





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 				_UpperCamelCase       (						lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       List[str]       = KandinskyPipeline
       _UpperCamelCase					:       Any       = [
           '''prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
       ]
       _UpperCamelCase					:       Dict       = ['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
       _UpperCamelCase					:       Dict       = [
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       _UpperCamelCase					:       Dict       = False

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Tuple:




              """simple docstring"""


              return 32

       @property
       def lowercase			(		self:			Optional[Any]		)							->			Union[str, Any]:




              """simple docstring"""


              return 32

       @property
       def lowercase			(		self:			Optional[Any]		)							->			str:




              """simple docstring"""


              return self.time_input_dim

       @property
       def lowercase			(		self:			List[str]		)							->			List[Any]:




              """simple docstring"""


              return self.time_input_dim * 4

       @property
       def lowercase			(		self:			Optional[int]		)							->			Any:




              """simple docstring"""


              return 100

       @property
       def lowercase			(		self:			Any		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
              return tokenizer

       @property
       def lowercase			(		self:			int		)							->			Optional[int]:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_ 							=	MCLIPConfig(
                  numDims=self.cross_attention_dim       , transformerDimensions=self.text_embedder_hidden_size       , hidden_size=self.text_embedder_hidden_size       , intermediate_size=37       , num_attention_heads=4       , num_hidden_layers=5       , vocab_size=1005       , )

              UpperCamelCase_ 							=	MultilingualCLIP(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	text_encoder.eval()

              return text_encoder

       @property
       def lowercase			(		self:			str		)							->			List[str]:




              """simple docstring"""


              torch.manual_seed(0		)

              UpperCamelCase_ 							=	{
                  "in_channels": 4,
                  # Out channels is double in channels because predicts mean and variance
                  "out_channels": 8,
                  "addition_embed_type": "text_image",
                  "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
                  "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
                  "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
                  "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                  "layers_per_block": 1,
                  "encoder_hid_dim": self.text_embedder_hidden_size,
                  "encoder_hid_dim_type": "text_image_proj",
                  "cross_attention_dim": self.cross_attention_dim,
                  "attention_head_dim": 4,
                  "resnet_time_scale_shift": "scale_shift",
                  "class_embed_type": None,
              }

              UpperCamelCase_ 							=	UNetaDConditionModel(**_SCREAMING_SNAKE_CASE		)
              return model

       @property
       def lowercase			(		self:			Optional[Any]		)							->			str:




              """simple docstring"""


              return {
                  "block_out_channels": [32, 64],
                  "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                  "in_channels": 3,
                  "latent_channels": 4,
                  "layers_per_block": 1,
                  "norm_num_groups": 8,
                  "norm_type": "spatial",
                  "num_vq_embeddings": 12,
                  "out_channels": 3,
                  "up_block_types": [
                      "AttnUpDecoderBlock2D",
                      "UpDecoderBlock2D",
                  ],
                  "vq_embed_dim": 4,
              }

       @property
       def lowercase			(		self:			Any		)							->			Tuple:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_ 							=	VQModel(**self.dummy_movq_kwargs		)
              return model

       def lowercase			(		self:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.dummy_text_encoder
              UpperCamelCase_ 							=	self.dummy_tokenizer
              UpperCamelCase_ 							=	self.dummy_unet
              UpperCamelCase_ 							=	self.dummy_movq

              UpperCamelCase_ 							=	DDIMScheduler(
                  num_train_timesteps=1000       , beta_schedule="linear"       , beta_start=0.0_00_85       , beta_end=0.0_12       , clip_sample=_SCREAMING_SNAKE_CASE       , set_alpha_to_one=_SCREAMING_SNAKE_CASE       , steps_offset=1       , prediction_type="epsilon"       , thresholding=_SCREAMING_SNAKE_CASE       , )

              UpperCamelCase_ 							=	{
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "unet": unet,
                  "scheduler": scheduler,
                  "movq": movq,
              }
              return components

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Any=0		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	floats_tensor((1, self.cross_attention_dim)       , rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	floats_tensor((1, self.cross_attention_dim)       , rng=random.Random(seed + 1		)		).to(_SCREAMING_SNAKE_CASE		)
              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_ 							=	torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "prompt": "horse",
                  "image_embeds": image_embeds,
                  "negative_image_embeds": negative_image_embeds,
                  "generator": generator,
                  "height": 64,
                  "width": 64,
                  "guidance_scale": 4.0,
                  "num_inference_steps": 2,
                  "output_type": "np",
              }
              return inputs



       def lowercase			(		self:			int		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	"cpu"

              UpperCamelCase_ 							=	self.get_dummy_components()

              UpperCamelCase_ 							=	self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	pipe.to(_SCREAMING_SNAKE_CASE		)

              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_ 							=	output.images

              UpperCamelCase_ 							=	pipe(
                  **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)       , return_dict=_SCREAMING_SNAKE_CASE       , )[0]

              UpperCamelCase_ 							=	image[0, -3:, -3:, -1]
              UpperCamelCase_ 							=	image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 64, 64, 3)

              UpperCamelCase_ 							=	np.array(
                  [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]		)

              assert (
                  np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
              assert (
                  np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



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

       def lowercase			(		self:			str		)							->			int:




              """simple docstring"""


              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()



       def lowercase			(		self:			Optional[int]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/kandinsky/kandinsky_text2img_cat_fp16.npy"		)

