "type_ids" are used to indicate whether this is the first
           # sequence or the second sequence. The embedding vectors for `type=0` and
           # `type=1` were learned during pre-training and are added to the wordpiece
           # embedding vector (and position vector). This is not *strictly* necessary
           # since the [SEP] token unambiguously separates the sequences, but it makes
           # it easier for the model to learn the concept of sequences.
           #
           # For classification tasks, the first vector (corresponding to [CLS]) is
           # used as the "sentence vector". Note that this only makes sense because
           # the entire model is fine-tuned.
           tokens += [sep_token]
           label_ids += [pad_token_label_id]
           if sep_token_extra:
             # roberta uses an extra separator b/w pairs of sentences
             tokens += [sep_token]
             label_ids += [pad_token_label_id]
           lowerCAmelCase__     :Dict											=				[sequence_a_segment_id] * len(snake_case_ )

           if cls_token_at_end:
             tokens += [cls_token]
             label_ids += [pad_token_label_id]
             segment_ids += [cls_token_segment_id]
           else:
             lowerCAmelCase__     :str											=				[cls_token] + tokens
             lowerCAmelCase__     :Union[str, Any]											=				[pad_token_label_id] + label_ids
             lowerCAmelCase__     :Any											=				[cls_token_segment_id] + segment_ids

           lowerCAmelCase__     :Union[str, Any]											=				tokenizer.convert_tokens_to_ids(snake_case_ )

           # The mask has 1 for real tokens and 0 for padding tokens. Only real
           # tokens are attended to.
           lowerCAmelCase__     :Tuple											=				[1 if mask_padding_with_zero else 0] * len(snake_case_ )

           # Zero-pad up to the sequence length.
           lowerCAmelCase__     :Dict											=				max_seq_length - len(snake_case_ )
           if pad_on_left:
             lowerCAmelCase__     :Optional[Any]											=				([pad_token] * padding_length) + input_ids
             lowerCAmelCase__     :Union[str, Any]											=				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
             lowerCAmelCase__     :Union[str, Any]											=				([pad_token_segment_id] * padding_length) + segment_ids
             lowerCAmelCase__     :Union[str, Any]											=				([pad_token_label_id] * padding_length) + label_ids
           else:
             input_ids += [pad_token] * padding_length
             input_mask += [0 if mask_padding_with_zero else 1] * padding_length
             segment_ids += [pad_token_segment_id] * padding_length
             label_ids += [pad_token_label_id] * padding_length

           assert len(snake_case_ ) == max_seq_length
           assert len(snake_case_ ) == max_seq_length
           assert len(snake_case_ ) == max_seq_length
           assert len(snake_case_ ) == max_seq_length

           if ex_index < 5:
             logger.info('*** Example ***' )
             logger.info('guid: %s'       ,					example.guid )
             logger.info('tokens: %s'       ,					' '.join([str(snake_case_ ) for x in tokens] ) )
             logger.info('input_ids: %s'       ,					' '.join([str(snake_case_ ) for x in input_ids] ) )
             logger.info('input_mask: %s'       ,					' '.join([str(snake_case_ ) for x in input_mask] ) )
             logger.info('segment_ids: %s'       ,					' '.join([str(snake_case_ ) for x in segment_ids] ) )
             logger.info('label_ids: %s'       ,					' '.join([str(snake_case_ ) for x in label_ids] ) )

           if "token_type_ids" not in tokenizer.model_input_names:
             lowerCAmelCase__     :Tuple											=				None

           features.append(
               InputFeatures(
                   input_ids=snake_case_       ,					attention_mask=snake_case_       ,					token_type_ids=snake_case_       ,					label_ids=snake_case_ ) )
         return features


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset






  class    _lowerCAmelCase  (	_a			):
         """simple docstring"""




         __magic_name__	:Optional[Any]						    =   42
         __magic_name__	:Optional[Any]						    =   nn.CrossEntropyLoss().ignore_index






         def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase = Split.train       ,					):



