'''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
#
########################################################################

A_     :						Union[str, Any]    							=16
A_     :						List[Any]    							=32



def snake_case_      (					__snake_case  :							Accelerator	,   __snake_case  :							int = 16)			->					int:
   lowerCAmelCase_			=      AutoTokenizer.from_pretrained('''bert-base-cased''')
   lowerCAmelCase_			=      load_dataset('''glue'''	,   '''mrpc''')

   def tokenize_function(__snake_case  :							str):
      # max_length=None => use the model max length (it's actually the default)
      lowerCAmelCase_			=      tokenizer(examples['''sentence1''']	,   examples['''sentence2''']	,   truncation=__snake_case	,   max_length=__snake_case)
      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(
          __snake_case	,   batched=__snake_case	,   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(__snake_case  :							Optional[int]):
      # 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(
          __snake_case	,   padding='''longest'''	,   max_length=__snake_case	,   pad_to_multiple_of=__snake_case	,   return_tensors='''pt'''	,   )

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

   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_     :						str    							=mocked_dataloaders  # noqa: F811



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							List[str])			->					Dict:




   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,   __snake_case) == "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(__snake_case)

   lowerCAmelCase_	,lowerCAmelCase_			=      get_dataloaders(__snake_case	,   __snake_case)
   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=__snake_case)

   # 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=__snake_case)

   # Instantiate scheduler
   lowerCAmelCase_			=      get_linear_schedule_with_warmup(
       optimizer=__snake_case	,   num_warmup_steps=100	,   num_training_steps=(len(__snake_case) * 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(
       __snake_case	,   __snake_case	,   __snake_case	,   __snake_case	,   __snake_case)

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

   # Now we train the model
   for epoch in range(__snake_case):
      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(__snake_case):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device)
         lowerCAmelCase_			=      model(**__snake_case)
         lowerCAmelCase_			=      outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         lowerCAmelCase_			=      loss / gradient_accumulation_steps
         accelerator.backward(__snake_case)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(__snake_case):
         # 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(**__snake_case)
         lowerCAmelCase_			=      outputs.logits.argmax(dim=-1)
         lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.gather_for_metrics((predictions, batch['''labels''']))
         metric.add_batch(
             predictions=__snake_case	,   references=__snake_case	,   )

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

      # 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(__snake_case),
                 '''epoch''': epoch,
             }	,   step=__snake_case	,   )

    # 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 snake_case_      (					)			->					List[str]:
   lowerCAmelCase_			=      argparse.ArgumentParser(description='''Simple example of training script.''')
   parser.add_argument(
       '''--mixed_precision'''	,   type=__snake_case	,   default=__snake_case	,   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=__snake_case	,   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(__snake_case	,   __snake_case)


if __name__ == "__main__":
 main()







"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"AutoTokenizer"




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          super().__init__(lowercase_ ,							lowercase_				)
          UpperCAmelCase          =       self.image_processor




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Any:

          if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'				)

          if text is not None:
              UpperCAmelCase          =       self.tokenizer(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if images is not None:
              UpperCAmelCase          =       self.image_processor(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if text is not None and images is not None:
              UpperCAmelCase          =       image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowercase_				) ,							tensor_type=lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[int]:
          return self.tokenizer.decode(*lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]



