



import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
UpperCAmelCase_			      =  False
UpperCAmelCase_			      =  Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase_			      =  datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__=sys.argv[1:]      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None
					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							Union[str, Any]					     =   1
					_snake_case     , _snake_case					:							Dict					     =   getopt.getopt(SCREAMING_SNAKE_CASE__						,	"""i:o:b:s"""						,	["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]      )
					for opt, arg in opts:
						if opt in ("-i", "--inputdir"):
							_snake_case					:							List[Any]					     =   arg
						elif opt in ("-o", "--outfile"):
							_snake_case					:							Dict					     =   arg
						elif opt in ("-b", "--batch_size"):
							_snake_case					:							Union[str, Any]					     =   int(SCREAMING_SNAKE_CASE__      )
						elif opt in ("-s", "--subset_list"):
							_snake_case					:							List[Any]					     =   arg

					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
					assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE__      ), f'''{outputfile}'''
					if subset_list is not None:
						with open(os.path.realpath(SCREAMING_SNAKE_CASE__      )      ) as f:
							_snake_case					:							int					     =   {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE__      )}
					else:
						_snake_case					:							List[str]					     =   None

					_snake_case					:							Any					     =   CONFIG
					if torch.cuda.is_available():
						_snake_case					:							str					     =   """cuda"""
					_snake_case					:							Union[str, Any]					     =   os.path.realpath(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   os.path.realpath(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   Preprocess(self.config      )
					_snake_case					:							Optional[int]					     =   GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""						,	config=self.config      )
					_snake_case					:							List[str]					     =   batch_size if batch_size != 0 else 1
					_snake_case					:							Optional[Any]					     =   DEFAULT_SCHEMA







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   int(file.split("""."""      )[0].split("""_"""      )[-1]      )
					_snake_case					:							Optional[int]					     =   os.path.join(self.inputdir						,	SCREAMING_SNAKE_CASE__      )
					return (img_id, filepath)







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   []
					for i, file in enumerate(os.listdir(self.inputdir      )      ):
						if self.subset_list is not None and i not in self.subset_list:
							continue
						batch.append(self._vqa_file_split(SCREAMING_SNAKE_CASE__      )      )
						if len(SCREAMING_SNAKE_CASE__      ) == self.batch:
							_snake_case					:							Union[str, Any]					     =   batch
							_snake_case					:							Optional[Any]					     =   []
							yield list(map(SCREAMING_SNAKE_CASE__						,	zip(*SCREAMING_SNAKE_CASE__      )      )      )

					for i in range(1      ):
						yield list(map(SCREAMING_SNAKE_CASE__						,	zip(*SCREAMING_SNAKE_CASE__      )      )      )







				def __call__(				self      ):






					"""simple docstring"""

					if not TEST:
						_snake_case					:							Optional[Any]					     =   datasets.ArrowWriter(features=self.schema						,	path=self.outputfile      )
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
						_snake_case     , _snake_case     , _snake_case					:							List[Any]					     =   self.preprocess(SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Tuple					     =   self.model(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	scales_yx=SCREAMING_SNAKE_CASE__						,	padding="""max_detections"""						,	max_detections=self.config.MAX_DETECTIONS						,	pad_value=0						,	return_tensors="""np"""						,	location="""cpu"""						,	)
						_snake_case					:							Dict					     =   output_dict.pop("""normalized_boxes"""      )
						if not TEST:
							_snake_case					:							List[str]					     =   np.array(SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   self.schema.encode_batch(SCREAMING_SNAKE_CASE__      )
							writer.write_batch(SCREAMING_SNAKE_CASE__      )
						if TEST:
							break
						# finalizer the writer
					if not TEST:
						_snake_case     , _snake_case					:							Optional[int]					     =   writer.finalize()
						print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''      )
def   UpperCAmelCase							(		A__     )							->					List[str]:
	try:
		_snake_case					:							Optional[int]					     =   json.load(A__     )
		try:
			_snake_case					:							Union[str, Any]					     =   list(data.keys()     )
		except Exception:
			_snake_case					:							Optional[int]					     =   [d["""img_id"""] for d in data]
	except Exception:
		try:
			_snake_case					:							List[str]					     =   eval(stream.read()     )
		except Exception:
			_snake_case					:							Any					     =   stream.read().split("""\n"""     )
	return data


if __name__ == "__main__":
						UpperCAmelCase_			      =  Extract(sys.argv[1:])
						extract()
						if not TEST:
												UpperCAmelCase_			      =  datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)










