

"""simple docstring"""


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     :

				def __init__(     self							:    Tuple					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    List[Any]=sys.maxsize)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      '''bilinear'''
						__lowerCAmelCase:	Tuple   				=      max_size
						__lowerCAmelCase:	Optional[Any]   				=      short_edge_length




				def __call__(     self							:    Dict					,  UpperCamelCase__							:    Dict)->       int:



						'''simple docstring'''


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

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

						return img_augs





class    snake_case     :

				def __init__(     self							:    List[str]					,  UpperCamelCase__							:    Union[str, Any])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]					,  cfg.INPUT.MAX_SIZE_TEST)
						__lowerCAmelCase:	Union[str, Any]   				=      cfg.INPUT.FORMAT
						__lowerCAmelCase:	Optional[Any]   				=      cfg.SIZE_DIVISIBILITY
						__lowerCAmelCase:	List[Any]   				=      cfg.PAD_VALUE
						__lowerCAmelCase:	Optional[Any]   				=      cfg.INPUT.MAX_SIZE_TEST
						__lowerCAmelCase:	Tuple   				=      cfg.MODEL.DEVICE
						__lowerCAmelCase:	List[Any]   				=      torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)					,  1					,  1)
						__lowerCAmelCase:	List[str]   				=      torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)					,  1					,  1)
						__lowerCAmelCase:	Any   				=      lambda UpperCamelCase__: (x - self.pixel_mean) / self.pixel_std

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Any)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      tuple(max(__lowercase) for s in zip(*[img.shape for img in images]))
						__lowerCAmelCase:	str   				=      [im.shape[-2:] for im in images]
						__lowerCAmelCase:	Optional[int]   				=      [
						    nn.functional.pad(
						        __lowercase					,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]					,  value=self.pad_value					,  )
						    for size, im in zip(__lowercase					,  __lowercase)
						]

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




				def __call__(     self							:    Optional[int]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    str=False)->       int:



						'''simple docstring'''


						with torch.no_grad():
								if not isinstance(__lowercase					,  __lowercase):
										__lowerCAmelCase:	str   				=      [images]
								if single_image:
										assert len(__lowercase) == 1
								for i in range(len(__lowercase)):
										if isinstance(images[i]					,  torch.Tensor):
												images.insert(__lowercase					,  images.pop(__lowercase).to(self.device).float())
										elif not isinstance(images[i]					,  torch.Tensor):
												images.insert(
												    __lowercase					,  torch.as_tensor(img_tensorize(images.pop(__lowercase)					,  input_format=self.input_format))
												    .to(self.device)
												    .float()					,  )
            # resize smallest edge
								__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([im.shape[:2] for im in images])
								__lowerCAmelCase:	List[str]   				=      self.aug(__lowercase)
								# 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
								__lowerCAmelCase:	int   				=      [self.normalizer(__lowercase) for x in images]
								# now pad them to do the following operations
								__lowerCAmelCase:	List[Any]   				=      self.pad(__lowercase)
								# Normalize

								if self.size_divisibility > 0:
										raise NotImplementedError()
								# pad
								__lowerCAmelCase:	Union[str, Any]   				=      torch.true_divide(__lowercase					,  __lowercase)
								if single_image:
										return images[0], sizes[0], scales_yx[0]
								else:
										return images, sizes, scales_yx






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Dict:
		assert torch.isfinite(__lowerCAmelCase			).all(), "Box tensor contains infinite or NaN!"
		__lowerCAmelCase:	Any   				=      box_size
		tensor[:, 0].clamp_(min=0 ,			max=__lowerCAmelCase			)
		tensor[:, 1].clamp_(min=0 ,			max=__lowerCAmelCase			)
		tensor[:, 2].clamp_(min=0 ,			max=__lowerCAmelCase			)
		tensor[:, 3].clamp_(min=0 ,			max=__lowerCAmelCase			)





"""simple docstring"""


import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


__A						       =      datasets.logging.get_logger(__name__)


__A						       =      "\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

__A						       =      "\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

__A						       =      "\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

__A						       =      {
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}





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

				def 							lowercase_					(     self							:    Optional[int])->       Any:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  homepage="https://github.com/google-research/bleurt"					,  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/bleurt"]					,  reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]					,  )

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict)->       List[str]:



						'''simple docstring'''


						if self.config_name == "default":
								logger.warning(
								    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
								    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').")
								__lowerCAmelCase:	List[str]   				=      "bleurt-base-128"

						if self.config_name.lower() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Optional[int]   				=      self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Tuple   				=      self.config_name.upper()

						else:
								raise KeyError(
								    f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}")

						# download the model checkpoint specified by self.config_name and set up the scorer
						__lowerCAmelCase:	Union[str, Any]   				=      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
						__lowerCAmelCase:	Dict   				=      score.BleurtScorer(os.path.join(UpperCamelCase__					,  UpperCamelCase__))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.scorer.score(references=UpperCamelCase__					,  candidates=UpperCamelCase__)
						return {"scores": scores}


