


'''simple docstring'''


from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)






@flax.struct.dataclass
class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	jnp.ndarray
			lowerCAmelCase :	jnp.ndarray






class 	lowerCAmelCase__			(			nn.Module	):
			lowerCAmelCase :	int
			lowerCAmelCase :	Tuple[int]          =     (16, 32, 96, 256)
			lowerCAmelCase :	jnp.dtype          =     jnp.floataa




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      nn.Conv(
					    self.block_out_channels[0]	,  kernel_size=(3, 3)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

					_UpperCAmelCase		:       Tuple								=      []
					for i in range(len(self.block_out_channels ) - 1 ):
							_UpperCAmelCase		:       Union[str, Any]								=      self.block_out_channels[i]
							_UpperCAmelCase		:       List[Any]								=      self.block_out_channels[i + 1]
							_UpperCAmelCase		:       Optional[int]								=      nn.Conv(
							    lowerCamelCase__	,  kernel_size=(3, 3)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )
							blocks.append(lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      nn.Conv(
							    lowerCamelCase__	,  kernel_size=(3, 3)	,  strides=(2, 2)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )
							blocks.append(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      blocks

					_UpperCAmelCase		:       Optional[Any]								=      nn.Conv(
					    self.conditioning_embedding_channels	,  kernel_size=(3, 3)	,  padding=((1, 1), (1, 1))	,  kernel_init=nn.initializers.zeros_init()	,  bias_init=nn.initializers.zeros_init()	,  dtype=self.dtype	,  )






			def __call__(		self  :     Optional[Any]	,  lowerCamelCase__  :     Tuple )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      self.conv_in(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      nn.silu(lowerCamelCase__ )

					for block in self.blocks:
							_UpperCAmelCase		:       str								=      block(lowerCamelCase__ )
							_UpperCAmelCase		:       Union[str, Any]								=      nn.silu(lowerCamelCase__ )

					_UpperCAmelCase		:       Union[str, Any]								=      self.conv_out(lowerCamelCase__ )

					return embedding






@flax_register_to_config
class 	lowerCAmelCase__			(			nn.Module    ,       UpperCAmelCase__    ,       UpperCAmelCase__	):
			lowerCAmelCase :	int          =     32
			lowerCAmelCase :	int          =     4
			lowerCAmelCase :	Tuple[str]          =     (
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			lowerCAmelCase :	Union[bool, Tuple[bool]]          =     False
			lowerCAmelCase :	Tuple[int]          =     (320, 640, 1_280, 1_280)
			lowerCAmelCase :	int          =     2
			lowerCAmelCase :	Union[int, Tuple[int]]          =     8
			lowerCAmelCase :	Optional[Union[int, Tuple[int]]]          =     None
			lowerCAmelCase :	int          =     1_280
			lowerCAmelCase :	float          =     0.0
			lowerCAmelCase :	bool          =     False
			lowerCAmelCase :	jnp.dtype          =     jnp.floataa
			lowerCAmelCase :	bool          =     True
			lowerCAmelCase :	int          =     0
			lowerCAmelCase :	str          =     "rgb"
			lowerCAmelCase :	Tuple[int]          =     (16, 32, 96, 256)




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     jax.random.KeyArray )   ->FrozenDict:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      (1, self.in_channels, self.sample_size, self.sample_size)
					_UpperCAmelCase		:       Optional[Any]								=      jnp.zeros(lowerCamelCase__	,  dtype=jnp.floataa )
					_UpperCAmelCase		:       Tuple								=      jnp.ones((1,)	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Any								=      jnp.zeros((1, 1, self.cross_attention_dim)	,  dtype=jnp.floataa )
					_UpperCAmelCase		:       Optional[Any]								=      (1, 3, self.sample_size * 8, self.sample_size * 8)
					_UpperCAmelCase		:       int								=      jnp.zeros(lowerCamelCase__	,  dtype=jnp.floataa )

					_UpperCAmelCase		:       List[Any]								=      jax.random.split(lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      {"params": params_rng, "dropout": dropout_rng}

					return self.init(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )["params"]




			def 							lowerCAmelCase__    (		self  :     Dict )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.block_out_channels
					_UpperCAmelCase		:       List[Any]								=      block_out_channels[0] * 4

					# If `num_attention_heads` is not defined (which is the case for most models)
					# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
					# The reason for this behavior is to correct for incorrectly named variables that were introduced
					# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
					# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
					# which is why we correct for the naming here.
					_UpperCAmelCase		:       Union[str, Any]								=      self.num_attention_heads or self.attention_head_dim

					# input
					_UpperCAmelCase		:       Optional[Any]								=      nn.Conv(
					    block_out_channels[0]	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

