





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    List[Any]		):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




       @property
       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						Any   =						1
         __snake_case:						Dict   =						3
         __snake_case:						List[str]   =						(32, 32)

         __snake_case:						Any   =						floats_tensor((batch_size, num_channels) + sizes							,				rng=random.Random(0		)		).to(lowerCAmelCase__		)
         return image




       @property
       def 							UpperCAmelCase__ (  self :    Tuple		):
         torch.manual_seed(0		)
         __snake_case:						int   =						UNetaDConditionModel(
             block_out_channels=(32, 32, 64)							,				layers_per_block=2							,				sample_size=32							,				in_channels=7							,				out_channels=4							,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")							,				up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")							,				cross_attention_dim=32							,				attention_head_dim=8							,				use_linear_projection=lowerCAmelCase__							,				only_cross_attention=(True, True, False)							,				num_class_embeds=100							,				)
         return model




       @property
       def 							UpperCAmelCase__ (  self :    List[Any]		):
         torch.manual_seed(0		)
         __snake_case:						Optional[Any]   =						AutoencoderKL(
             block_out_channels=[32, 32, 64]							,				in_channels=3							,				out_channels=3							,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,				latent_channels=4							,				)
         return model




       @property
       def 							UpperCAmelCase__ (  self :    str		):
         torch.manual_seed(0		)
         __snake_case:						Any   =						CLIPTextConfig(
             bos_token_id=0							,				eos_token_id=2							,				hidden_size=32							,				intermediate_size=37							,				layer_norm_eps=1E-05							,				num_attention_heads=4							,				num_hidden_layers=5							,				pad_token_id=1							,				vocab_size=1_000							,				hidden_act="""gelu"""							,				projection_dim=512							,				)
         return CLIPTextModel(lowerCAmelCase__		)




       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						Optional[int]   =						"cpu"  # ensure determinism for the device-dependent torch.Generator
         __snake_case:						Union[str, Any]   =						self.dummy_cond_unet_upscale
         __snake_case:						Union[str, Any]   =						DDPMScheduler()
         __snake_case:						Optional[Any]   =						DDIMScheduler(prediction_type="""v_prediction"""		)
         __snake_case:						List[Any]   =						self.dummy_vae
         __snake_case:						List[Any]   =						self.dummy_text_encoder
         __snake_case:						Any   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

         __snake_case:						int   =						self.dummy_image.cpu().permute(0							,				2							,				3							,				1		)[0]
         __snake_case:						Dict   =						Image.fromarray(np.uinta(lowerCAmelCase__		)		).convert("""RGB"""		).resize((64, 64)		)

         # make sure here that pndm scheduler skips prk
         __snake_case:						Optional[Any]   =						StableDiffusionUpscalePipeline(
             unet=lowerCAmelCase__							,				low_res_scheduler=lowerCAmelCase__							,				scheduler=lowerCAmelCase__							,				vae=lowerCAmelCase__							,				text_encoder=lowerCAmelCase__							,				tokenizer=lowerCAmelCase__							,				max_noise_level=350							,				)
         __snake_case:						Any   =						sd_pipe.to(lowerCAmelCase__		)
         sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

         __snake_case:						str   =						"A painting of a squirrel eating a burger"
         __snake_case:						Optional[Any]   =						torch.Generator(device=lowerCAmelCase__		).manual_seed(0		)
         __snake_case:						Optional[int]   =						sd_pipe(
             [prompt]							,				image=lowerCAmelCase__							,				generator=lowerCAmelCase__							,				guidance_scale=6.0							,				noise_level=20							,				num_inference_steps=2							,				output_type="""np"""							,				)

         __snake_case:						Any   =						output.images

         __snake_case:						str   =						torch.Generator(device=lowerCAmelCase__		).manual_seed(0		)
         __snake_case:						Any   =						sd_pipe(
             [prompt]							,				image=lowerCAmelCase__							,				generator=lowerCAmelCase__							,				guidance_scale=6.0							,				noise_level=20							,				num_inference_steps=2							,				output_type="""np"""							,				return_dict=lowerCAmelCase__							,				)[0]

         __snake_case:						str   =						image[0, -3:, -3:, -1]
         __snake_case:						Tuple   =						image_from_tuple[0, -3:, -3:, -1]

         __snake_case:						str   =						low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         __snake_case:						Optional[int]   =						np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						List[str]   =						"cpu"  # ensure determinism for the device-dependent torch.Generator
         __snake_case:						Optional[Any]   =						self.dummy_cond_unet_upscale
         __snake_case:						str   =						DDPMScheduler()
         __snake_case:						Union[str, Any]   =						DDIMScheduler(prediction_type="""v_prediction"""		)
         __snake_case:						Optional[int]   =						self.dummy_vae
         __snake_case:						Any   =						self.dummy_text_encoder
         __snake_case:						Union[str, Any]   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

