



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

UpperCamelCase__  :			Tuple          =					16
UpperCamelCase__  :			Tuple          =					32



def   UpperCAmelCase			(  a_	,						a_ = 1_6						)					->    Dict:



   """simple docstring"""






   A_   :						Optional[int]		= AutoTokenizer.from_pretrained("""bert-base-cased"""						)
   A_   :						Optional[Any]		= load_dataset("""glue"""	,						"""mrpc"""						)

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

   def collate_fn(a_						):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_   :						Dict		= 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":
         A_   :						Optional[Any]		= 1_6
      elif accelerator.mixed_precision != "no":
         A_   :						Any		= 8
      else:
         A_   :						List[Any]		= None

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

   # Instantiate dataloaders.
   A_   :						Tuple		= DataLoader(
       tokenized_datasets["""train"""]	,						shuffle=_UpperCAmelCase	,						collate_fn=_UpperCAmelCase	,						batch_size=_UpperCAmelCase						)
   A_   :						Tuple		= 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

  UpperCamelCase__  :			List[Any]          =					mocked_dataloaders  # noqa: F811



def   UpperCAmelCase			(  a_	,						a_						)					->    str:



   """simple docstring"""






   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,						_UpperCAmelCase						) == "1":
      A_   :						Tuple		= 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:
      A_   :						Dict		= Accelerator(
          cpu=args.cpu	,						mixed_precision=args.mixed_precision	,						log_with="""all"""	,						project_dir=args.project_dir						)
   else:
      A_   :						List[str]		= Accelerator(cpu=args.cpu	,						mixed_precision=args.mixed_precision						)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_   :						List[str]		= config['''lr''']
   A_   :						Any		= int(config["""num_epochs"""]						)
   A_   :						Tuple		= int(config["""seed"""]						)
   A_   :						int		= int(config["""batch_size"""]						)
   set_seed(_UpperCAmelCase						)

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

   # If the batch size is too big we use gradient accumulation
   A_   :						Dict		= 1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_   :						List[str]		= batch_size // MAX_GPU_BATCH_SIZE
      A_   :						Dict		= MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_   :						int		= 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).
   A_   :						Optional[int]		= model.to(accelerator.device						)

   # Instantiate optimizer
   A_   :						Any		= AdamW(params=model.parameters()	,						lr=_UpperCAmelCase						)

   # Instantiate scheduler
   A_   :						Union[str, Any]		= get_linear_schedule_with_warmup(
       optimizer=_UpperCAmelCase	,						num_warmup_steps=1_0_0	,						num_training_steps=(len(_UpperCAmelCase						) * 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.
   A_   :						List[str]		= accelerator.prepare(
       _UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase						)

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

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

      A_   :						int		= metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"	,						_UpperCAmelCase						)

      # 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(_UpperCAmelCase						),
                 """epoch""": epoch,
             }	,						step=_UpperCAmelCase	,						)

    # 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   UpperCAmelCase			(  )					->    List[str]:



   """simple docstring"""






   A_   :						str		= 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."""						)
   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=_UpperCAmelCase	,						default="""logs"""	,						help="""Location on where to store experiment tracking logs` and relevent project information"""	,						)
   A_   :						str		= parser.parse_args()
   A_   :						Union[str, Any]		= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
   training_function(_UpperCAmelCase	,						_UpperCAmelCase						)


if __name__ == "__main__":
  main()





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
				from .pipeline_kandinsky import KandinskyPipeline
				from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
				from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
				from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
				from .text_encoder import MultilingualCLIP