					# time
					_UpperCAmelCase		:       Any								=      FlaxTimesteps(
					    block_out_channels[0]	,  flip_sin_to_cos=self.flip_sin_to_cos	,  freq_shift=self.config.freq_shift )
					_UpperCAmelCase		:       Optional[Any]								=      FlaxTimestepEmbedding(lowerCamelCase__	,  dtype=self.dtype )

					_UpperCAmelCase		:       Union[str, Any]								=      FlaxControlNetConditioningEmbedding(
					    conditioning_embedding_channels=block_out_channels[0]	,  block_out_channels=self.conditioning_embedding_out_channels	,  )

					_UpperCAmelCase		:       int								=      self.only_cross_attention
					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       str								=      (only_cross_attention,) * len(self.down_block_types )

					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Optional[int]								=      (num_attention_heads,) * len(self.down_block_types )

					# down
					_UpperCAmelCase		:       Dict								=      []
					_UpperCAmelCase		:       List[str]								=      []

					_UpperCAmelCase		:       Dict								=      block_out_channels[0]

					_UpperCAmelCase		:       List[Any]								=      nn.Conv(
					    lowerCamelCase__	,  kernel_size=(1, 1)	,  padding="VALID"	,  kernel_init=nn.initializers.zeros_init()	,  bias_init=nn.initializers.zeros_init()	,  dtype=self.dtype	,  )
					controlnet_down_blocks.append(lowerCamelCase__ )

					for i, down_block_type in enumerate(self.down_block_types ):
							_UpperCAmelCase		:       Dict								=      output_channel
							_UpperCAmelCase		:       Optional[Any]								=      block_out_channels[i]
							_UpperCAmelCase		:       int								=      i == len(lowerCamelCase__ ) - 1

							if down_block_type == "CrossAttnDownBlock2D":
									_UpperCAmelCase		:       Union[str, Any]								=      FlaxCrossAttnDownBlockaD(
									    in_channels=lowerCamelCase__	,  out_channels=lowerCamelCase__	,  dropout=self.dropout	,  num_layers=self.layers_per_block	,  num_attention_heads=num_attention_heads[i]	,  add_downsample=not is_final_block	,  use_linear_projection=self.use_linear_projection	,  only_cross_attention=only_cross_attention[i]	,  dtype=self.dtype	,  )
							else:
									_UpperCAmelCase		:       Union[str, Any]								=      FlaxDownBlockaD(
									    in_channels=lowerCamelCase__	,  out_channels=lowerCamelCase__	,  dropout=self.dropout	,  num_layers=self.layers_per_block	,  add_downsample=not is_final_block	,  dtype=self.dtype	,  )

							down_blocks.append(lowerCamelCase__ )

							for _ in range(self.layers_per_block ):
									_UpperCAmelCase		:       Dict								=      nn.Conv(
									    lowerCamelCase__	,  kernel_size=(1, 1)	,  padding="VALID"	,  kernel_init=nn.initializers.zeros_init()	,  bias_init=nn.initializers.zeros_init()	,  dtype=self.dtype	,  )
									controlnet_down_blocks.append(lowerCamelCase__ )

							if not is_final_block:
									_UpperCAmelCase		:       List[Any]								=      nn.Conv(
									    lowerCamelCase__	,  kernel_size=(1, 1)	,  padding="VALID"	,  kernel_init=nn.initializers.zeros_init()	,  bias_init=nn.initializers.zeros_init()	,  dtype=self.dtype	,  )
									controlnet_down_blocks.append(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      down_blocks
					_UpperCAmelCase		:       List[Any]								=      controlnet_down_blocks

					# mid
					_UpperCAmelCase		:       Tuple								=      block_out_channels[-1]
					_UpperCAmelCase		:       str								=      FlaxUNetMidBlockaDCrossAttn(
					    in_channels=lowerCamelCase__	,  dropout=self.dropout	,  num_attention_heads=num_attention_heads[-1]	,  use_linear_projection=self.use_linear_projection	,  dtype=self.dtype	,  )

					_UpperCAmelCase		:       Optional[int]								=      nn.Conv(
					    lowerCamelCase__	,  kernel_size=(1, 1)	,  padding="VALID"	,  kernel_init=nn.initializers.zeros_init()	,  bias_init=nn.initializers.zeros_init()	,  dtype=self.dtype	,  )






			def __call__(		self  :     Union[str, Any]	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     float = 1.0	,  lowerCamelCase__  :     bool = True	,  lowerCamelCase__  :     bool = False	,  )   ->Union[FlaxControlNetOutput, Tuple]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      self.controlnet_conditioning_channel_order
					if channel_order == "bgr":
							_UpperCAmelCase		:       Optional[Any]								=      jnp.flip(lowerCamelCase__	,  axis=1 )