         __snake_case:						Optional[Any]   =						self.dummy_image.cpu().permute(0							,				2							,				3							,				1		)[0]
         __snake_case:						Any   =						Image.fromarray(np.uinta(lowerCAmelCase__		)		).convert("""RGB"""		).resize((64, 64)		)

         # make sure here that pndm scheduler skips prk
         __snake_case:						List[Any]   =						StableDiffusionUpscalePipeline(
             unet=lowerCAmelCase__							,				low_res_scheduler=lowerCAmelCase__							,				scheduler=lowerCAmelCase__							,				vae=lowerCAmelCase__							,				text_encoder=lowerCAmelCase__							,				tokenizer=lowerCAmelCase__							,				max_noise_level=350							,				)
         __snake_case:						Any   =						sd_pipe.to(lowerCAmelCase__		)
         sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

         __snake_case:						Dict   =						"A painting of a squirrel eating a burger"
         __snake_case:						int   =						sd_pipe(
             2 * [prompt]							,				image=2 * [low_res_image]							,				guidance_scale=6.0							,				noise_level=20							,				num_inference_steps=2							,				output_type="""np"""							,				)
         __snake_case:						str   =						output.images
         assert image.shape[0] == 2

         __snake_case:						Tuple   =						torch.Generator(device=lowerCAmelCase__		).manual_seed(0		)
         __snake_case:						int   =						sd_pipe(
             [prompt]							,				image=lowerCAmelCase__							,				generator=lowerCAmelCase__							,				num_images_per_prompt=2							,				guidance_scale=6.0							,				noise_level=20							,				num_inference_steps=2							,				output_type="""np"""							,				)
         __snake_case:						Optional[int]   =						output.images
         assert image.shape[0] == 2






       @unittest.skipIf(torch_device != """cuda"""							,				"""This test requires a GPU"""		)
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						str   =						self.dummy_cond_unet_upscale
         __snake_case:						Tuple   =						DDPMScheduler()
         __snake_case:						Dict   =						DDIMScheduler(prediction_type="""v_prediction"""		)
         __snake_case:						Optional[int]   =						self.dummy_vae
         __snake_case:						str   =						self.dummy_text_encoder
         __snake_case:						Optional[int]   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

         __snake_case:						Dict   =						self.dummy_image.cpu().permute(0							,				2							,				3							,				1		)[0]
         __snake_case:						int   =						Image.fromarray(np.uinta(lowerCAmelCase__		)		).convert("""RGB"""		).resize((64, 64)		)

         # put models in fp16, except vae as it overflows in fp16
         __snake_case:						Any   =						unet.half()
         __snake_case:						int   =						text_encoder.half()

         # make sure here that pndm scheduler skips prk
         __snake_case:						List[Any]   =						StableDiffusionUpscalePipeline(
             unet=lowerCAmelCase__							,				low_res_scheduler=lowerCAmelCase__							,				scheduler=lowerCAmelCase__							,				vae=lowerCAmelCase__							,				text_encoder=lowerCAmelCase__							,				tokenizer=lowerCAmelCase__							,				max_noise_level=350							,				)
         __snake_case:						int   =						sd_pipe.to(lowerCAmelCase__		)
         sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

         __snake_case:						Dict   =						"A painting of a squirrel eating a burger"
         __snake_case:						str   =						torch.manual_seed(0		)
         __snake_case:						Optional[Any]   =						sd_pipe(
             [prompt]							,				image=lowerCAmelCase__							,				generator=lowerCAmelCase__							,				num_inference_steps=2							,				output_type="""np"""							,				).images

         __snake_case:						List[str]   =						low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any		):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						List[Any]   =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-upscale/low_res_cat.png"""		)
         __snake_case:						Optional[int]   =						load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
             """/upsampled_cat.npy"""		)

         __snake_case:						Optional[int]   =						"stabilityai/stable-diffusion-x4-upscaler"
         __snake_case:						str   =						StableDiffusionUpscalePipeline.from_pretrained(lowerCAmelCase__		)
         pipe.to(lowerCAmelCase__		)
         pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
         pipe.enable_attention_slicing()

         __snake_case:						Union[str, Any]   =						"a cat sitting on a park bench"

         __snake_case:						Optional[Any]   =						torch.manual_seed(0		)
         __snake_case:						List[Any]   =						pipe(
             prompt=lowerCAmelCase__							,				image=lowerCAmelCase__							,				generator=lowerCAmelCase__							,				output_type="""np"""							,				)
         __snake_case:						int   =						output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image		).max() < 1E-3




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						Union[str, Any]   =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-upscale/low_res_cat.png"""		)
         __snake_case:						int   =						load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
             """/upsampled_cat_fp16.npy"""		)

         __snake_case:						Union[str, Any]   =						"stabilityai/stable-diffusion-x4-upscaler"
         __snake_case:						List[str]   =						StableDiffusionUpscalePipeline.from_pretrained(
             lowerCAmelCase__							,				torch_dtype=torch.floataa							,				)
         pipe.to(lowerCAmelCase__		)
         pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
         pipe.enable_attention_slicing()

