
'''simple docstring'''





import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
     import importlib.resources as pkg_resources
except ImportError:
     # Try backported to PY<37 `importlib_resources`.
     import importlib_resources as pkg_resources

from . import resources


A							     = "https://github.com/huggingface/datasets/tree/main/src/datasets/utils"
A							     = F"""{BASE_REF_URL}/{__file__}"""
A							     = logging.getLogger(__name__)
def 	snake_case_							(				_a  :				str      ):
      snake_case_    							=				pkg_resources.read_text(_a			,       _a      )
      return yaml.safe_load(_a      ), F'''{BASE_REF_URL}/resources/{resource}'''


A     ,  A							     = load_yaml_resource("readme_structure.yaml")

A							     = [
    "[Needs More Information]",
    "[More Information Needed]",
    "(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)",
]

# Dictionary representation of section/readme, error_list, warning_list
A							     = Tuple[dict, List[str], List[str]]



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			str						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			List[str] = None						,			lowerCAmelCase_	:			bool = False     ) ->							List[Any]:
        snake_case_    							=				name
        snake_case_    							=				level
        snake_case_    							=				lines
        snake_case_    							=				""
        snake_case_    							=				True
        snake_case_    							=				{}
        snake_case_    							=				[]
        snake_case_    							=				[]
        if self.lines is not None:
              self.parse(suppress_parsing_errors=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			bool = False     ) ->							Union[str, Any]:
        snake_case_    							=				""
        snake_case_    							=				[]
        snake_case_    							=				False
        for line in self.lines:
              if line.strip(" \n"     ) == "":
                    continue
              elif line.strip(" \n"     )[:3] == "```":
                    snake_case_    							=				not code_start
              elif line.split()[0] == self.level + "#" and not code_start:
                    if current_sub_level != "":
                          snake_case_    							=				Section(lowerCAmelCase_						,			self.level + "#"						,			lowerCAmelCase_     )
                          snake_case_    							=				[]
                    else:
                          if current_lines != []:
                                self.text += "".join(lowerCAmelCase_     ).strip()
                                if self.text != "" and self.text not in FILLER_TEXT:
                                      snake_case_    							=				False
                                snake_case_    							=				[]

                    snake_case_    							=				" ".join(line.split()[1:]     ).strip(" \n"     )
              else:
                    current_lines.append(lowerCAmelCase_     )
        else:
              if current_sub_level != "":
                    if current_sub_level in self.content:
                          self.parsing_error_list.append(
                              f'''Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections.'''     )
                    snake_case_    							=				Section(lowerCAmelCase_						,			self.level + "#"						,			lowerCAmelCase_     )
              else:
                    if current_lines != []:
                          self.text += "".join(lowerCAmelCase_     ).strip()
                          if self.text != "" and self.text not in FILLER_TEXT:
                                snake_case_    							=				False

        if self.level == "" and not suppress_parsing_errors:
              if self.parsing_error_list != [] or self.parsing_warning_list != []:
                    snake_case_    							=				snake_case_    							=				"\n".join("-\t" + x for x in self.parsing_error_list + self.parsing_warning_list     )
                    snake_case_    							=				f'''The following issues were found while parsing the README at `{self.name}`:\n''' + errors
                    raise ValueError(lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Dict						,			lowerCAmelCase_	:			dict     ) ->							ReadmeValidatorOutput:
        snake_case_    							=				[]
        snake_case_    							=				[]
        if structure["allow_empty"] is False:
              # If content is expected
              if self.is_empty_text and self.content == {}:
                    # If no content is found, mention it in the error_list
                    error_list.append(f'''Expected some content in section `{self.name}` but it is empty.'''     )

