

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class     _a						:


					'''simple docstring'''






					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			):
						A__							:    Union[str, Any]        =		torch.arange(self.height * self.width			)
						A__							:    Tuple        =		torch.stack(
						    [
						        pixel_indices % self.width,
						        torch.div(A__    ,       self.width    ,       rounding_mode="""trunc"""			),
						    ]    ,       axis=1    ,       )
						return coords






					@property
					def   __A			( self			):
						A__   ,   *A__							:    str        =		self.shape
						A__							:    Optional[Any]        =		int(np.prod(A__			)			)

						A__							:    Tuple        =		self.get_image_coords()
						A__							:    Any        =		torch.broadcast_to(coords.unsqueeze(0			)    ,       [batch_size * inner_batch_size, *coords.shape]			)
						A__							:    str        =		self.get_camera_rays(A__			)

						A__							:    List[Any]        =		rays.view(A__    ,       inner_batch_size * self.height * self.width    ,       2    ,       3			)

						return rays






					def   __A			( self    ,       A__			):
						A__   ,   *A__   ,   A__							:    Optional[int]        =		coords.shape
						assert n_coords == 2
						assert batch_size == self.origin.shape[0]

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

						A__							:    Dict        =		self.resolution()
						A__							:    int        =		self.fov()

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

						A__							:    List[str]        =		fracs.view(A__    ,       -1    ,       2			)
						A__							:    str        =		(
						    self.z.view(A__    ,       1    ,       3			)
						    + self.x.view(A__    ,       1    ,       3			) * fracs[:, :, :1]
						    + self.y.view(A__    ,       1    ,       3			) * fracs[:, :, 1:]
						)
						A__							:    Optional[Any]        =		directions / directions.norm(dim=-1    ,       keepdim=A__			)
						A__							:    Union[str, Any]        =		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 						UpperCamelCase					(lowercase_: int					)     ->      DifferentiableProjectiveCamera:
	A__							:    Optional[int]        =		[]
	A__							:    Union[str, Any]        =		[]
	A__							:    List[str]        =		[]
	A__							:    str        =		[]
	for theta in np.linspace(0    ,      2 * np.pi    ,      num=20					):
		A__							:    Optional[Any]        =		np.array([np.sin(lowercase_					), np.cos(lowercase_					), -0.5]					)
		z /= np.sqrt(np.sum(z**2					)					)
		A__							:    Optional[int]        =		-z * 4
		A__							:    Any        =		np.array([np.cos(lowercase_					), -np.sin(lowercase_					), 0.0]					)
		A__							:    Tuple        =		np.cross(lowercase_    ,      lowercase_					)
		origins.append(lowercase_					)
		xs.append(lowercase_					)
		ys.append(lowercase_					)
		zs.append(lowercase_					)
	return DifferentiableProjectiveCamera(
	    origin=torch.from_numpy(np.stack(lowercase_    ,      axis=0					)					).float()    ,      x=torch.from_numpy(np.stack(lowercase_    ,      axis=0					)					).float()    ,      y=torch.from_numpy(np.stack(lowercase_    ,      axis=0					)					).float()    ,      z=torch.from_numpy(np.stack(lowercase_    ,      axis=0					)					).float()    ,      width=lowercase_    ,      height=lowercase_    ,      x_fov=0.7    ,      y_fov=0.7    ,      shape=(1, len(lowercase_					))    ,      )






import argparse
from collections import defaultdict

import yaml


A_       : List[str]         =			'docs/source/en/_toctree.yml'

def 						UpperCamelCase					(lowercase_: Optional[int]					)     ->      List[str]:
	A__							:    Dict        =		defaultdict(lowercase_					)
	A__							:    Optional[int]        =		[]
	A__							:    Union[str, Any]        =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}					)
		else:
			new_doc_list.append(lowercase_					)

	A__							:    Optional[int]        =		new_doc_list
	A__							:    Optional[int]        =		[key for key, value in counts.items() if value > 1]

	A__							:    Optional[Any]        =		[]
	for duplicate_key in duplicates:
		A__							:    List[Any]        =		list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}					)
		if len(lowercase_					) > 1:
			raise ValueError(
			    f"""{duplicate_key} is present several times in the documentation table of content at """
			    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
			    """others."""					)
		# Only add this once
		new_doc.append({"""local""": duplicate_key, """title""": titles[0]}					)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]					)
	A__							:    Dict        =		sorted(lowercase_    ,      key=lambda lowercase_					: s["title"].lower()					)

	# "overview" gets special treatment and is always first
	if len(lowercase_					) > 1:
		raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""					)

	overview_doc.extend(lowercase_					)

	# Sort
	return overview_doc

def 						UpperCamelCase					(lowercase_: Tuple=False					)     ->      List[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    Dict        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    List[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    Union[str, Any]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    Dict        =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	A__							:    List[Any]        =		api_doc[scheduler_idx]["""sections"""]
	A__							:    Union[str, Any]        =		clean_doc_toc(lowercase_					)

	A__							:    Optional[int]        =		False
	if new_scheduler_doc != scheduler_doc:
		A__							:    List[Any]        =		True
		if overwrite:
			A__							:    Optional[int]        =		new_scheduler_doc

	if diff:
		if overwrite:
			A__							:    Tuple        =		api_doc
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
				f.write(yaml.dump(lowercase_    ,      allow_unicode=lowercase_					)					)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)

def 						UpperCamelCase					(lowercase_: Dict=False					)     ->      Optional[Any]:
	with open(lowercase_    ,      encoding="""utf-8"""					) as f:
		A__							:    int        =		yaml.safe_load(f.read()					)

	# Get to the API doc
	A__							:    Optional[Any]        =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	A__							:    List[str]        =		content[api_idx]["""sections"""]

	# Then to the model doc
	A__							:    List[Any]        =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	A__							:    Dict        =		False
	A__							:    Tuple        =		api_doc[pipeline_idx]["""sections"""]
	A__							:    Tuple        =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			A__							:    List[Any]        =		pipeline_doc["""section"""]
			A__							:    Dict        =		clean_doc_toc(lowercase_					)
			if overwrite:
				A__							:    Optional[Any]        =		new_sub_pipeline_doc
		new_pipeline_docs.append(lowercase_					)

	# sort overall pipeline doc
	A__							:    Optional[int]        =		clean_doc_toc(lowercase_					)

	if new_pipeline_docs != pipeline_docs:
		A__							:    int        =		True
		if overwrite:
			A__							:    List[Any]        =		new_pipeline_docs

	if diff:
		if overwrite:
			A__							:    Union[str, Any]        =		api_doc
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
				f.write(yaml.dump(lowercase_    ,      allow_unicode=lowercase_					)					)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)


if __name__ == "__main__":
	A_       : str         =			argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	A_       : str         =			parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)



