


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__UpperCamelCase				:  List[Any]      					=							'\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n'

__UpperCamelCase				:  Optional[int]      					=							'\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

__UpperCamelCase				:  List[Any]      					=							'\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n'





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






				'''simple docstring'''





				def 					_lowerCamelCase (	self				:   Optional[int]	):





							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,   citation=_CITATION       ,   inputs_description=_KWARGS_DESCRIPTION       ,   features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''       ,   id='''sequence'''	),
							            '''references''': datasets.Value('''string'''       ,   id='''sequence'''	),
							        }	)       ,   codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']       ,   reference_urls=[
							        '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
							        '''https://github.com/google-research/google-research/tree/master/rouge''',
							    ]       ,   )





				def 					_lowerCamelCase (	self				:   int       ,   __snake_case				:   Tuple       ,   __snake_case				:   Any       ,   __snake_case				:   List[str]=None       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   List[Any]=False	):





							'''simple docstring'''







							if rouge_types is None:
										UpperCAmelCase_							:      str						=   ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

							UpperCAmelCase_							:      Dict						=   rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase       ,   use_stemmer=__lowerCAmelCase	)
							if use_aggregator:
										UpperCAmelCase_							:      List[str]						=   scoring.BootstrapAggregator()
							else:
										UpperCAmelCase_							:      Tuple						=   []

							for ref, pred in zip(__lowerCAmelCase       ,   __lowerCAmelCase	):
										UpperCAmelCase_							:      Union[str, Any]						=   scorer.score(__lowerCAmelCase       ,   __lowerCAmelCase	)
										if use_aggregator:
													aggregator.add_scores(__lowerCAmelCase	)
										else:
													scores.append(__lowerCAmelCase	)

							if use_aggregator:
										UpperCAmelCase_							:      str						=   aggregator.aggregate()
							else:
										UpperCAmelCase_							:      Any						=   {}
										for key in scores[0]:
													UpperCAmelCase_							:      Tuple						=   [score[key] for score in scores]

							return result



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class  lowerCAmelCase__:






    '''simple docstring'''





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





    def 					_lowerCamelCase (	self				:   List[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 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   torch.arange(self.height * self.width	)
       UpperCAmelCase_							:      Any						=   torch.stack(
           [
               pixel_indices % self.width,
               torch.div(__snake_case       ,   self.width       ,   rounding_mode='''trunc'''	),
           ]       ,   axis=1       ,   )
       return coords





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_							:      List[Any]						=   self.shape
       UpperCAmelCase_							:      Optional[Any]						=   int(np.prod(__snake_case	)	)

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

       UpperCAmelCase_							:      Union[str, Any]						=   rays.view(__snake_case       ,   inner_batch_size * self.height * self.width       ,   2       ,   3	)

       return rays





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   torch.Tensor	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       UpperCAmelCase_							:      str						=   coords.view(__snake_case       ,   -1       ,   2	)

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

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

       UpperCAmelCase_							:      Optional[Any]						=   fracs.view(__snake_case       ,   -1       ,   2	)
       UpperCAmelCase_							:      List[Any]						=   (
           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:]
       )
       UpperCAmelCase_							:      Union[str, Any]						=   directions / directions.norm(dim=-1       ,   keepdim=__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   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 					_lowerCamelCase (	self				:   Any       ,   __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      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[Any]						=   []
   UpperCAmelCase_							:      int						=   []
   UpperCAmelCase_							:      Any						=   []
   UpperCAmelCase_							:      Tuple						=   []
   for theta in np.linspace(0			,       2 * np.pi			,       num=2_0       ):
      UpperCAmelCase_							:      Tuple						=   np.array([np.sin(__lowercase       ), np.cos(__lowercase       ), -0.5]       )
      z /= np.sqrt(np.sum(z**2       )       )
      UpperCAmelCase_							:      str						=   -z * 4
      UpperCAmelCase_							:      List[Any]						=   np.array([np.cos(__lowercase       ), -np.sin(__lowercase       ), 0.0]       )
      UpperCAmelCase_							:      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       ))			,       )