


"""simple docstring"""






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      torch.Tensor  # [batch_size x 3]
				_lowerCAmelCase      :      torch.Tensor  # [batch_size x 3]
				_lowerCAmelCase      :      torch.Tensor  # [batch_size x 3]
				_lowerCAmelCase      :      torch.Tensor  # [batch_size x 3]
				_lowerCAmelCase      :      int
				_lowerCAmelCase      :      int
				_lowerCAmelCase      :      float
				_lowerCAmelCase      :      float
				_lowerCAmelCase      :      Tuple[int]



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



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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				torch.arange(self.height * self.width     )
								SCREAMING_SNAKE_CASE__     							=				torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(UpperCAmelCase__       ,		self.width       ,		rounding_mode="trunc"     ),
								    ]       ,		axis=1       ,		)
								return coords



				@property
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__     							=				self.shape
								SCREAMING_SNAKE_CASE__     							=				int(np.prod(UpperCAmelCase__     )     )

								SCREAMING_SNAKE_CASE__     							=				self.get_image_coords()
								SCREAMING_SNAKE_CASE__     							=				torch.broadcast_to(coords.unsqueeze(0     )       ,		[batch_size * inner_batch_size, *coords.shape]     )
								SCREAMING_SNAKE_CASE__     							=				self.get_camera_rays(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				rays.view(UpperCAmelCase__       ,		inner_batch_size * self.height * self.width       ,		2       ,		3     )

								return rays



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								SCREAMING_SNAKE_CASE__     							=				coords.view(UpperCAmelCase__       ,		-1       ,		2     )

								SCREAMING_SNAKE_CASE__     							=				self.resolution()
								SCREAMING_SNAKE_CASE__     							=				self.fov()

								SCREAMING_SNAKE_CASE__     							=				(flat.float() / (res - 1)) * 2 - 1
								SCREAMING_SNAKE_CASE__     							=				fracs * torch.tan(fov / 2     )

								SCREAMING_SNAKE_CASE__     							=				fracs.view(UpperCAmelCase__       ,		-1       ,		2     )
								SCREAMING_SNAKE_CASE__     							=				(
								    self.z.view(UpperCAmelCase__       ,		1       ,		3     )
								    + self.x.view(UpperCAmelCase__       ,		1       ,		3     ) * fracs[:, :, :1]
								    + self.y.view(UpperCAmelCase__       ,		1       ,		3     ) * fracs[:, :, 1:]
								)
								SCREAMING_SNAKE_CASE__     							=				directions / directions.norm(dim=-1       ,		keepdim=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				torch.stack(
								    [
								        torch.broadcast_to(self.origin.view(UpperCAmelCase__       ,		1       ,		3     )       ,		[batch_size, directions.shape[1], 3]     ),
								        directions,
								    ]       ,		dim=2       ,		)
								return rays.view(UpperCAmelCase__       ,		*UpperCAmelCase__       ,		2       ,		3     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								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=UpperCAmelCase__       ,		height=UpperCAmelCase__       ,		x_fov=self.x_fov       ,		y_fov=self.y_fov       ,		)


def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				for theta in np.linspace(0						,					2 * np.pi						,					num=20  ):
								SCREAMING_SNAKE_CASE__     							=				np.array([np.sin(lowerCamelCase_  ), np.cos(lowerCamelCase_  ), -0.5]  )
								z /= np.sqrt(np.sum(z**2  )  )
								SCREAMING_SNAKE_CASE__     							=				-z * 4
								SCREAMING_SNAKE_CASE__     							=				np.array([np.cos(lowerCamelCase_  ), -np.sin(lowerCamelCase_  ), 0.0]  )
								SCREAMING_SNAKE_CASE__     							=				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_  ))						,					)




"""simple docstring"""






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase     =  500000

_lowerCamelCase     ,     _lowerCamelCase     =  os.path.split(__file__)
_lowerCamelCase     =  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.map(**lowerCamelCase_  )


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.filter(**lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				{"num examples": SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE__     							=				datasets.Features({"text": datasets.Value("string"  ), "numbers": datasets.Value("float32"  )}  )
								SCREAMING_SNAKE_CASE__     							=				generate_example_dataset(
								    os.path.join(lowerCamelCase_						,					"dataset.arrow"  )						,					lowerCamelCase_						,					num_examples=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				transformers.AutoTokenizer.from_pretrained("bert-base-cased"						,					use_fast=lowerCamelCase_  )

								def tokenize(lowerCamelCase_  :      Union[str, Any]  ):
												return tokenizer(examples["text"]  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="numpy"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="pandas"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="torch"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="tensorflow"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				filter(lowerCamelCase_  )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_						,					"wb"  ) as f:
								f.write(json.dumps(lowerCamelCase_  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_map_filter()
