

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch






@dataclass
class 	UpperCAmelCase__		:






  """simple docstring"""


  a									= 42  # [batch_size x 3]
  a									= 42  # [batch_size x 3]
  a									= 42  # [batch_size x 3]
  a									= 42  # [batch_size x 3]
  a									= 42
  a									= 42
  a									= 42
  a									= 42
  a									= 42






  def 				lowercase_		(							self						:		Optional[Any] )		->							Tuple:
       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 				lowercase_		(							self						:		Dict )		->							Optional[Any]:
       return torch.from_numpy(np.array([self.width, self.height]     ,       dtype=np.floataa ) )






  def 				lowercase_		(							self						:		Any )		->							List[str]:
       return torch.from_numpy(np.array([self.x_fov, self.y_fov]     ,       dtype=np.floataa ) )






  def 				lowercase_		(							self						:		Union[str, Any] )		->							torch.Tensor:
       SCREAMING_SNAKE_CASE__   =   torch.arange(self.height * self.width )
       SCREAMING_SNAKE_CASE__   =   torch.stack(
           [
               pixel_indices % self.width,
               torch.div(__lowerCamelCase     ,       self.width     ,       rounding_mode='''trunc''' ),
           ]     ,       axis=1     ,       )
       return coords






  @property
  def 				lowercase_		(							self						:		Dict )		->							List[Any]:
       SCREAMING_SNAKE_CASE__,*SCREAMING_SNAKE_CASE__   =   self.shape
       SCREAMING_SNAKE_CASE__   =   int(np.prod(__lowerCamelCase ) )

       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(__lowerCamelCase )

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

       return rays






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







  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		int )		->							"DifferentiableProjectiveCamera":
       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=__lowerCamelCase     ,       height=__lowerCamelCase     ,       x_fov=self.x_fov     ,       y_fov=self.y_fov     ,       )



def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     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(_A    ), np.cos(_A    ), -0.5]    )
          z /= np.sqrt(np.sum(z**2    )    )
          SCREAMING_SNAKE_CASE__   =   -z * 4
          SCREAMING_SNAKE_CASE__   =   np.array([np.cos(_A    ), -np.sin(_A    ), 0.0]    )
          SCREAMING_SNAKE_CASE__   =   np.cross(_A				,		_A    )
          origins.append(_A    )
          xs.append(_A    )
          ys.append(_A    )
          zs.append(_A    )
     return DifferentiableProjectiveCamera(
         origin=torch.from_numpy(np.stack(_A				,		axis=0    )    ).float()				,		x=torch.from_numpy(np.stack(_A				,		axis=0    )    ).float()				,		y=torch.from_numpy(np.stack(_A				,		axis=0    )    ).float()				,		z=torch.from_numpy(np.stack(_A				,		axis=0    )    ).float()				,		width=_A				,		height=_A				,		x_fov=0.7				,		y_fov=0.7				,		shape=(1, len(_A    ))				,		)





import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_SCREAMING_SNAKE_CASE					:  Dict             =      '''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION	)
class 	UpperCAmelCase__		(	datasets.Metric	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
                   '''references''': {
                       '''id''': datasets.Value('''string''' ),
                       '''answers''': datasets.features.Sequence(
                           {
                               '''text''': datasets.Value('''string''' ),
                               '''answer_start''': datasets.Value('''int32''' ),
                           } ),
                   },
               } )     ,       codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       )







  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
       SCREAMING_SNAKE_CASE__   =   [
           {
               '''paragraphs''': [
                   {
                       '''qas''': [
                           {
                               '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                               '''id''': ref['''id'''],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       SCREAMING_SNAKE_CASE__   =   evaluate(dataset=__lowerCamelCase     ,       predictions=__lowerCamelCase )
       return score


