



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  Any, _snake_case  :  Optional[int], _snake_case  :  List[str]=sys.maxsize					)   ->Any:
            snake_case__							:	Any   	=	'bilinear'
            snake_case__							:	Optional[int]   	=	max_size
            snake_case__							:	Union[str, Any]   	=	short_edge_length






     def __call__(       self  :  Tuple, _snake_case  :  int					)   ->List[Any]:
            snake_case__							:	List[str]   	=	[]
            for img in imgs:
                   snake_case__	,				snake_case__							:	Union[str, Any]   	=	img.shape[:2]
                   # later: provide list and randomly choose index for resize
                   snake_case__							:	Dict   	=	np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1					)
                   if size == 0:
                          return img
                   snake_case__							:	Any   	=	size * 1.0 / min(_snake_case, _snake_case					)
                   if h < w:
                          snake_case__	,				snake_case__							:	str   	=	size, scale * w
                   else:
                          snake_case__	,				snake_case__							:	Optional[int]   	=	scale * h, size
                   if max(_snake_case, _snake_case					) > self.max_size:
                          snake_case__							:	Union[str, Any]   	=	self.max_size * 1.0 / max(_snake_case, _snake_case					)
                          snake_case__							:	Optional[int]   	=	newh * scale
                          snake_case__							:	Dict   	=	neww * scale
                   snake_case__							:	List[str]   	=	int(neww + 0.5					)
                   snake_case__							:	Dict   	=	int(newh + 0.5					)

                   if img.dtype == np.uinta:
                          snake_case__							:	str   	=	Image.fromarray(_snake_case					)
                          snake_case__							:	List[Any]   	=	pil_image.resize((neww, newh), PILImageResampling.BILINEAR					)
                          snake_case__							:	List[Any]   	=	np.asarray(_snake_case					)
                   else:
                          snake_case__							:	Dict   	=	img.permute(2, 0, 1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
                          snake_case__							:	int   	=	nn.functional.interpolate(
                              _snake_case, (newh, neww), mode=self.interp_method, align_corners=_snake_case					).squeeze(0					)
                   img_augs.append(_snake_case					)

            return img_augs




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  Dict, _snake_case  :  List[Any]					)   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST					)
            snake_case__							:	Optional[Any]   	=	cfg.INPUT.FORMAT
            snake_case__							:	Optional[Any]   	=	cfg.SIZE_DIVISIBILITY
            snake_case__							:	int   	=	cfg.PAD_VALUE
            snake_case__							:	Optional[int]   	=	cfg.INPUT.MAX_SIZE_TEST
            snake_case__							:	Optional[int]   	=	cfg.MODEL.DEVICE
            snake_case__							:	List[str]   	=	torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					), 1, 1					)
            snake_case__							:	Dict   	=	torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					), 1, 1					)
            snake_case__							:	Any   	=	lambda _snake_case					: (x - self.pixel_mean) / self.pixel_std






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  Optional[int]					)   ->List[Any]:
            snake_case__							:	str   	=	tuple(max(_snake_case					) for s in zip(*[img.shape for img in images]					)					)
            snake_case__							:	Any   	=	[im.shape[-2:] for im in images]
            snake_case__							:	Any   	=	[
                nn.functional.pad(
                    _snake_case, [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]], value=self.pad_value, )
                for size, im in zip(_snake_case, _snake_case					)
            ]

            return torch.stack(_snake_case					), torch.tensor(_snake_case					)






     def __call__(       self  :  Dict, _snake_case  :  int, _snake_case  :  Dict=False					)   ->int:
            with torch.no_grad():
                   if not isinstance(_snake_case, _snake_case					):
                          snake_case__							:	Optional[Any]   	=	[images]
                   if single_image:
                          assert len(_snake_case					) == 1
                   for i in range(len(_snake_case					)					):
                          if isinstance(images[i], torch.Tensor					):
                                 images.insert(_snake_case, images.pop(_snake_case					).to(self.device					).float()					)
                          elif not isinstance(images[i], torch.Tensor					):
                                 images.insert(
                                     _snake_case, torch.as_tensor(img_tensorize(images.pop(_snake_case					), input_format=self.input_format					)					)
                                     .to(self.device					)
                                     .float(), )
            # resize smallest edge
                   snake_case__							:	Union[str, Any]   	=	torch.tensor([im.shape[:2] for im in images]					)
                   snake_case__							:	str   	=	self.aug(_snake_case					)
                   # transpose images and convert to torch tensors
                   # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                   # now normalize before pad to avoid useless arithmetic
                   snake_case__							:	str   	=	[self.normalizer(_snake_case					) for x in images]
                   # now pad them to do the following operations
                   snake_case__	,				snake_case__							:	List[str]   	=	self.pad(_snake_case					)
                   # Normalize

                   if self.size_divisibility > 0:
                          raise NotImplementedError()
                   # pad
                   snake_case__							:	Tuple   	=	torch.true_divide(_snake_case, _snake_case					)
                   if single_image:
                          return images[0], sizes[0], scales_yx[0]
                   else:
                          return images, sizes, scales_yx


def        lowercase_							(A : List[str]		,					A : Any      ):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes





def        lowercase_							(A : List[str]		,					A : Tuple[int, int]      ):
       assert torch.isfinite(A      ).all(), "Box tensor contains infinite or NaN!"
       snake_case__	,				snake_case__							:	Dict   	=	box_size
       tensor[:, 0].clamp_(min=0		,					max=A      )
       tensor[:, 1].clamp_(min=0		,					max=A      )
       tensor[:, 2].clamp_(min=0		,					max=A      )
       tensor[:, 3].clamp_(min=0		,					max=A      )











from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a_   :Any        =      "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

a_   :List[str]        =      "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

a_   :List[str]        =      "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"




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






     """simple docstring"""


     def    lowercase_   (       self  :  str					)   ->MetricInfo:
            return datasets.MetricInfo(
                description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
                    {
                        'predictions': datasets.Sequence(datasets.Value('string', id='token'					), id='sequence'					),
                        'references': datasets.Sequence(
                            datasets.Sequence(datasets.Value('string', id='token'					), id='sequence'					), id='references'					),
                    }					), )






     def    lowercase_   (       self  :  str, _snake_case  :  List[List[List[str]]], _snake_case  :  List[List[str]], _snake_case  :  int = 1, _snake_case  :  int = 4, )   ->Dict[str, float]:
            return {
                "google_bleu": gleu_score.corpus_gleu(
                    list_of_references=_snake_case, hypotheses=_snake_case, min_len=_snake_case, max_len=_snake_case					)
            }






