"""simple docstring"""





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def     a_					(				_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			bool					):





       '''simple docstring'''

       if not inplace:
              lowercase__      :		Any          =     ma + ma
       else:
              ma += ma

       return ma





def     a_					(				_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			List[int]					):





       '''simple docstring'''

       lowercase__      :		List[str]          =     -1 * len(_lowerCAmelCase					)
       lowercase__      :		Union[str, Any]          =     list(range(len(tensor.shape[:zero_index]					)					)					)
       return tensor.permute(first_inds + [zero_index + i for i in inds]					)





def     a_					(				_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			int					):





       '''simple docstring'''

       return t.reshape(t.shape[:-no_dims] + (-1,)					)





def     a_					(				_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			float = 1E-4					):





       '''simple docstring'''

       lowercase__      :		Optional[int]          =     mask.expand(*value.shape					)
       return torch.sum(mask * value       ,			dim=_lowerCAmelCase					) / (eps + torch.sum(_lowerCAmelCase       ,			dim=_lowerCAmelCase					))





def     a_					(				_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			torch.types.Number = 2.3_1_2_5       ,			_lowerCAmelCase  :			torch.types.Number = 2_1.6_8_7_5       ,			_lowerCAmelCase  :			int = 64					):





       '''simple docstring'''

       lowercase__      :		int          =     torch.linspace(_lowerCAmelCase       ,			_lowerCAmelCase       ,			no_bins - 1       ,			device=pts.device					)
       lowercase__      :		Union[str, Any]          =     torch.sqrt(torch.sum((pts.unsqueeze(-2					) - pts.unsqueeze(-3					)) ** 2       ,			dim=-1					)					)
       return torch.bucketize(_lowerCAmelCase       ,			_lowerCAmelCase					)





def     a_					(				_lowerCAmelCase  :			Callable[[list], Any]       ,			_lowerCAmelCase  :			List[dict]					):





       '''simple docstring'''

       lowercase__      :		List[str]          =     dicts[0]
       lowercase__      :		int          =     {}
       for k, v in first.items():
              lowercase__      :		Optional[Any]          =     [d[k] for d in dicts]
              if isinstance(_lowerCAmelCase       ,			_lowerCAmelCase					):
                     lowercase__      :		List[Any]          =     dict_multimap(_lowerCAmelCase       ,			_lowerCAmelCase					)
              else:
                     lowercase__      :		Union[str, Any]          =     fn(_lowerCAmelCase					)

       return new_dict





def     a_					(				_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			torch.Tensor					):





       '''simple docstring'''

       lowercase__      :		Union[str, Any]          =     v_bins.view(((1,) * len(x.shape					)) + (len(_lowerCAmelCase					),)					)
       lowercase__      :		Dict          =     x[..., None] - reshaped_bins
       lowercase__      :		Any          =     torch.argmin(torch.abs(_lowerCAmelCase					)       ,			dim=-1					)
       return nn.functional.one_hot(_lowerCAmelCase       ,			num_classes=len(_lowerCAmelCase					)					).float()





def     a_					(				_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			torch.Tensor       ,			_lowerCAmelCase  :			int = 0       ,			_lowerCAmelCase  :			int = 0					):





       '''simple docstring'''

       lowercase__      :		List[Union[slice, torch.Tensor]]          =     []
       for i, s in enumerate(data.shape[:no_batch_dims]					):
              lowercase__      :		Union[str, Any]          =     torch.arange(_lowerCAmelCase					)
              lowercase__      :		List[Any]          =     r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape					) - i - 1)))					)
              ranges.append(_lowerCAmelCase					)

       lowercase__      :		List[Union[slice, torch.Tensor]]          =     [slice(_lowerCAmelCase					) for _ in range(len(data.shape					) - no_batch_dims					)]
       lowercase__      :		Optional[Any]          =     inds
       ranges.extend(_lowerCAmelCase					)
       # Matt note: Editing this to get around the behaviour of using a list as an array index changing
       # in recent Numpy versions
       return data[tuple(_lowerCAmelCase					)]