        if structure["allow_empty_text"] is False:
              # If some text is expected
              if self.is_empty_text:
                    # If no text is found, mention it in the error_list
                    error_list.append(
                        f'''Expected some text in section `{self.name}` but it is empty (text in subsections are ignored).'''     )
        # Subsections Validation
        if structure["subsections"] is not None:
              # If subsections are expected
              if self.content == {}:
                    # If no subsections are present
                    snake_case_    							=				[subsection["name"] for subsection in structure["subsections"]]
                    # Mention the expected values in the error_list
                    error_list.append(
                        f'''Section `{self.name}` expected the following subsections: {', '.join(['`'+x+'`' for x in values]     )}. Found \'None\'.'''     )
              else:
                    # If some subsections are present
                    snake_case_    							=				[subsection["name"] for subsection in structure["subsections"]]
                    snake_case_    							=				False
                    for idx, name in enumerate(lowerCAmelCase_     ):
                          if name not in self.content:
                                # If the expected subsection is not present
                                error_list.append(f'''Section `{self.name}` is missing subsection: `{name}`.'''     )
                                snake_case_    							=				True
                          else:
                                # If the subsection is present, validate subsection, return the result
                                # and concat the errors from subsection to section error_list

                                # Skip sublevel validation if current level is `###`
                                if self.level == "###":
                                      continue
                                else:
                                      snake_case_							,						snake_case_							,						snake_case_    							=				self.content[name].validate(
                                          structure["subsections"][idx]     )
                                error_list += subsec_error_list
                                warning_list += subsec_warning_list

                    if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
                          for name in self.content:
                                if name not in structure_names:
                                      # If an extra subsection is present
                                      warning_list.append(
                                          f'''`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown.'''     )
        if error_list:
              # If there are errors, do not return the dictionary as it is invalid
              return {}, error_list, warning_list
        else:
              return self.to_dict(), error_list, warning_list


  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							dict:
        return {
            "name": self.name,
            "text": self.text,
            "is_empty_text": self.is_empty_text,
            "subsections": [value.to_dict() for value in self.content.values()],
        }



class 			lowerCAmelCase__ (							UpperCamelCase							):  # Level 0



  '''simple docstring'''






  def __init__(self	:			Optional[int]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			dict = None						,			lowerCAmelCase_	:			bool = False     ) ->							List[str]:
        super().__init__(name=lowerCAmelCase_						,			level=""     )  # Not using lines here as we need to use a child class parse
        snake_case_    							=				structure
        snake_case_    							=				-2
        snake_case_    							=				0
        snake_case_    							=				lines
        if self.lines is not None:
              self.parse(suppress_parsing_errors=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Any:
        if self.structure is None:
              snake_case_							,						snake_case_							,						snake_case_    							=				self._validate(lowerCAmelCase_     )
        else:
              snake_case_							,						snake_case_							,						snake_case_    							=				self._validate(self.structure     )
        if error_list != [] or warning_list != []:
              snake_case_    							=				"\n".join(["-\t" + x for x in error_list + warning_list]     )
              snake_case_    							=				f'''The following issues were found for the README at `{self.name}`:\n''' + errors
              raise ValueError(lowerCAmelCase_     )

  @classmethod
  def 		_lowerCamelCase						(cls	:			int						,			lowerCAmelCase_	:			Path						,			lowerCAmelCase_	:			dict = None						,			lowerCAmelCase_	:			bool = False     ) ->							int:
        with open(lowerCAmelCase_						,			encoding="utf-8"     ) as f:
              snake_case_    							=				f.readlines()
        return cls(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			suppress_parsing_errors=lowerCAmelCase_     )

  @classmethod
  def 		_lowerCamelCase						(cls	:			Optional[int]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			dict = None						,			lowerCAmelCase_	:			str = "root"						,			lowerCAmelCase_	:			bool = False     ) ->							Optional[int]:
        snake_case_    							=				string.split("\n"     )
        return cls(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			suppress_parsing_errors=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[Any]						,			lowerCAmelCase_	:			bool = False     ) ->							Dict:
        # Skip Tags
        snake_case_    							=				0

        for line in self.lines:
              self.yaml_tags_line_count += 1
              if line.strip(" \n"     ) == "---":
                    self.tag_count += 1
                    if self.tag_count == 2:
                          break
              line_count += 1
        if self.tag_count == 2:
              snake_case_    							=				self.lines[line_count + 1 :]  # Get the last + 1 th item.
        else:
              snake_case_    							=				self.lines[self.tag_count :]
        super().parse(suppress_parsing_errors=lowerCAmelCase_     )

  def __str__(self	:			Any     ) ->							Any:
        return str(self.to_dict()     )