					# 1. time
					if not isinstance(lowerCamelCase__	,  jnp.ndarray ):
							_UpperCAmelCase		:       Union[str, Any]								=      jnp.array([timesteps]	,  dtype=jnp.intaa )
					elif isinstance(lowerCamelCase__	,  jnp.ndarray ) and len(timesteps.shape ) == 0:
							_UpperCAmelCase		:       List[str]								=      timesteps.astype(dtype=jnp.floataa )
							_UpperCAmelCase		:       Optional[int]								=      jnp.expand_dims(lowerCamelCase__	,  0 )

					_UpperCAmelCase		:       Any								=      self.time_proj(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      self.time_embedding(lowerCamelCase__ )

					# 2. pre-process
					_UpperCAmelCase		:       List[Any]								=      jnp.transpose(lowerCamelCase__	,  (0, 2, 3, 1) )
					_UpperCAmelCase		:       Dict								=      self.conv_in(lowerCamelCase__ )

					_UpperCAmelCase		:       List[Any]								=      jnp.transpose(lowerCamelCase__	,  (0, 2, 3, 1) )
					_UpperCAmelCase		:       Optional[Any]								=      self.controlnet_cond_embedding(lowerCamelCase__ )
					sample += controlnet_cond

					# 3. down
					_UpperCAmelCase		:       int								=      (sample,)
					for down_block in self.down_blocks:
							if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
									_UpperCAmelCase		:       str								=      down_block(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  deterministic=not train )
							else:
									_UpperCAmelCase		:       List[str]								=      down_block(lowerCamelCase__	,  lowerCamelCase__	,  deterministic=not train )
							down_block_res_samples += res_samples

					# 4. mid
					_UpperCAmelCase		:       Optional[int]								=      self.mid_block(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  deterministic=not train )

					# 5. contronet blocks
					_UpperCAmelCase		:       List[str]								=      ()
					for down_block_res_sample, controlnet_block in zip(lowerCamelCase__	,  self.controlnet_down_blocks ):
							_UpperCAmelCase		:       Dict								=      controlnet_block(lowerCamelCase__ )
							controlnet_down_block_res_samples += (down_block_res_sample,)

					_UpperCAmelCase		:       Dict								=      controlnet_down_block_res_samples

					_UpperCAmelCase		:       Dict								=      self.controlnet_mid_block(lowerCamelCase__ )

					# 6. scaling
					_UpperCAmelCase		:       int								=      [sample * conditioning_scale for sample in down_block_res_samples]
					mid_block_res_sample *= conditioning_scale

					if not return_dict:
							return (down_block_res_samples, mid_block_res_sample)

					return FlaxControlNetOutput(
					    down_block_res_samples=lowerCamelCase__	,  mid_block_res_sample=lowerCamelCase__ )







'''simple docstring'''


import argparse
import gc
import json
import os

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.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase__    	=     16
lowerCamelCase__    	=     32






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return int(x / 2**20   )






class 	lowerCAmelCase__			:




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



					'''simple docstring'''







					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					_UpperCAmelCase		:       Tuple								=      torch.cuda.memory_allocated()
					return self






			def __exit__(		self  :     Tuple	,  *lowerCamelCase__  :     str )   ->int:



					'''simple docstring'''







					gc.collect()
					torch.cuda.empty_cache()
					_UpperCAmelCase		:       List[str]								=      torch.cuda.memory_allocated()
					_UpperCAmelCase		:       Tuple								=      torch.cuda.max_memory_allocated()
					_UpperCAmelCase		:       List[Any]								=      bamb(self.end - self.begin )
					_UpperCAmelCase		:       int								=      bamb(self.peak - self.begin )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = 16      , __lowerCAmelCase = "bert-base-cased"      , __lowerCAmelCase = 320      , __lowerCAmelCase = 160      , ):
		_UpperCAmelCase		:       int								=      AutoTokenizer.from_pretrained(__lowerCAmelCase   )
		_UpperCAmelCase		:       Any								=      load_dataset(
		    "glue"      , "mrpc"      , split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

		def tokenize_function(__lowerCAmelCase   ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCAmelCase		:       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
		_UpperCAmelCase		:       int								=      datasets.map(
		    __lowerCAmelCase      , batched=__lowerCAmelCase      , remove_columns=["idx", "sentence1", "sentence2"]      , load_from_cache_file=__lowerCAmelCase   )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		_UpperCAmelCase		:       List[Any]								=      tokenized_datasets.rename_column("label"      , "labels"   )

		def collate_fn(__lowerCAmelCase   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(__lowerCAmelCase      , padding="max_length"      , max_length=128      , return_tensors="pt"   )
				return tokenizer.pad(__lowerCAmelCase      , padding="longest"      , return_tensors="pt"   )