_UpperCamelCase       :						Dict     =   TypeVar("T")





def     a_					(				_lowerCAmelCase  :			Callable[[T], Any]       ,			_lowerCAmelCase  :			Dict[Any, Union[dict, list, tuple, T]]       ,			_lowerCAmelCase  :			Type[T]					):





       '''simple docstring'''

       lowercase__      :		Dict[Any, Union[dict, list, tuple, Any]]          =     {}
       for k, v in dic.items():
              if isinstance(_lowerCAmelCase       ,			_lowerCAmelCase					):
                     lowercase__      :		Tuple          =     dict_map(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)
              else:
                     lowercase__      :		Tuple          =     tree_map(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)

       return new_dict





@overload
def     a_					(				_lowerCAmelCase  :			Callable[[T], Any]       ,			_lowerCAmelCase  :			T       ,			_lowerCAmelCase  :			Type[T]					):





       '''simple docstring'''

       ...





@overload
def     a_					(				_lowerCAmelCase  :			Callable[[T], Any]       ,			_lowerCAmelCase  :			dict       ,			_lowerCAmelCase  :			Type[T]					):





       '''simple docstring'''

       ...





@overload
def     a_					(				_lowerCAmelCase  :			Callable[[T], Any]       ,			_lowerCAmelCase  :			list       ,			_lowerCAmelCase  :			Type[T]					):





       '''simple docstring'''

       ...





@overload
def     a_					(				_lowerCAmelCase  :			Callable[[T], Any]       ,			_lowerCAmelCase  :			tuple       ,			_lowerCAmelCase  :			Type[T]					):





       '''simple docstring'''

       ...





def     a_					(				_lowerCAmelCase  :			List[str]       ,			_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			int					):





       '''simple docstring'''

       if isinstance(_lowerCAmelCase       ,			_lowerCAmelCase					):
              return dict_map(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)
       elif isinstance(_lowerCAmelCase       ,			_lowerCAmelCase					):
              return [tree_map(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					) for x in tree]
       elif isinstance(_lowerCAmelCase       ,			_lowerCAmelCase					):
              return tuple(tree_map(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					) for x in tree					)
       elif isinstance(_lowerCAmelCase       ,			_lowerCAmelCase					):
              return fn(_lowerCAmelCase					)
       else:
              print(type(_lowerCAmelCase					)					)
              raise ValueError('Not supported'					)






_UpperCamelCase       :						Optional[int]     =   partial(tree_map, leaf_type=torch.Tensor)


"""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


_UpperCamelCase       :						Optional[int]     =   "https://github.com/huggingface/datasets/tree/main/src/datasets/utils"
_UpperCamelCase       :						Union[str, Any]     =   f'''{BASE_REF_URL}/{__file__}'''
_UpperCamelCase       :						Any     =   logging.getLogger(__name__)





def     a_					(				_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     pkg_resources.read_text(_lowerCAmelCase       ,			_lowerCAmelCase					)
       return yaml.safe_load(_lowerCAmelCase					), f"""{BASE_REF_URL}/resources/{resource}"""


_UpperCamelCase   ,				_UpperCamelCase       :						int     =   load_yaml_resource("readme_structure.yaml")

_UpperCamelCase       :						Optional[Any]     =   [
    "[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
_UpperCamelCase       :						List[Any]     =   Tuple[dict, List[str], List[str]]


class 					UpperCAmelCase_     :


      def __init__(       self		,      a		,      a		,      a = None		,      a = False							)	-> str:
             lowercase__      :		List[str]          =     name
             lowercase__      :		int          =     level
             lowercase__      :		Dict          =     lines
             lowercase__      :		Optional[int]          =     ''
             lowercase__      :		List[str]          =     True
             lowercase__      :		Union[str, Any]          =     {}
             lowercase__      :		Any          =     []
             lowercase__      :		Optional[int]          =     []
             if self.lines is not None:
                    self.parse(suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self		,      a = False							)	-> Any:
             lowercase__      :		List[str]          =     ''
             lowercase__      :		int          =     []
             lowercase__      :		Any          =     False
             for line in self.lines:
                    if line.strip(' \n'							) == "":
                           continue
                    elif line.strip(' \n'							)[:3] == "```":
                           lowercase__      :		Optional[Any]          =     not code_start
                    elif line.split()[0] == self.level + "#" and not code_start:
                           if current_sub_level != "":
                                  lowercase__      :		Tuple          =     Section(a		,      self.level + '#'		,      a							)
                                  lowercase__      :		str          =     []
                           else:
                                  if current_lines != []:
                                         self.text += "".join(a							).strip()
                                         if self.text != "" and self.text not in FILLER_TEXT:
                                                lowercase__      :		List[Any]          =     False
                                         lowercase__      :		List[str]          =     []

