"""simple docstring"""





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
#
########################################################################


_UpperCamelCase     :		Any											=					16
_UpperCamelCase     :		Tuple											=					32





def        a_  (   _lowerCAmelCase						:				Accelerator     ,				_lowerCAmelCase						:				int = 16  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			AutoTokenizer.from_pretrained('bert-base-cased'  )
       lowercase__    :	int     					=			load_dataset('glue'     ,				'mrpc'  )

       def tokenize_function(_lowerCAmelCase						:				List[Any]  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	List[Any]     					=			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():
              lowercase__    :	List[Any]     					=			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
       lowercase__    :	Dict     					=			tokenized_datasets.rename_column('label'     ,				'labels'  )

       def collate_fn(_lowerCAmelCase						:				List[Any]  ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase__    :	Optional[int]     					=			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":
                     lowercase__    :	Dict     					=			16
              elif accelerator.mixed_precision != "no":
                     lowercase__    :	List[Any]     					=			8
              else:
                     lowercase__    :	List[str]     					=			None

              return tokenizer.pad(
                  _lowerCAmelCase     ,				padding='longest'     ,				max_length=_lowerCAmelCase     ,				pad_to_multiple_of=_lowerCAmelCase     ,				return_tensors='pt'     ,				)

       # Instantiate dataloaders.
       lowercase__    :	Dict     					=			DataLoader(
           tokenized_datasets['train']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )
       lowercase__    :	Tuple     					=			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

       _UpperCamelCase     :		Any											=					mocked_dataloaders  # noqa: F811





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				int  ):





       '''simple docstring'''

       if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,				_lowerCAmelCase  ) == "1":
              lowercase__    :	int     					=			2
       # Initialize accelerator
       lowercase__    :	Optional[int]     					=			Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision  )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__    :	Dict     					=			config['lr']
       lowercase__    :	Optional[Any]     					=			int(config['num_epochs']  )
       lowercase__    :	List[Any]     					=			int(config['seed']  )
       lowercase__    :	Dict     					=			int(config['batch_size']  )

       lowercase__    :	str     					=			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=_lowerCAmelCase  )
       def inner_training_loop(_lowerCAmelCase						:				List[Any]  ):
              # 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(_lowerCAmelCase  )

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

              # Instantiate optimizer
              lowercase__    :	Union[str, Any]     					=			AdamW(params=model.parameters()     ,				lr=_lowerCAmelCase  )
              lowercase__					,   lowercase__    :	int     					=			get_dataloaders(_lowerCAmelCase     ,				_lowerCAmelCase  )

              # Instantiate scheduler
              lowercase__    :	int     					=			get_linear_schedule_with_warmup(
                  optimizer=_lowerCAmelCase     ,				num_warmup_steps=100     ,				num_training_steps=(len(_lowerCAmelCase  ) * 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.
              lowercase__					,   lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			accelerator.prepare(
                  _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

              # Now we train the model
              for epoch in range(_lowerCAmelCase  ):
                     model.train()
                     for step, batch in enumerate(_lowerCAmelCase  ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device  )
                            lowercase__    :	Union[str, Any]     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	Tuple     					=			outputs.loss
                            accelerator.backward(_lowerCAmelCase  )
                            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`.
                            batch.to(accelerator.device  )
                            with torch.no_grad():
                                   lowercase__    :	str     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	List[Any]     					=			outputs.logits.argmax(dim=-1  )
                            lowercase__					,   lowercase__    :	List[str]     					=			accelerator.gather_for_metrics((predictions, batch['labels'])  )
                            metric.add_batch(
                                predictions=_lowerCAmelCase     ,				references=_lowerCAmelCase     ,				)

                     lowercase__    :	Any     					=			metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(f"""epoch {epoch}:"""     ,				_lowerCAmelCase  )

    # 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        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			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.'  )
       lowercase__    :	Optional[int]     					=			parser.parse_args()
       lowercase__    :	Any     					=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(_lowerCAmelCase     ,				_lowerCAmelCase  )


if __name__ == "__main__":
       main()


"""simple docstring"""





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard


@slow
@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