		# Instantiate dataloaders.
		_UpperCAmelCase		:       Any								=      DataLoader(
		    tokenized_datasets["train"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )
		_UpperCAmelCase		:       List[str]								=      DataLoader(
		    tokenized_datasets["validation"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )

		return train_dataloader, eval_dataloader






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		# Initialize accelerator
		_UpperCAmelCase		:       Union[str, Any]								=      Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_UpperCAmelCase		:       List[Any]								=      config["lr"]
		_UpperCAmelCase		:       List[Any]								=      int(config["num_epochs"]   )
		_UpperCAmelCase		:       int								=      int(config["seed"]   )
		_UpperCAmelCase		:       Union[str, Any]								=      int(config["batch_size"]   )
		_UpperCAmelCase		:       Tuple								=      args.model_name_or_path

		set_seed(__lowerCAmelCase   )
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      get_dataloaders(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , args.n_train      , args.n_val   )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCAmelCase		:       Optional[int]								=      AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase      , return_dict=__lowerCAmelCase   )

		# Instantiate optimizer
		_UpperCAmelCase		:       Dict								=      (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_UpperCAmelCase		:       str								=      optimizer_cls(params=model.parameters()      , lr=__lowerCAmelCase   )

		if accelerator.state.deepspeed_plugin is not None:
				_UpperCAmelCase		:       Any								=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				_UpperCAmelCase		:       Any								=      1
		_UpperCAmelCase		:       Optional[int]								=      (len(__lowerCAmelCase   ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_UpperCAmelCase		:       Tuple								=      get_linear_schedule_with_warmup(
				    optimizer=__lowerCAmelCase      , num_warmup_steps=0      , num_training_steps=__lowerCAmelCase      , )
		else:
				_UpperCAmelCase		:       Optional[Any]								=      DummyScheduler(__lowerCAmelCase      , total_num_steps=__lowerCAmelCase      , warmup_num_steps=0   )

		# 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		:       int								=      accelerator.prepare(
		    __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

		# We need to keep track of how many total steps we have iterated over
		_UpperCAmelCase		:       Union[str, Any]								=      0
		# We also need to keep track of the stating epoch so files are named properly
		_UpperCAmelCase		:       str								=      0

		# Now we train the model
		_UpperCAmelCase		:       Optional[Any]								=      {}
		for epoch in range(__lowerCAmelCase      , __lowerCAmelCase   ):
				with TorchTracemalloc() as tracemalloc:
						model.train()
						for step, batch in enumerate(__lowerCAmelCase   ):
								_UpperCAmelCase		:       Union[str, Any]								=      model(**__lowerCAmelCase   )
								_UpperCAmelCase		:       Union[str, Any]								=      outputs.loss
								_UpperCAmelCase		:       List[Any]								=      loss / gradient_accumulation_steps
								accelerator.backward(__lowerCAmelCase   )
								if step % gradient_accumulation_steps == 0:
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()

								overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
				accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
				accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
				accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
				accelerator.print(
				    "Total Peak Memory consumed during the train (max): {}".format(
				        tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
				_UpperCAmelCase		:       Optional[int]								=      tracemalloc.peaked + bamb(tracemalloc.begin   )
				if args.peak_memory_upper_bound is not None:
						assert (
						    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
						), "Peak memory usage exceeded the upper bound"

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir      , "peak_memory_utilization.json"   )      , "w"   ) as f:
						json.dump(__lowerCAmelCase      , __lowerCAmelCase   )






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Any								=      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
		parser.add_argument(
		    "--model_name_or_path"      , type=__lowerCAmelCase      , default="bert-base-cased"      , help="Path to pretrained model or model identifier from huggingface.co/models."      , required=__lowerCAmelCase      , )
		parser.add_argument(
		    "--output_dir"      , type=__lowerCAmelCase      , default="."      , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      , )
		parser.add_argument(
		    "--peak_memory_upper_bound"      , type=__lowerCAmelCase      , default=__lowerCAmelCase      , help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."      , )
		parser.add_argument(
		    "--n_train"      , type=__lowerCAmelCase      , default=320      , help="Number of training examples to use."      , )
		parser.add_argument(
		    "--n_val"      , type=__lowerCAmelCase      , default=160      , help="Number of validation examples to use."      , )
		parser.add_argument(
		    "--num_epochs"      , type=__lowerCAmelCase      , default=1      , help="Number of train epochs."      , )
		_UpperCAmelCase		:       Tuple								=      parser.parse_args()
		_UpperCAmelCase		:       Optional[Any]								=      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
		training_function(__lowerCAmelCase      , __lowerCAmelCase   )


if __name__ == "__main__":
						main()



