



"""simple docstring"""

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


a_       =       get_logger(__name__)







class  __lowercase  :







						"""simple docstring"""







						_A		:				Tuple											=		"""dummy_data"""
						_A		:				Tuple											=		"""datasets"""
						_A		:				List[Any]											=		False







						def __init__(self ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__ = None ,		lowercase__ = False ,		lowercase__ = True ,		lowercase__ = None ,		):
											snake_case_       :		Dict			       =       0
											snake_case_       :		Optional[int]			       =       dataset_name
											snake_case_       :		int			       =       cache_dir
											snake_case_       :		Optional[Any]			       =       use_local_dummy_data
											snake_case_       :		Tuple			       =       config
											# download_callbacks take a single url as input
											snake_case_       :		Union[str, Any]			       =       download_callbacks or []
											# if False, it doesn't load existing files and it returns the paths of the dummy files relative
											# to the dummy_data zip file root
											snake_case_       :		List[str]			       =       load_existing_dummy_data

											# TODO(PVP, QL) might need to make this more general
											snake_case_       :		str			       =       str(snake_case__							)
											# to be downloaded
											snake_case_       :		Union[str, Any]			       =       None
											snake_case_       :		Dict			       =       None







						@property
						def     __UpperCamelCase				(self							):

											if self._dummy_file is None:
																snake_case_       :		Dict			       =       self.download_dummy_data()
											return self._dummy_file







						@property
						def     __UpperCamelCase				(self							):

											if self.config is not None:
																# structure is dummy / config_name / version_name
																return os.path.join("""dummy""" ,		self.config.name ,		self.version_name							)
											# structure is dummy / version_name
											return os.path.join("""dummy""" ,		self.version_name							)







						@property
						def     __UpperCamelCase				(self							):
											return os.path.join(self.dummy_data_folder ,		"""dummy_data.zip"""							)







						def     __UpperCamelCase				(self							):
											snake_case_       :		Any			       =       (
											    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
											)

											snake_case_       :		str			       =       cached_path(
											    snake_case__ ,		cache_dir=self.cache_dir ,		extract_compressed_file=snake_case__ ,		force_extract=snake_case__							)

											return os.path.join(snake_case__ ,		self.dummy_file_name							)







						@property
						def     __UpperCamelCase				(self							):
											return os.path.join(self.datasets_scripts_dir ,		self.dataset_name ,		self.dummy_zip_file							)







						@property
						def     __UpperCamelCase				(self							):

											if self._bucket_url is None:
																snake_case_       :		Union[str, Any]			       =       hf_github_url(self.dataset_name ,		self.dummy_zip_file.replace(os.sep ,		"""/"""							)							)
											return self._bucket_url







						@property
						def     __UpperCamelCase				(self							):

											if os.path.isdir(self.dummy_file							):
																return self.dummy_file
											# else cut off path to file -> example `xsum`.
											return "/".join(self.dummy_file.replace(os.sep ,		"""/"""							).split("""/"""							)[:-1]							)







						def     __UpperCamelCase				(self ,		lowercase__ ,		*lowercase__							):

											if self.load_existing_dummy_data:
																# dummy data is downloaded and tested
																snake_case_       :		Any			       =       self.dummy_file
											else:
																# dummy data cannot be downloaded and only the path to dummy file is returned
																snake_case_       :		List[Any]			       =       self.dummy_file_name

											# special case when data_url is a dict
											if isinstance(snake_case__ ,		snake_case__							):
																return self.create_dummy_data_dict(snake_case__ ,		snake_case__							)
											elif isinstance(snake_case__ ,		(list, tuple)							):
																return self.create_dummy_data_list(snake_case__ ,		snake_case__							)
											else:
																return self.create_dummy_data_single(snake_case__ ,		snake_case__							)







						def     __UpperCamelCase				(self ,		lowercase__ ,		*lowercase__							):
											return self.download_and_extract(snake_case__							)







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__							):
											return self.download_and_extract(snake_case__							)







						def     __UpperCamelCase				(self ,		lowercase__ ,		*lowercase__ ,		**lowercase__							):
											return path







						def     __UpperCamelCase				(self							):
											return {}







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__							):
											snake_case_       :		List[str]			       =       {}
											for key, single_urls in data_url.items():
																for download_callback in self.download_callbacks:
																					if isinstance(snake_case__ ,		snake_case__							):
																										for single_url in single_urls:
																															download_callback(snake_case__							)
																					else:
																										snake_case_       :		Union[str, Any]			       =       single_urls
																										download_callback(snake_case__							)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
																if isinstance(snake_case__ ,		snake_case__							):
																					snake_case_       :		int			       =       [os.path.join(snake_case__ ,		urllib.parse.quote_plus(Path(snake_case__							).name							)							) for x in single_urls]
																else:
																					snake_case_       :		List[str]			       =       single_urls
																					snake_case_       :		Optional[int]			       =       os.path.join(snake_case__ ,		urllib.parse.quote_plus(Path(snake_case__							).name							)							)
																snake_case_       :		Tuple			       =       value