              UpperCamelCase_ 							=	KandinskyPriorPipeline.from_pretrained(
                  "kandinsky-community/kandinsky-2-1-prior"       , torch_dtype=torch.floataa		)
              pipe_prior.to(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"       , torch_dtype=torch.floataa		)
              UpperCamelCase_ 							=	pipeline.to(_SCREAMING_SNAKE_CASE		)
              pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	"red cat, 4k photo"

              UpperCamelCase_ 							=	torch.Generator(device="cuda"		).manual_seed(0		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	pipe_prior(
                  _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , num_inference_steps=5       , negative_prompt=""       , ).to_tuple()

              UpperCamelCase_ 							=	torch.Generator(device="cuda"		).manual_seed(0		)
              UpperCamelCase_ 							=	pipeline(
                  _SCREAMING_SNAKE_CASE       , image_embeds=_SCREAMING_SNAKE_CASE       , negative_image_embeds=_SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , num_inference_steps=100       , output_type="np"       , )

              UpperCamelCase_ 							=	output.images[0]

              assert image.shape == (512, 512, 3)

              assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)








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 checkpointing 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	)  ->		Dict:
       UpperCamelCase_ 							=	AutoTokenizer.from_pretrained("bert-base-cased"	)
       UpperCamelCase_ 							=	load_dataset("glue"       ,		"mrpc"	)

       def tokenize_function(UpperCamelCase_	):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_ 							=	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():
              UpperCamelCase_ 							=	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
       UpperCamelCase_ 							=	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.
              UpperCamelCase_ 							=	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":
                     UpperCamelCase_ 							=	16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase_ 							=	8
              else:
                     UpperCamelCase_ 							=	None

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

       # Instantiate dataloaders.
       UpperCamelCase_ 							=	DataLoader(
           tokenized_datasets["train"]       ,		shuffle=UpperCamelCase_       ,		collate_fn=UpperCamelCase_       ,		batch_size=UpperCamelCase_	)
       UpperCamelCase_ 							=	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_	)  ->		Optional[Any]:
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,		UpperCamelCase_	) == "1":
              UpperCamelCase_ 							=	2
       # Initialize accelerator
       UpperCamelCase_ 							=	Accelerator(cpu=args.cpu       ,		mixed_precision=args.mixed_precision	)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCamelCase_ 							=	config["lr"]
       UpperCamelCase_ 							=	int(config["num_epochs"]	)
       UpperCamelCase_ 							=	int(config["seed"]	)
       UpperCamelCase_ 							=	int(config["batch_size"]	)

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps       ,		"isdigit"	):
              if args.checkpointing_steps == "epoch":
                     UpperCamelCase_ 							=	args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCamelCase_ 							=	int(args.checkpointing_steps	)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
       else:
              UpperCamelCase_ 							=	None

       set_seed(UpperCamelCase_	)

       UpperCamelCase_							,			UpperCamelCase_ 							=	get_dataloaders(UpperCamelCase_       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	evaluate.load("glue"       ,		"mrpc"	)

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCamelCase_ 							=	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).
       UpperCamelCase_ 							=	model.to(accelerator.device	)

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

       # Instantiate scheduler
       UpperCamelCase_ 							=	get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase_       ,		num_warmup_steps=100       ,		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.
       UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.prepare(
           UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCamelCase_ 							=	0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCamelCase_ 							=	0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
                     accelerator.load_state(args.resume_from_checkpoint	)
                     UpperCamelCase_ 							=	os.path.basename(args.resume_from_checkpoint	)
              else:
                     # Get the most recent checkpoint
                     UpperCamelCase_ 							=	[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
                     dirs.sort(key=os.path.getctime	)
                     UpperCamelCase_ 							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCamelCase_ 							=	os.path.splitext(UpperCamelCase_	)[0]

              if "epoch" in training_difference:
                     UpperCamelCase_ 							=	int(training_difference.replace("epoch_"       ,		""	)	) + 1
                     UpperCamelCase_ 							=	None
              else:
                     UpperCamelCase_ 							=	int(training_difference.replace("step_"       ,		""	)	)
                     UpperCamelCase_ 							=	resume_step // len(UpperCamelCase_	)
                     resume_step -= starting_epoch * len(UpperCamelCase_	)

    # Now we train the model
       for epoch in range(UpperCamelCase_       ,		UpperCamelCase_	):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     UpperCamelCase_ 							=	accelerator.skip_first_batches(UpperCamelCase_       ,		UpperCamelCase_	)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCamelCase_ 							=	train_dataloader
              for step, batch in enumerate(UpperCamelCase_	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.loss
                     UpperCamelCase_ 							=	loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase_	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(UpperCamelCase_       ,		UpperCamelCase_	):
                            UpperCamelCase_ 							=	F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                                   accelerator.save_state(UpperCamelCase_	)

              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():
                            UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.logits.argmax(dim=-1	)
                     UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.gather_for_metrics((predictions, batch["labels"])	)
                     metric.add_batch(
                         predictions=UpperCamelCase_       ,		references=UpperCamelCase_       ,		)

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

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCamelCase_ 							=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                     accelerator.save_state(UpperCamelCase_	)




def 			lowerCAmelCase_			(				)  ->		Dict:
       UpperCamelCase_ 							=	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(
           "--checkpointing_steps"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."       ,		)
       parser.add_argument(
           "--output_dir"       ,		type=UpperCamelCase_       ,		default="."       ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."       ,		)
       parser.add_argument(
           "--resume_from_checkpoint"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="If the training should continue from a checkpoint folder."       ,		)
       UpperCamelCase_ 							=	parser.parse_args()
       UpperCamelCase_ 							=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCamelCase_       ,		UpperCamelCase_	)


if __name__ == "__main__":
     main()

