






import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowerCamelCase      :       Optional[int]    =						16
lowerCamelCase      :       Any    =						32





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case = 16							):
     __lowerCAmelCase             =       AutoTokenizer.from_pretrained("bert-base-cased"							)
     __lowerCAmelCase             =       load_dataset("glue"       ,			"mrpc"							)

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

     # Instantiate dataloaders.
     __lowerCAmelCase             =       DataLoader(
         tokenized_datasets["train"]       ,			shuffle=A__       ,			collate_fn=A__       ,			batch_size=A__							)
     __lowerCAmelCase             =       DataLoader(
         tokenized_datasets["validation"]       ,			shuffle=A__       ,			collate_fn=A__       ,			batch_size=A__							)

     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

 lowerCamelCase      :       Tuple    =						mocked_dataloaders  # noqa: F811





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     # For testing only
     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,			A__							) == "1":
          __lowerCAmelCase             =       2
     # Initialize accelerator
     __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"]							)

     __lowerCAmelCase             =       evaluate.load("glue"       ,			"mrpc"							)

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=A__							)
     def inner_training_loop(__snake_case							):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(A__							)

          # 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=A__							)

          # 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=A__							)
          __lowerCAmelCase             =       get_dataloaders(A__       ,			A__							)

          # Instantiate scheduler
          __lowerCAmelCase             =       get_linear_schedule_with_warmup(
              optimizer=A__       ,			num_warmup_steps=100       ,			num_training_steps=(len(A__							) * 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.
          __lowerCAmelCase             =       accelerator.prepare(
              A__       ,			A__       ,			A__       ,			A__       ,			A__							)

          # Now we train the model
          for epoch in range(A__							):
               model.train()
               for step, batch in enumerate(A__							):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device							)
                    __lowerCAmelCase             =       model(**A__							)
                    __lowerCAmelCase             =       outputs.loss
                    accelerator.backward(A__							)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               model.eval()
               for step, batch in enumerate(A__							):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device							)
                    with torch.no_grad():
                         __lowerCAmelCase             =       model(**A__							)
                    __lowerCAmelCase             =       outputs.logits.argmax(dim=-1							)
                    __lowerCAmelCase             =       accelerator.gather_for_metrics((predictions, batch["labels"])							)
                    metric.add_batch(
                        predictions=A__       ,			references=A__       ,			)

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision"       ,			type=A__       ,			default=A__       ,			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."							)
     __lowerCAmelCase             =       parser.parse_args()
     __lowerCAmelCase             =       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(A__       ,			A__							)


if __name__ == "__main__":
 main()










import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