  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple     ) ->							Optional[Any]:
        snake_case_    							=				[]
        snake_case_    							=				[]
        if self.yaml_tags_line_count == 0:
              warning_list.append("Empty YAML markers are present in the README."     )
        elif self.tag_count == 0:
              warning_list.append("No YAML markers are present in the README."     )
        elif self.tag_count == 1:
              warning_list.append("Only the start of YAML tags present in the README."     )
        # Check how many first level sections are present.
        snake_case_    							=				len(self.content.keys()     )
        if num_first_level_keys > 1:
              # If more than one, add to the error list, continue
              error_list.append(
                  f'''The README has several first-level headings: {', '.join(['`'+x+'`' for x in list(self.content.keys()     )]     )}. Only one heading is expected. Skipping further validation for this README.'''     )
        elif num_first_level_keys < 1:
              # If less than one, append error.
              error_list.append(
                  "The README has no first-level headings. One heading is expected. Skipping further validation for this README."     )

        else:
              # If one exactly
              snake_case_    							=				list(self.content.keys()     )[0]  # Get the key
              if start_key.startswith("Dataset Card for"     ):  # Check correct start
                    # If the starting is correct, validate all the sections
                    snake_case_							,						snake_case_							,						snake_case_    							=				self.content[start_key].validate(
                        readme_structure["subsections"][0]     )
                    error_list += sec_error_list
                    warning_list += sec_warning_list
              else:
                    # If not found, append error
                    error_list.append(
                        "No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."     )
        if error_list:
              # If there are errors, do not return the dictionary as it is invalid
              return {}, error_list, warning_list
        else:
              return self.to_dict(), error_list, warning_list


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

     A							     = ArgumentParser(usage="Validate the content (excluding YAML tags) of a README.md file.")
     ap.add_argument("readme_filepath")
     A							     = ap.parse_args()
     A							     = Path(args.readme_filepath)
     A							     = ReadMe.from_readme(readme_filepath)



'''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 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]=sys.maxsize     ) ->							Tuple:
        snake_case_    							=				"bilinear"
        snake_case_    							=				max_size
        snake_case_    							=				short_edge_length


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

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

        return img_augs



class 			lowerCAmelCase__ :



  '''simple docstring'''






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

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple     ) ->							Any:
        snake_case_    							=				tuple(max(lowerCAmelCase_     ) for s in zip(*[img.shape for img in images]     )     )
        snake_case_    							=				[im.shape[-2:] for im in images]
        snake_case_    							=				[
            nn.functional.pad(
                lowerCAmelCase_						,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,			value=self.pad_value						,			)
            for size, im in zip(lowerCAmelCase_						,			lowerCAmelCase_     )
        ]

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


  def __call__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]=False     ) ->							int:
        with torch.no_grad():
              if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				[images]
              if single_image:
                    assert len(lowerCAmelCase_     ) == 1
              for i in range(len(lowerCAmelCase_     )     ):
                    if isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(lowerCAmelCase_						,			images.pop(lowerCAmelCase_     ).to(self.device     ).float()     )
                    elif not isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(
                              lowerCAmelCase_						,			torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_     )						,			input_format=self.input_format     )     )
                              .to(self.device     )
                              .float()						,			)
            # resize smallest edge
              snake_case_    							=				torch.tensor([im.shape[:2] for im in images]     )
              snake_case_    							=				self.aug(lowerCAmelCase_     )
              # 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_    							=				[self.normalizer(lowerCAmelCase_     ) for x in images]
              # now pad them to do the following operations
              snake_case_							,						snake_case_    							=				self.pad(lowerCAmelCase_     )
              # Normalize

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

def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[Any]      ):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes




def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Tuple[int, int]      ):
      assert torch.isfinite(_a      ).all(), "Box tensor contains infinite or NaN!"
      snake_case_							,						snake_case_    							=				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      )

