"""simple docstring"""




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch
@dataclass
class 						__A							:
     _UpperCamelCase							:			torch.Tensor  # [batch_size x 3]
     _UpperCamelCase							:			torch.Tensor  # [batch_size x 3]
     _UpperCamelCase							:			torch.Tensor  # [batch_size x 3]
     _UpperCamelCase							:			torch.Tensor  # [batch_size x 3]
     _UpperCamelCase							:			int
     _UpperCamelCase							:			int
     _UpperCamelCase							:			float
     _UpperCamelCase							:			float
     _UpperCamelCase							:			Tuple[int]






     def     __A  (		self  ):
       assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
       assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
       assert len(self.x.shape  ) == len(self.y.shape  ) == len(self.z.shape  ) == len(self.origin.shape  ) == 2






     def     __A  (		self  ):
       return torch.from_numpy(np.array([self.width, self.height]				,     dtype=np.floataa  )  )






     def     __A  (		self  ):
       return torch.from_numpy(np.array([self.x_fov, self.y_fov]				,     dtype=np.floataa  )  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     torch.arange(self.height * self.width  )
       _lowerCAmelCase						:       Dict  			=     torch.stack(
           [
               pixel_indices % self.width,
               torch.div(a__				,     self.width				,     rounding_mode="""trunc"""  ),
           ]				,     axis=1				,     )
       return coords






     @property
     def     __A  (		self  ):
       _lowerCAmelCase			,	*_lowerCAmelCase						:       Dict  			=     self.shape
       _lowerCAmelCase						:       int  			=     int(np.prod(a__  )  )

       _lowerCAmelCase						:       List[Any]  			=     self.get_image_coords()
       _lowerCAmelCase						:       str  			=     torch.broadcast_to(coords.unsqueeze(0  )				,     [batch_size * inner_batch_size, *coords.shape]  )
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_camera_rays(a__  )

       _lowerCAmelCase						:       str  			=     rays.view(a__				,     inner_batch_size * self.height * self.width				,     2				,     3  )

       return rays






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase			,	*_lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       _lowerCAmelCase						:       Optional[Any]  			=     coords.view(a__				,     -1				,     2  )

       _lowerCAmelCase						:       Optional[Any]  			=     self.resolution()
       _lowerCAmelCase						:       Any  			=     self.fov()

       _lowerCAmelCase						:       List[Any]  			=     (flat.float() / (res - 1)) * 2 - 1
       _lowerCAmelCase						:       Optional[Any]  			=     fracs * torch.tan(fov / 2  )

       _lowerCAmelCase						:       Optional[Any]  			=     fracs.view(a__				,     -1				,     2  )
       _lowerCAmelCase						:       Union[str, Any]  			=     (
           self.z.view(a__				,     1				,     3  )
           + self.x.view(a__				,     1				,     3  ) * fracs[:, :, :1]
           + self.y.view(a__				,     1				,     3  ) * fracs[:, :, 1:]
       )
       _lowerCAmelCase						:       List[Any]  			=     directions / directions.norm(dim=-1				,     keepdim=a__  )
       _lowerCAmelCase						:       List[str]  			=     torch.stack(
           [
               torch.broadcast_to(self.origin.view(a__				,     1				,     3  )				,     [batch_size, directions.shape[1], 3]  ),
               directions,
           ]				,     dim=2				,     )
       return rays.view(a__				,     *a__				,     2				,     3  )






     def     __A  (		self				,     a__				,     a__  ):
       assert width * self.height == height * self.width, "The aspect ratio should not change."
       return DifferentiableProjectiveCamera(
           origin=self.origin				,     x=self.x				,     y=self.y				,     z=self.z				,     width=a__				,     height=a__				,     x_fov=self.x_fov				,     y_fov=self.y_fov				,     )







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int	)       ->      DifferentiableProjectiveCamera:
  _lowerCAmelCase						:       List[Any]  			=     []
  _lowerCAmelCase						:       Union[str, Any]  			=     []
  _lowerCAmelCase						:       int  			=     []
  _lowerCAmelCase						:       Any  			=     []
  for theta in np.linspace(0			,2 * np.pi			,num=20	):
    _lowerCAmelCase						:       Union[str, Any]  			=     np.array([np.sin(_lowerCamelCase	), np.cos(_lowerCamelCase	), -0.5]	)
    z /= np.sqrt(np.sum(z**2	)	)
    _lowerCAmelCase						:       Optional[int]  			=     -z * 4
    _lowerCAmelCase						:       Dict  			=     np.array([np.cos(_lowerCamelCase	), -np.sin(_lowerCamelCase	), 0.0]	)
    _lowerCAmelCase						:       int  			=     np.cross(_lowerCamelCase			,_lowerCamelCase	)
    origins.append(_lowerCamelCase	)
    xs.append(_lowerCamelCase	)
    ys.append(_lowerCamelCase	)
    zs.append(_lowerCamelCase	)
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(_lowerCamelCase			,axis=0	)	).float()			,x=torch.from_numpy(np.stack(_lowerCamelCase			,axis=0	)	).float()			,y=torch.from_numpy(np.stack(_lowerCamelCase			,axis=0	)	).float()			,z=torch.from_numpy(np.stack(_lowerCamelCase			,axis=0	)	).float()			,width=_lowerCamelCase			,height=_lowerCamelCase			,x_fov=0.7			,y_fov=0.7			,shape=(1, len(_lowerCamelCase	))			,)