         __snake_case:						Any   =						"a cat sitting on a park bench"

         __snake_case:						Any   =						torch.manual_seed(0		)
         __snake_case:						Any   =						pipe(
             prompt=lowerCAmelCase__							,				image=lowerCAmelCase__							,				generator=lowerCAmelCase__							,				output_type="""np"""							,				)
         __snake_case:						Dict   =						output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image		).max() < 5E-1






       def 							UpperCAmelCase__ (  self :    List[str]		):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __snake_case:						List[Any]   =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-upscale/low_res_cat.png"""		)

         __snake_case:						int   =						"stabilityai/stable-diffusion-x4-upscaler"
         __snake_case:						Union[str, Any]   =						StableDiffusionUpscalePipeline.from_pretrained(
             lowerCAmelCase__							,				torch_dtype=torch.floataa							,				)
         pipe.to(lowerCAmelCase__		)
         pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
         pipe.enable_attention_slicing(1		)
         pipe.enable_sequential_cpu_offload()

         __snake_case:						str   =						"a cat sitting on a park bench"

         __snake_case:						Union[str, Any]   =						torch.manual_seed(0		)
         __snake_case:						Optional[int]   =						pipe(
             prompt=lowerCAmelCase__							,				image=lowerCAmelCase__							,				generator=lowerCAmelCase__							,				num_inference_steps=5							,				output_type="""np"""							,				)

         __snake_case:						Optional[int]   =						torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9













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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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	:	Any		     =       16
UpperCAmelCase	:	str		     =       32
def      _A       (		SCREAMING_SNAKE_CASE     :   Accelerator       ,       SCREAMING_SNAKE_CASE     :   int = 16						):





  """simple docstring"""



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

  def tokenize_function(SCREAMING_SNAKE_CASE     :   List[Any]						):
    # max_length=None => use the model max length (it's actually the default)
    a__	:					int										=tokenizer(examples["sentence1"]       ,       examples["sentence2"]       ,       truncation=SCREAMING_SNAKE_CASE       ,       max_length=SCREAMING_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():
    a__	:					Dict										=datasets.map(
        SCREAMING_SNAKE_CASE       ,       batched=SCREAMING_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
  a__	:					Dict										=tokenized_datasets.rename_column("label"       ,       "labels"						)

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

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

  # Instantiate dataloaders.
  a__	:					Any										=DataLoader(
      tokenized_datasets["train"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_SNAKE_CASE						)
  a__	:					int										=DataLoader(
      tokenized_datasets["validation"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_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

     UpperCAmelCase	:	str		     =       mocked_dataloaders  # noqa: F811
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



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

  a__	:					int										=evaluate.load("glue"       ,       "mrpc"						)

  # If the batch size is too big we use gradient accumulation
  a__	:					int										=1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    a__	:					Dict										=batch_size // MAX_GPU_BATCH_SIZE
    a__	:					Tuple										=MAX_GPU_BATCH_SIZE

  set_seed(SCREAMING_SNAKE_CASE						)
  a__					,							a__	:					Optional[int]										=get_dataloaders(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a__	:					List[str]										=AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,       return_dict=SCREAMING_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).
  a__	:					List[str]										=model.to(accelerator.device						)

  # Instantiate optimizer
  a__	:					List[Any]										=AdamW(params=model.parameters()       ,       lr=SCREAMING_SNAKE_CASE						)

  # Instantiate scheduler
  a__	:					Optional[int]										=get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE       ,       num_warmup_steps=100       ,       num_training_steps=(len(SCREAMING_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.
  a__					,							a__					,							a__					,							a__					,							a__	:					Optional[int]										=accelerator.prepare(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # Now we train the model
  for epoch in range(SCREAMING_SNAKE_CASE						):
    model.train()
    for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      a__	:					Dict										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					List[Any]										=outputs.loss
      a__	:					List[str]										=loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    a__	:					Optional[Any]										=0
    for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      with torch.no_grad():
        a__	:					Any										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					str										=outputs.logits.argmax(dim=-1						)
      a__					,							a__	:					List[str]										=accelerator.gather((predictions, batch["labels"])						)
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(SCREAMING_SNAKE_CASE						) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          a__	:					Optional[Any]										=predictions[: len(eval_dataloader.dataset						) - samples_seen]
          a__	:					Dict										=references[: len(eval_dataloader.dataset						) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=SCREAMING_SNAKE_CASE       ,       references=SCREAMING_SNAKE_CASE       ,       )

    a__	:					Tuple										=metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		):





  """simple docstring"""



  a__	:					List[str]										=argparse.ArgumentParser(description="Simple example of training script."						)
  parser.add_argument(
      "--mixed_precision"       ,       type=SCREAMING_SNAKE_CASE       ,       default=SCREAMING_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."						)
  a__	:					str										=parser.parse_args()
  a__	:					Optional[int]										={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()






