
import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
__magic_name__  :  Dict     			=   """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
__magic_name__  :  List[str]     			=   """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
__magic_name__  :  Union[str, Any]     			=   """"""

# TODO: Add the licence for the dataset here if you can find it
__magic_name__  :  Optional[Any]     			=   """"""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
__magic_name__  :  List[Any]     			=   {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}


class 							lowerCamelCase       (		datasets.GeneratorBasedBuilder					):



  """simple docstring"""





  lowerCAmelCase_								=		datasets.Version("""1.1.0"""					)

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  lowerCAmelCase_								=		[
      datasets.BuilderConfig(name="""first_domain"""			,  version=__UpperCAmelCase			,  description="""This part of my dataset covers a first domain"""					),
      datasets.BuilderConfig(name="""second_domain"""			,  version=__UpperCAmelCase			,  description="""This part of my dataset covers a second domain"""					),
  ]

  lowerCAmelCase_								=		"""first_domain"""  # It's not mandatory to have a default configuration. Just use one if it make sense.

  def 					lowercase_						(					self					):
      # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
      if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
          A_								=					datasets.Features(
              {
                  "sentence": datasets.Value("string"					),
                  "option1": datasets.Value("string"					),
                  "answer": datasets.Value("string"					)
                  # These are the features of your dataset like images, labels ...
              }					)
      else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
          A_								=					datasets.Features(
              {
                  "sentence": datasets.Value("string"					),
                  "option2": datasets.Value("string"					),
                  "second_domain_answer": datasets.Value("string"					)
                  # These are the features of your dataset like images, labels ...
              }					)
      return datasets.DatasetInfo(
          # This is the description that will appear on the datasets page.
          description=_DESCRIPTION						,    features=__UpperCamelCase						,    homepage=_HOMEPAGE						,    license=_LICENSE						,    citation=_CITATION						,    )

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
      # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

      # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
      # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
      # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
      A_								=					_URLS[self.config.name]
      A_								=					dl_manager.download_and_extract(__UpperCamelCase					)
      return [
          datasets.SplitGenerator(
              name=datasets.Split.TRAIN						,    gen_kwargs={
                  "filepath": os.path.join(__UpperCamelCase						,    "train.jsonl"					),
                  "split": "train",
              }						,    ),
          datasets.SplitGenerator(
              name=datasets.Split.VALIDATION						,    gen_kwargs={
                  "filepath": os.path.join(__UpperCamelCase						,    "dev.jsonl"					),
                  "split": "dev",
              }						,    ),
          datasets.SplitGenerator(
              name=datasets.Split.TEST						,    gen_kwargs={
                  "filepath": os.path.join(__UpperCamelCase						,    "test.jsonl"					),
                  "split": "test"
              }						,    ),
      ]

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
      # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
      with open(__UpperCamelCase						,    encoding="utf-8"					) as f:
          for key, row in enumerate(__UpperCamelCase					):
              A_								=					json.loads(__UpperCamelCase					)
              if self.config.name == "first_domain":
                  # Yields examples as (key, example) tuples
                  yield key, {
                      "sentence": data["sentence"],
                      "option1": data["option1"],
                      "answer": "" if split == "test" else data["answer"],
                  }
              else:
                  yield key, {
                      "sentence": data["sentence"],
                      "option2": data["option2"],
                      "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                  }








'''simple docstring'''







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		_SCREAMING_SNAKE_CASE					=			gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						for row in range(1						,      image_shape[0]	):
												for col in range(1						,      image_shape[1]	):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)