import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_ :						Union[str, Any]      =      16
UpperCAmelCase_ :						int      =      32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Accelerator	,			__magic_name__					:  int = 16	,			__magic_name__					:  str = "bert-base-cased"					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	AutoTokenizer.from_pretrained(__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	load_dataset("""glue"""	,			"""mrpc"""					)

	def tokenize_function(__magic_name__					:  Tuple					):
		# max_length=None => use the model max length (it's actually the default)
		UpperCamelCase     :List[Any]          =	tokenizer(examples["""sentence1"""]	,			examples["""sentence2"""]	,			truncation=__magic_name__	,			max_length=__magic_name__					)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	UpperCamelCase     :List[Any]          =	datasets.map(
	    __magic_name__	,			batched=__magic_name__	,			remove_columns=["""idx""", """sentence1""", """sentence2"""]	,			load_from_cache_file=__magic_name__					)

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

	def collate_fn(__magic_name__					:  Any					):
		# 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(__magic_name__	,			padding="""max_length"""	,			max_length=128	,			return_tensors="""pt"""					)
		return tokenizer.pad(__magic_name__	,			padding="""longest"""	,			return_tensors="""pt"""					)

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

	return train_dataloader, eval_dataloader

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Optional[Any]					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	Accelerator()

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

	set_seed(__magic_name__					)
	UpperCamelCase     ,      UpperCamelCase     :Dict          =	get_dataloaders(__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCamelCase     :List[str]          =	AutoModelForSequenceClassification.from_pretrained(__magic_name__	,			return_dict=__magic_name__					)

	# Instantiate optimizer
	UpperCamelCase     :Union[str, Any]          =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	UpperCamelCase     :Optional[Any]          =	optimizer_cls(params=model.parameters()	,			lr=__magic_name__					)

	if accelerator.state.deepspeed_plugin is not None:
		UpperCamelCase     :Any          =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		UpperCamelCase     :Any          =	1
	UpperCamelCase     :Dict          =	(len(__magic_name__					) * 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     :List[Any]          =	get_linear_schedule_with_warmup(
		    optimizer=__magic_name__	,			num_warmup_steps=0	,			num_training_steps=__magic_name__	,			)
	else:
		UpperCamelCase     :Any          =	DummyScheduler(__magic_name__	,			total_num_steps=__magic_name__	,			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     :str          =	accelerator.prepare(
	    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)

	# We need to keep track of how many total steps we have iterated over
	UpperCamelCase     :int          =	0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCamelCase     :Tuple          =	0

	# Now we train the model
	UpperCamelCase     :Any          =	evaluate.load("""glue"""	,			"""mrpc"""					)
	UpperCamelCase     :Tuple          =	0
	UpperCamelCase     :List[Any]          =	{}
	for epoch in range(__magic_name__	,			__magic_name__					):
		model.train()
		for step, batch in enumerate(__magic_name__					):
			UpperCamelCase     :List[str]          =	model(**__magic_name__					)
			UpperCamelCase     :Dict          =	outputs.loss
			UpperCamelCase     :Optional[int]          =	loss / gradient_accumulation_steps
			accelerator.backward(__magic_name__					)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		UpperCamelCase     :str          =	0
		for step, batch in enumerate(__magic_name__					):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device					)
			with torch.no_grad():
				UpperCamelCase     :Optional[int]          =	model(**__magic_name__					)
			UpperCamelCase     :List[Any]          =	outputs.logits.argmax(dim=-1					)
			# It is slightly faster to call this once, than multiple times
			UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	accelerator.gather(
			    (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(__magic_name__					) - 1:
					UpperCamelCase     :Dict          =	predictions[: len(eval_dataloader.dataset					) - samples_seen]
					UpperCamelCase     :List[str]          =	references[: len(eval_dataloader.dataset					) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=__magic_name__	,			references=__magic_name__	,			)

		UpperCamelCase     :List[str]          =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,			__magic_name__					)
		UpperCamelCase     :Dict          =	eval_metric["""accuracy"""]

		if best_performance < eval_metric["accuracy"]:
			UpperCamelCase     :str          =	eval_metric["""accuracy"""]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

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

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
	parser.add_argument(
	    """--model_name_or_path"""	,			type=__magic_name__	,			default="""bert-base-cased"""	,			help="""Path to pretrained model or model identifier from huggingface.co/models."""	,			required=__magic_name__	,			)
	parser.add_argument(
	    """--output_dir"""	,			type=__magic_name__	,			default="""."""	,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,			)
	parser.add_argument(
	    """--performance_lower_bound"""	,			type=__magic_name__	,			default=__magic_name__	,			help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""	,			)
	parser.add_argument(
	    """--num_epochs"""	,			type=__magic_name__	,			default=3	,			help="""Number of train epochs."""	,			)
	UpperCamelCase     :str          =	parser.parse_args()
	UpperCamelCase     :Any          =	{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(__magic_name__	,			__magic_name__					)


if __name__ == "__main__":
	main()