           '''simple docstring'''

           lowerCAmelCase__     :Tuple											=				os.path.join(
               snake_case_       ,					'cached_{}_{}_{}'.format(mode.value       ,					tokenizer.__class__.__name__       ,					str(snake_case_ ) )       ,					)

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           lowerCAmelCase__     :Dict											=				cached_features_file + """.lock"""
           with FileLock(snake_case_ ):
             if os.path.exists(snake_case_ ) and not overwrite_cache:
               logger.info(F"Loading features from cached file {cached_features_file}" )
               lowerCAmelCase__     :Tuple											=				torch.load(snake_case_ )
             else:
               logger.info(F"Creating features from dataset file at {data_dir}" )
               lowerCAmelCase__     :Any											=				token_classification_task.read_examples_from_file(snake_case_       ,					snake_case_ )
               # TODO clean up all this to leverage built-in features of tokenizers
               lowerCAmelCase__     :Any											=				token_classification_task.convert_examples_to_features(
                   snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					cls_token_at_end=bool(model_type in ['xlnet'] )       ,					cls_token=tokenizer.cls_token       ,					cls_token_segment_id=2 if model_type in ['xlnet'] else 0       ,					sep_token=tokenizer.sep_token       ,					sep_token_extra=snake_case_       ,					pad_on_left=bool(tokenizer.padding_side == 'left' )       ,					pad_token=tokenizer.pad_token_id       ,					pad_token_segment_id=tokenizer.pad_token_type_id       ,					pad_token_label_id=self.pad_token_label_id       ,					)
               logger.info(F"Saving features into cached file {cached_features_file}" )
               torch.save(self.features       ,					snake_case_ )






         def __len__(       self ):



           '''simple docstring'''

           return len(self.features )






         def __getitem__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           return self.features[i]


if is_tf_available():
  import tensorflow as tf






  class    _lowerCAmelCase  :
         """simple docstring"""




         __magic_name__	:Tuple						    =   42
         __magic_name__	:Any						    =   -100






         def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase = Split.train       ,					):



           '''simple docstring'''

           lowerCAmelCase__     :int											=				token_classification_task.read_examples_from_file(snake_case_       ,					snake_case_ )
           # TODO clean up all this to leverage built-in features of tokenizers
           lowerCAmelCase__     :Optional[Any]											=				token_classification_task.convert_examples_to_features(
               snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					cls_token_at_end=bool(model_type in ['xlnet'] )       ,					cls_token=tokenizer.cls_token       ,					cls_token_segment_id=2 if model_type in ['xlnet'] else 0       ,					sep_token=tokenizer.sep_token       ,					sep_token_extra=snake_case_       ,					pad_on_left=bool(tokenizer.padding_side == 'left' )       ,					pad_token=tokenizer.pad_token_id       ,					pad_token_segment_id=tokenizer.pad_token_type_id       ,					pad_token_label_id=self.pad_token_label_id       ,					)

           def gen():
             for ex in self.features:
               if ex.token_type_ids is None:
                 yield (
                     {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                     ex.label_ids,
                 )
               else:
                 yield (
                     {
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
                     },
                     ex.label_ids,
                 )

           if "token_type_ids" not in tokenizer.model_input_names:
             lowerCAmelCase__     :str											=				tf.data.Dataset.from_generator(
                 snake_case_       ,					({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)       ,					(
                     {'input_ids': tf.TensorShape([None] ), 'attention_mask': tf.TensorShape([None] )},
                     tf.TensorShape([None] ),
                 )       ,					)
           else:
             lowerCAmelCase__     :str											=				tf.data.Dataset.from_generator(
                 snake_case_       ,					({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)       ,					(
                     {
                         'input_ids': tf.TensorShape([None] ),
                         'attention_mask': tf.TensorShape([None] ),
                         'token_type_ids': tf.TensorShape([None] ),
                     },
                     tf.TensorShape([None] ),
                 )       ,					)






         def 	snake_case		(       self ):