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_			      =  '''https://github.com/huggingface/datasets/tree/main/src/datasets/utils'''
UpperCAmelCase_			      =  f"""{BASE_REF_URL}/{__file__}"""
UpperCAmelCase_			      =  logging.getLogger(__name__)
def   UpperCAmelCase							(		A__     )							->					Tuple[Any, str]:
	_snake_case					:							Optional[int]					     =   pkg_resources.read_text(A__				,  A__     )
	return yaml.safe_load(A__     ), f'''{BASE_REF_URL}/resources/{resource}'''


UpperCAmelCase_      ,				UpperCAmelCase_			      =  load_yaml_resource('''readme_structure.yaml''')

UpperCAmelCase_			      =  [
    '''[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_			      =  Tuple[dict, List[str], List[str]]





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   name
					_snake_case					:							str					     =   level
					_snake_case					:							Optional[Any]					     =   lines
					_snake_case					:							Any					     =   """"""
					_snake_case					:							int					     =   True
					_snake_case					:							Dict					     =   {}
					_snake_case					:							List[str]					     =   []
					_snake_case					:							Optional[Any]					     =   []
					if self.lines is not None:
						self.parse(suppress_parsing_errors=SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

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

							_snake_case					:							Optional[Any]					     =   """ """.join(line.split()[1:]      ).strip(""" \n"""      )
						else:
							current_lines.append(SCREAMING_SNAKE_CASE__      )
					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					:							Union[str, Any]					     =   Section(SCREAMING_SNAKE_CASE__						,	self.level + """#"""						,	SCREAMING_SNAKE_CASE__      )
						else:
							if current_lines != []:
								self.text += "".join(SCREAMING_SNAKE_CASE__      ).strip()
								if self.text != "" and self.text not in FILLER_TEXT:
									_snake_case					:							Dict					     =   False

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Union[str, Any]					     =   []
					_snake_case					:							Tuple					     =   []
					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					:							Optional[int]					     =   [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					:							Tuple					     =   [subsection["""name"""] for subsection in structure["""subsections"""]]
							_snake_case					:							Dict					     =   False
							for idx, name in enumerate(SCREAMING_SNAKE_CASE__      ):
								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					:							int					     =   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					:							Tuple					     =   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      ):






					"""simple docstring"""

					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 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):  # Level 0






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					super().__init__(name=SCREAMING_SNAKE_CASE__						,	level=""""""      )  # Not using lines here as we need to use a child class parse
					_snake_case					:							Optional[int]					     =   structure
					_snake_case					:							str					     =   -2
					_snake_case					:							Any					     =   0
					_snake_case					:							int					     =   lines
					if self.lines is not None:
						self.parse(suppress_parsing_errors=SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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







				@classmethod
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					with open(SCREAMING_SNAKE_CASE__						,	encoding="""utf-8"""      ) as f:
						_snake_case					:							List[str]					     =   f.readlines()
					return cls(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	suppress_parsing_errors=SCREAMING_SNAKE_CASE__      )







				@classmethod
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = "root"						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   string.split("""\n"""      )
					return cls(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	suppress_parsing_errors=SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   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					:							Optional[int]					     =   self.lines[line_count + 1 :]  # Get the last + 1 th item.
					else:
						_snake_case					:							List[str]					     =   self.lines[self.tag_count :]
					super().parse(suppress_parsing_errors=SCREAMING_SNAKE_CASE__      )







				def __str__(				self      ):






					"""simple docstring"""

					return str(self.to_dict()      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							str					     =   []
					_snake_case					:							str					     =   []
					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					:							List[str]					     =   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[str]					     =   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					:							List[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_			      =  ArgumentParser(usage='''Validate the content (excluding YAML tags) of a README.md file.''')
						ap.add_argument('''readme_filepath''')
						UpperCAmelCase_			      =  ap.parse_args()
						UpperCAmelCase_			      =  Path(args.readme_filepath)
						UpperCAmelCase_			      =  ReadMe.from_readme(readme_filepath)





