


'''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 experiment tracking 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
#
########################################################################

__snake_case    						=16
__snake_case    						=32





def 	a_					( lowerCamelCase	:     Accelerator	, lowerCamelCase	:     int = 16				):
   lowerCAmelCase     =			AutoTokenizer.from_pretrained('bert-base-cased'				)
   lowerCAmelCase     =			load_dataset('glue'	, 'mrpc'				)

   def tokenize_function(lowerCamelCase	:     Any				):
      # max_length=None => use the model max length (it's actually the default)
      lowerCAmelCase     =			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():
      lowerCAmelCase     =			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
   lowerCAmelCase     =			tokenized_datasets.rename_column('label'	, 'labels'				)

   def collate_fn(lowerCamelCase	:     List[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(
          lowerCamelCase	, padding='longest'	, max_length=lowerCamelCase	, pad_to_multiple_of=lowerCamelCase	, return_tensors='pt'	, )

   # Instantiate dataloaders.
   lowerCAmelCase     =			DataLoader(
       tokenized_datasets['train']	, shuffle=lowerCamelCase	, collate_fn=lowerCamelCase	, batch_size=lowerCamelCase				)
   lowerCAmelCase     =			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

       __snake_case    						=mocked_dataloaders  # noqa: F811





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     Tuple				):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'	, lowerCamelCase				) == "1":
      lowerCAmelCase     =			2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      lowerCAmelCase     =			Accelerator(
          cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with='all'	, project_dir=args.project_dir				)
   else:
      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']				)
   set_seed(lowerCamelCase				)

   lowerCAmelCase     ,  lowerCAmelCase     =			get_dataloaders(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			evaluate.load('glue'	, 'mrpc'				)

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

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

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

   # Instantiate scheduler
   lowerCAmelCase     =			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.
   lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			accelerator.prepare(
       lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      lowerCAmelCase     =			os.path.split(lowerCamelCase				)[-1].split('.'				)[0]
      accelerator.init_trackers(lowerCamelCase	, lowerCamelCase				)

   # Now we train the model
   for epoch in range(lowerCamelCase				):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         lowerCAmelCase     =			0
      for step, batch in enumerate(lowerCamelCase				):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device				)
         lowerCAmelCase     =			model(**lowerCamelCase				)
         lowerCAmelCase     =			outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         lowerCAmelCase     =			loss / gradient_accumulation_steps
         accelerator.backward(lowerCamelCase				)
         if step % gradient_accumulation_steps == 0:
            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` (the default).
         batch.to(accelerator.device				)
         with torch.no_grad():
            lowerCAmelCase     =			model(**lowerCamelCase				)
         lowerCAmelCase     =			outputs.logits.argmax(dim=-1				)
         lowerCAmelCase     ,  lowerCAmelCase     =			accelerator.gather_for_metrics((predictions, batch['labels'])				)
         metric.add_batch(
             predictions=lowerCamelCase	, references=lowerCamelCase	, )

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

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 'accuracy': eval_metric['accuracy'],
                 'f1': eval_metric['f1'],
                 'train_loss': total_loss.item() / len(lowerCamelCase				),
                 'epoch': epoch,
             }	, step=lowerCamelCase	, )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()





def 	a_					( ):
   lowerCAmelCase     =			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(
       '--with_tracking'	, action='store_true'	, help='Whether to load in all available experiment trackers from the environment and use them for logging.'	, )
   parser.add_argument(
       '--project_dir'	, type=lowerCamelCase	, default='logs'	, help='Location on where to store experiment tracking logs` and relevent project information'	, )
   lowerCAmelCase     =			parser.parse_args()
   lowerCAmelCase     =			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCamelCase	, lowerCamelCase				)


if __name__ == "__main__":
       main()









'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[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 + sep + token_ids_a + sep    ) * [0]