                           lowercase__      :		Dict          =     ' '.join(line.split()[1:]							).strip(' \n'							)
                    else:
                           current_lines.append(a							)
             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."""							)
                           lowercase__      :		Tuple          =     Section(a		,      self.level + '#'		,      a							)
                    else:
                           if current_lines != []:
                                  self.text += "".join(a							).strip()
                                  if self.text != "" and self.text not in FILLER_TEXT:
                                         lowercase__      :		Dict          =     False

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


      def 		_UpperCAmelCase	(       self		,      a							)	-> ReadmeValidatorOutput:
             lowercase__      :		Tuple          =     []
             lowercase__      :		int          =     []
             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
                           lowercase__      :		str          =     [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
                           lowercase__      :		Union[str, Any]          =     [subsection['name'] for subsection in structure['subsections']]
                           lowercase__      :		Any          =     False
                           for idx, name in enumerate(a							):
                                  if name not in self.content:
                                         # If the expected subsection is not present
                                         error_list.append(f"""Section `{self.name}` is missing subsection: `{name}`."""							)
                                         lowercase__      :		str          =     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:
                                                lowercase__   ,				lowercase__   ,				lowercase__      :		Optional[Any]          =     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 		_UpperCAmelCase	(       self							)	-> 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 					UpperCAmelCase_     (   _a):  # Level 0


      def __init__(       self		,      a		,      a		,      a = None		,      a = False							)	-> Any:
             super().__init__(name=a		,      level=''							)  # Not using lines here as we need to use a child class parse
             lowercase__      :		List[Any]          =     structure
             lowercase__      :		Dict          =     -2
             lowercase__      :		Union[str, Any]          =     0
             lowercase__      :		Dict          =     lines
             if self.lines is not None:
                    self.parse(suppress_parsing_errors=a							)


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


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      a		,      a = None		,      a = False							)	-> Tuple:
             with open(a		,      encoding='utf-8'							) as f:
                    lowercase__      :		List[Any]          =     f.readlines()
             return cls(a		,      a		,      a		,      suppress_parsing_errors=a							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      a		,      a = None		,      a = "root"		,      a = False							)	-> int:
             lowercase__      :		Optional[Any]          =     string.split('\n'							)
             return cls(a		,      a		,      a		,      suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self		,      a = False							)	-> List[Any]:
             # Skip Tags
             lowercase__      :		Tuple          =     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:
                    lowercase__      :		Optional[int]          =     self.lines[line_count + 1 :]  # Get the last + 1 th item.
             else:
                    lowercase__      :		List[str]          =     self.lines[self.tag_count :]
             super().parse(suppress_parsing_errors=a							)


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


      def 		_UpperCAmelCase	(       self		,      a							)	-> str:
             lowercase__      :		Any          =     []
             lowercase__      :		Optional[int]          =     []
             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.
             lowercase__      :		int          =     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
                    lowercase__      :		Dict          =     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
                           lowercase__   ,				lowercase__   ,				lowercase__      :		Any          =     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

       _UpperCamelCase       :						Tuple     =   ArgumentParser(usage="Validate the content (excluding YAML tags) of a README.md file.")
       ap.add_argument("readme_filepath")
       _UpperCamelCase       :						str     =   ap.parse_args()
       _UpperCamelCase       :						Optional[int]     =   Path(args.readme_filepath)
       _UpperCamelCase       :						Dict     =   ReadMe.from_readme(readme_filepath)