           '''simple docstring'''

           lowerCAmelCase__     :Optional[Any]											=				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

           return self.dataset






         def __len__(       self ):



           '''simple docstring'''

           return len(self.features )






         def __getitem__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           return self.features[i]





'''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
# and perform 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
#
########################################################################


__a  			=						16
__a  			=						32





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase = 16						)						->					Optional[Any]:
      snake_case__    :       Optional[int]       		= AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      snake_case__    :       Optional[int]       		= load_dataset("""glue"""	,  """mrpc"""						)

      def tokenize_function(_lowerCAmelCase						):
            # max_length=None => use the model max length (it's actually the default)
            snake_case__    :       Union[str, Any]       		= 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():
            snake_case__    :       List[str]       		= 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
      snake_case__    :       Any       		= 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.
            snake_case__    :       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":
                  snake_case__    :       Optional[Any]       		= 16
            elif accelerator.mixed_precision != "no":
                  snake_case__    :       Tuple       		= 8
            else:
                  snake_case__    :       int       		= None

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

      # Instantiate dataloaders.
      snake_case__    :       List[Any]       		= DataLoader(
          tokenized_datasets["""train"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)
      snake_case__    :       Dict       		= 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

       __a  			=						mocked_dataloaders  # noqa: F811





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,  _lowerCAmelCase						) == "1":
            snake_case__    :       int       		= 2
      # New Code #
      snake_case__    :       Any       		= int(args.gradient_accumulation_steps						)
      # Initialize accelerator
      snake_case__    :       Any       		= Accelerator(
          cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  gradient_accumulation_steps=_lowerCAmelCase						)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case__    :       List[Any]       		= config["""lr"""]
      snake_case__    :       Optional[Any]       		= int(config["""num_epochs"""]						)
      snake_case__    :       Union[str, Any]       		= int(config["""seed"""]						)
      snake_case__    :       List[str]       		= int(config["""batch_size"""]						)

      snake_case__    :       Union[str, Any]       		= evaluate.load("""glue"""	,  """mrpc"""						)

      set_seed(_lowerCAmelCase						)
      snake_case__		,							snake_case__    :       Tuple       		= get_dataloaders(_lowerCAmelCase	,  _lowerCAmelCase						)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case__    :       Optional[int]       		= 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).
      snake_case__    :       Tuple       		= model.to(accelerator.device						)

      # Instantiate optimizer
      snake_case__    :       Any       		= AdamW(params=model.parameters()	,  lr=_lowerCAmelCase						)

      # Instantiate scheduler
      snake_case__    :       List[Any]       		= get_linear_schedule_with_warmup(
          optimizer=_lowerCAmelCase	,  num_warmup_steps=100	,  num_training_steps=(len(_lowerCAmelCase						) * 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.
      snake_case__		,							snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       List[Any]       		= accelerator.prepare(
          _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

      # Now we train the model
      for epoch in range(_lowerCAmelCase						):
            model.train()
            for step, batch in enumerate(_lowerCAmelCase						):
                  # 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(_lowerCAmelCase						):
                        snake_case__    :       Any       		= model(**_lowerCAmelCase						)
                        snake_case__    :       str       		= output.loss
                        accelerator.backward(_lowerCAmelCase						)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

            snake_case__    :       str       		= metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"	,  _lowerCAmelCase						)





def 		__snake_case(							)						->					List[str]:
      snake_case__    :       List[str]       		= 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."""	,  )
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,  type=_lowerCAmelCase	,  default=1	,  help="""The number of minibatches to be ran before gradients are accumulated."""	,  )
      parser.add_argument("""--cpu"""	,  action="""store_true"""	,  help="""If passed, will train on the CPU."""						)
      snake_case__    :       Tuple       		= parser.parse_args()
      snake_case__    :       Dict       		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase	,  _lowerCAmelCase						)


if __name__ == "__main__":
       main()