											# make sure that values are unique
											if all(isinstance(snake_case__ ,		snake_case__							) for i in dummy_data_dict.values()							) and len(set(dummy_data_dict.values()							)							) < len(
											    dummy_data_dict.values()							):
																# append key to value to make its name unique
																snake_case_       :		List[str]			       =       {key: value + key for key, value in dummy_data_dict.items()}

											return dummy_data_dict







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__							):
											snake_case_       :		Tuple			       =       []
											# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
											snake_case_       :		Union[str, Any]			       =       all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" ,		snake_case__							)							) for url in data_url							)
											snake_case_       :		Any			       =       all(
											    url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"""							) for url in data_url							)
											if data_url and (is_tf_records or is_pubmed_records):
																snake_case_       :		Optional[int]			       =       [data_url[0]] * len(snake_case__							)
											for single_url in data_url:
																for download_callback in self.download_callbacks:
																					download_callback(snake_case__							)
																# we force the name of each key to be the last file / folder name of the url path
																# if the url has arguments, we need to encode them with urllib.parse.quote_plus
																snake_case_       :		int			       =       os.path.join(snake_case__ ,		urllib.parse.quote_plus(single_url.split("""/"""							)[-1]							)							)
																dummy_data_list.append(snake_case__							)
											return dummy_data_list







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__							):

											for download_callback in self.download_callbacks:
																download_callback(snake_case__							)
											# we force the name of each key to be the last file / folder name of the url path
											# if the url has arguments, we need to encode them with urllib.parse.quote_plus
											snake_case_       :		Union[str, Any]			       =       os.path.join(snake_case__ ,		urllib.parse.quote_plus(data_url.split("""/"""							)[-1]							)							)
											if os.path.exists(snake_case__							) or not self.load_existing_dummy_data:
																return value
											else:
																# Backward compatibility, maybe deprecate at one point.
																# For many datasets with single url calls to dl_manager.download_and_extract,
																# the dummy_data.zip file is actually the zipped downloaded file
																# while now we expected the dummy_data.zip file to be a directory containing
																# the downloaded file.
																return path_to_dummy_data







						def     __UpperCamelCase				(self							):
											pass







						def     __UpperCamelCase				(self							):
											pass







						def     __UpperCamelCase				(self ,		lowercase__							):

											def _iter_archive_members(lowercase__							):
																# this preserves the order of the members inside the ZIP archive
																snake_case_       :		Tuple			       =       Path(self.dummy_file							).parent
																snake_case_       :		Optional[int]			       =       path.relative_to(snake_case__							)
																with ZipFile(self.local_path_to_dummy_data							) as zip_file:
																					snake_case_       :		Dict			       =       zip_file.namelist()
																for member in members:
																					if member.startswith(relative_path.as_posix()							):
																										yield dummy_parent_path.joinpath(snake_case__							)

											snake_case_       :		Tuple			       =       Path(snake_case__							)
											snake_case_       :		List[str]			       =       _iter_archive_members(snake_case__							) if self.use_local_dummy_data else path.rglob("""*"""							)
											for file_path in file_paths:
																if file_path.is_file() and not file_path.name.startswith((""".""", """__""")							):
																					yield file_path.relative_to(snake_case__							).as_posix(), file_path.open("""rb"""							)







						def     __UpperCamelCase				(self ,		lowercase__							):

											if not isinstance(snake_case__ ,		snake_case__							):
																snake_case_       :		str			       =       [paths]
											for path in paths:
																if os.path.isfile(snake_case__							):
																					if os.path.basename(snake_case__							).startswith((""".""", """__""")							):
																										return
																					yield path
																else:
																					for dirpath, dirnames, filenames in os.walk(snake_case__							):
																										if os.path.basename(snake_case__							).startswith((""".""", """__""")							):
																															continue
																										dirnames.sort()
																										for filename in sorted(snake_case__							):
																															if filename.startswith((""".""", """__""")							):
																																				continue
																															yield os.path.join(snake_case__ ,		snake_case__							)










from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class    lowercase_		:
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								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	:   Union[str, Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.arange(self.height * self.width  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case__					,							self.width					,							rounding_mode='trunc'  ),
								    ]					,							axis=1					,							)
								return coords



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

								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(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   torch.Tensor  ):

								"""simple docstring"""

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






						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   int  ):

								"""simple docstring"""

								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=snake_case__					,							height=snake_case__					,							x_fov=self.x_fov					,							y_fov=self.y_fov					,							)


def 					_a							(					lowerCAmelCase )->	DifferentiableProjectiveCamera:
		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 ))    ,						)