






'''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_					= 1_6
a_					= 3_2





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Optional[Any] = 1_6							):




 '''simple docstring'''






 SCREAMING_SNAKE_CASE__     :				Optional[Any]									=AutoTokenizer.from_pretrained('''bert-base-cased'''							)
 SCREAMING_SNAKE_CASE__     :				Any									=load_dataset('''glue''',	'''mrpc'''							)

 def tokenize_function(UpperCamelCase__				:						Tuple							):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE__     :				Optional[int]									=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():
  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=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
 SCREAMING_SNAKE_CASE__     :				Any									=tokenized_datasets.rename_column('''label''',	'''labels'''							)

 def collate_fn(UpperCamelCase__				:						Dict							):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   SCREAMING_SNAKE_CASE__     :				Tuple									=1_6
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE__     :				Any									=8
  else:
   SCREAMING_SNAKE_CASE__     :				Dict									=None

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

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE__     :				List[Any]									=DataLoader(
     tokenized_datasets['''train'''],	shuffle=lowerCAmelCase_,	collate_fn=lowerCAmelCase_,	batch_size=lowerCAmelCase_							)
 SCREAMING_SNAKE_CASE__     :				str									=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  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Union[str, Any]							):




 '''simple docstring'''






 if os.environ.get('''TESTING_MOCKED_DATALOADERS''',	lowerCAmelCase_							) == "1":
  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=2
 # New Code #
 SCREAMING_SNAKE_CASE__     :				List[str]									=int(args.gradient_accumulation_steps							)
 # Initialize accelerator
 SCREAMING_SNAKE_CASE__     :				List[str]									=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
 SCREAMING_SNAKE_CASE__     :				Tuple									=config['''lr''']
 SCREAMING_SNAKE_CASE__     :				Optional[int]									=int(config['''num_epochs''']							)
 SCREAMING_SNAKE_CASE__     :				Dict									=int(config['''seed''']							)
 SCREAMING_SNAKE_CASE__     :				Optional[Any]									=int(config['''batch_size''']							)

 SCREAMING_SNAKE_CASE__     :				List[str]									=evaluate.load('''glue''',	'''mrpc'''							)

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

 # Instantiate optimizer
 SCREAMING_SNAKE_CASE__     :				Any									=AdamW(params=model.parameters(),	lr=lowerCAmelCase_							)

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE__     :				List[Any]									=get_linear_schedule_with_warmup(
     optimizer=lowerCAmelCase_,	num_warmup_steps=1_0_0,	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.
 SCREAMING_SNAKE_CASE__     :				Tuple									=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_							):
    SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(**lowerCAmelCase_							)
    SCREAMING_SNAKE_CASE__     :				Optional[Any]									=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():
    SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(**lowerCAmelCase_							)
   SCREAMING_SNAKE_CASE__     :				List[Any]									=outputs.logits.argmax(dim=-1							)
   SCREAMING_SNAKE_CASE__     :				str									=accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
   metric.add_batch(
       predictions=lowerCAmelCase_,	references=lowerCAmelCase_,	)

  SCREAMING_SNAKE_CASE__     :				Tuple									=metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"epoch {epoch}:",	lowerCAmelCase_							)





def  _a(  ):




 '''simple docstring'''






 SCREAMING_SNAKE_CASE__     :				int									=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.'''							)
 SCREAMING_SNAKE_CASE__     :				List[Any]									=parser.parse_args()
 SCREAMING_SNAKE_CASE__     :				Union[str, Any]									={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
 training_function(lowerCAmelCase_,	lowerCAmelCase_							)


if __name__ == "__main__":
  main()







'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


a_					= logging.get_logger(__name__)

a_					= {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a_					= {
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

a_					= {
    'bert-base-uncased': 5_1_2,
    'bert-large-uncased': 5_1_2,
    'bert-base-cased': 5_1_2,
    'bert-large-cased': 5_1_2,
    'bert-base-multilingual-uncased': 5_1_2,
    'bert-base-multilingual-cased': 5_1_2,
    'bert-base-chinese': 5_1_2,
    'bert-base-german-cased': 5_1_2,
    'bert-large-uncased-whole-word-masking': 5_1_2,
    'bert-large-cased-whole-word-masking': 5_1_2,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 5_1_2,
    'bert-large-cased-whole-word-masking-finetuned-squad': 5_1_2,
    'bert-base-cased-finetuned-mrpc': 5_1_2,
    'bert-base-german-dbmdz-cased': 5_1_2,
    'bert-base-german-dbmdz-uncased': 5_1_2,
    'TurkuNLP/bert-base-finnish-cased-v1': 5_1_2,
    'TurkuNLP/bert-base-finnish-uncased-v1': 5_1_2,
    'wietsedv/bert-base-dutch-cased': 5_1_2,
}

a_					= {
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							VOCAB_FILES_NAMES
 snake_case_  							=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_  							=							PRETRAINED_INIT_CONFIGURATION
 snake_case_  							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_  							=							BertTokenizer




 def __init__(      self	:							int					,      __lowercase	:							Union[str, Any]=None					,      __lowercase	:							Tuple=None					,      __lowercase	:							str=True					,      __lowercase	:							Optional[Any]="[UNK]"					,      __lowercase	:							Tuple="[SEP]"					,      __lowercase	:							Any="[PAD]"					,      __lowercase	:							List[Any]="[CLS]"					,      __lowercase	:							Union[str, Any]="[MASK]"					,      __lowercase	:							Tuple=True					,      __lowercase	:							str=None					,      **__lowercase	:							Any					,      )	->						Optional[Any]:
       super().__init__(
           __lowercase					,      tokenizer_file=__lowercase					,      do_lower_case=__lowercase					,      unk_token=__lowercase					,      sep_token=__lowercase					,      pad_token=__lowercase					,      cls_token=__lowercase					,      mask_token=__lowercase					,      tokenize_chinese_chars=__lowercase					,      strip_accents=__lowercase					,      **__lowercase					,      )

       SCREAMING_SNAKE_CASE__     :				str									=json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
       if (
           normalizer_state.get('''lowercase'''					,      __lowercase      ) != do_lower_case
           or normalizer_state.get('''strip_accents'''					,      __lowercase      ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''					,      __lowercase      ) != tokenize_chinese_chars
       ):
             SCREAMING_SNAKE_CASE__     :				int									=getattr(__lowercase					,      normalizer_state.pop('''type'''      )      )
             SCREAMING_SNAKE_CASE__     :				Any									=do_lower_case
             SCREAMING_SNAKE_CASE__     :				Any									=strip_accents
             SCREAMING_SNAKE_CASE__     :				Dict									=tokenize_chinese_chars
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=normalizer_class(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=do_lower_case




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Union[str, Any]=None      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

       return output




 def      __magic_name__	(      self	:							Union[str, Any]					,      __lowercase	:							List[int]					,      __lowercase	:							Optional[List[int]] = None      )	->						List[int]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=[self.sep_token_id]
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[self.cls_token_id]
       if token_ids_a is None:
             return len(cls + token_ids_a + sep      ) * [0]
       return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							str					,      __lowercase	:							Optional[str] = None      )	->						Tuple[str]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self._tokenizer.model.save(__lowercase					,      name=__lowercase      )
       return tuple(__lowercase      )