


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class 						lowerCamelCase       (yaml.SafeLoader      ):







							"""simple docstring"""




							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					Dict						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						[self.constructed_objects[key_node] for key_node, _ in node.value]
														SCREAMING_SNAKE_CASE__       :      str     =						[tuple(_UpperCAmelCase						) if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) else key for key in keys]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						Counter(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[key for key in counter if counter[key] > 1]
														if duplicate_keys:
																					raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''						)






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[str]=False						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						super().construct_mapping(_UpperCAmelCase,			deep=_UpperCAmelCase						)
														self._check_no_duplicates_on_constructed_node(_UpperCAmelCase						)
														return mapping




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    Tuple[Optional[str], str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						list(readme_content.splitlines()       )
							if full_content and full_content[0] == "---" and "---" in full_content[1:]:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						full_content[1:].index("---"       ) + 1
														SCREAMING_SNAKE_CASE__       :      Any     =						"\n".join(full_content[1:sep_idx]       )
														return yamlblock, "\n".join(full_content[sep_idx + 1 :]       )

							return None, "\n".join(SCREAMING_SNAKE_CASE__       )


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   {"train_eval_index"}  # train-eval-index in the YAML metadata






							@classmethod
							def  A_     ( cls     :					int,			_UpperCAmelCase     :					Path						)				->			"DatasetMetadata":





														"""simple docstring"""
														with open(_UpperCAmelCase,			encoding="utf-8"						) as readme_file:
																					SCREAMING_SNAKE_CASE__       :      int     =						_split_yaml_from_readme(readme_file.read()						)
														if yaml_string is not None:
																					return cls.from_yaml_string(_UpperCAmelCase						)
														else:
																					return cls()






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Path						)				->			List[str]:





														"""simple docstring"""
														if path.exists():
																					with open(_UpperCAmelCase,			encoding="utf-8"						) as readme_file:
																												SCREAMING_SNAKE_CASE__       :      Any     =						readme_file.read()
														else:
																					SCREAMING_SNAKE_CASE__       :      str     =						None
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self._to_readme(_UpperCAmelCase						)
														with open(_UpperCAmelCase,			"w",			encoding="utf-8"						) as readme_file:
																					readme_file.write(_UpperCAmelCase						)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					Optional[str] = None						)				->			str:





														"""simple docstring"""
														if readme_content is not None:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						_split_yaml_from_readme(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						"---\n" + self.to_yaml_string() + "---\n" + content
														else:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"---\n" + self.to_yaml_string() + "---\n"
														return full_content






							@classmethod
							def  A_     ( cls     :					str,			_UpperCAmelCase     :					str						)				->			"DatasetMetadata":





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						yaml.load(_UpperCAmelCase,			Loader=_NoDuplicateSafeLoader						) or {}

														# Convert the YAML keys to DatasetMetadata fields
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						{
														    (key.replace("-",			"_"						) if key.replace("-",			"_"						) in cls._FIELDS_WITH_DASHES else key): value
														    for key, value in metadata_dict.items()
														}
														return cls(**_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any]						)				->			str:





														"""simple docstring"""
														return yaml.safe_dump(
														    {
														        (key.replace("_",			"-"						) if key in self._FIELDS_WITH_DASHES else key): value
														        for key, value in self.items()
														    },			sort_keys=_UpperCAmelCase,			allow_unicode=_UpperCAmelCase,			encoding="utf-8",			).decode("utf-8"						)


_lowerCamelCase :    Tuple  							= {
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		_lowerCamelCase :    Tuple  							= ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
		ap.add_argument('''readme_filepath''')
		_lowerCamelCase :    Optional[int]  							= ap.parse_args()

		_lowerCamelCase :    Tuple  							= Path(args.readme_filepath)
		_lowerCamelCase :    Optional[int]  							= DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)













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 						lowerCamelCase       :







							"""simple docstring"""




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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bilinear"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						short_edge_length






							def __call__( self     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]						)				->			str:





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

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

														return img_augs


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],			cfg.INPUT.MAX_SIZE_TEST						)
														SCREAMING_SNAKE_CASE__       :      Any     =						cfg.INPUT.FORMAT
														SCREAMING_SNAKE_CASE__       :      List[str]     =						cfg.SIZE_DIVISIBILITY
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						cfg.PAD_VALUE
														SCREAMING_SNAKE_CASE__       :      Dict     =						cfg.INPUT.MAX_SIZE_TEST
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cfg.MODEL.DEVICE
														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						lambda _UpperCAmelCase						: (x - self.pixel_mean) / self.pixel_std






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tuple(max(_UpperCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[im.shape[-2:] for im in images]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    nn.functional.pad(
														        _UpperCAmelCase,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],			value=self.pad_value,			)
														    for size, im in zip(_UpperCAmelCase,			_UpperCAmelCase						)
														]

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






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





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

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




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Union[str, Any]:

							'''simple docstring'''






							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple[int, int]       )	->    List[Any]:

							'''simple docstring'''






							assert torch.isfinite(SCREAMING_SNAKE_CASE__       ).all(), "Box tensor contains infinite or NaN!"
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						box_size
							tensor[:, 0].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 1].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 2].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 3].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )






