'''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     							=1_6
__UpperCAmelCase     							=3_2






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__ = 16				)	->    Optional[Any]:
    __lowerCamelCase		=  AutoTokenizer.from_pretrained('''bert-base-cased'''				)
    __lowerCamelCase		=  load_dataset('''glue'''				,  '''mrpc'''				)

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

    def collate_fn(UpperCamelCase__				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __lowerCamelCase		=  1_28 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(
            UpperCamelCase__				,  padding='''longest'''				,  max_length=UpperCamelCase__				,  pad_to_multiple_of=UpperCamelCase__				,  return_tensors='''pt'''				,  )

    # Instantiate dataloaders.
    __lowerCamelCase		=  DataLoader(
        tokenized_datasets['''train''']				,  shuffle=UpperCamelCase__				,  collate_fn=UpperCamelCase__				,  batch_size=UpperCamelCase__				)
    __lowerCamelCase		=  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     							=mocked_dataloaders  # noqa: F811






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Any:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,  UpperCamelCase__				) == "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(UpperCamelCase__				)

    __lowerCamelCase	,  __lowerCamelCase		=  get_dataloaders(UpperCamelCase__				,  UpperCamelCase__				)
    __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=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).
    __lowerCamelCase		=  model.to(accelerator.device				)

    # Instantiate optimizer
    __lowerCamelCase		=  AdamW(params=model.parameters()				,  lr=UpperCamelCase__				)

    # Instantiate scheduler
    __lowerCamelCase		=  get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase__				,  num_warmup_steps=1_00				,  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.
    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  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:
        __lowerCamelCase		=  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:
            __lowerCamelCase		=  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				)
            __lowerCamelCase		=  model(**UpperCamelCase__				)
            __lowerCamelCase		=  outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            __lowerCamelCase		=  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():
                __lowerCamelCase		=  model(**UpperCamelCase__				)
            __lowerCamelCase		=  outputs.logits.argmax(dim=-1				)
            __lowerCamelCase	,  __lowerCamelCase		=  accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
            metric.add_batch(
                predictions=UpperCamelCase__				,  references=UpperCamelCase__				,  )

        __lowerCamelCase		=  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 __lowerCAmelCase (							)	->    List[Any]:
    __lowerCamelCase		=  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'''				,  )
    __lowerCamelCase		=  parser.parse_args()
    __lowerCamelCase		=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    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() and is_transformers_version(""">=""", """4.25.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel






