


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCAmelCase__   	=				logging.get_logger(__name__)







@dataclass
class   lowerCAmelCase__ :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				None
     __SCREAMING_SNAKE_CASE						    =				None


     def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
            return json.dumps(dataclasses.asdict(self)				,		indent=2) + "\n"







@dataclass(frozen=a)
class   lowerCAmelCase__ :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				None
     __SCREAMING_SNAKE_CASE						    =				None
     __SCREAMING_SNAKE_CASE						    =				None


     def 				_lowerCamelCase				(		self)    ->       Optional[int]:
            return json.dumps(dataclasses.asdict(self)) + "\n"







class   lowerCAmelCase__ :




     '''simple docstring'''


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Tuple:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       int:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self)    ->       Any:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       List[Any]:
            if len(self.get_labels()) > 1:
                   _A   : Dict       					=				self.get_labels()[int(example.label)]
            return example


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Optional[Any]:
            with open(__lowerCamelCase				,		"r"				,		encoding="utf-8-sig") as f:
                   return list(csv.reader(__lowerCamelCase				,		delimiter="\t"				,		quotechar=__lowerCamelCase))







class   lowerCAmelCase__ (       a):




     '''simple docstring'''


     def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase="classification"				,		__lowerCamelCase=False)    ->       Dict:
            _A   : Optional[Any]       					=				[] if labels is None else labels
            _A   : Dict       					=				[] if examples is None else examples
            _A   : Any       					=				mode
            _A   : Optional[int]       					=				verbose


     def __len__(		self)    ->       Any:
            return len(self.examples)


     def __getitem__(		self				,		__lowerCamelCase)    ->       Optional[int]:
            if isinstance(__lowerCamelCase				,		__lowerCamelCase):
                   return SingleSentenceClassificationProcessor(labels=self.labels				,		examples=self.examples[idx])
            return self.examples[idx]


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=""				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		**__lowerCamelCase)    ->       int:
            _A   : Optional[int]       					=				cls(**__lowerCamelCase)
            processor.add_examples_from_csv(
                __lowerCamelCase				,		split_name=__lowerCamelCase				,		column_label=__lowerCamelCase				,		column_text=__lowerCamelCase				,		column_id=__lowerCamelCase				,		skip_first_row=__lowerCamelCase				,		overwrite_labels=__lowerCamelCase				,		overwrite_examples=__lowerCamelCase				,		)
            return processor


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       str:
            _A   : List[Any]       					=				cls(**__lowerCamelCase)
            processor.add_examples(__lowerCamelCase				,		labels=__lowerCamelCase)
            return processor


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=""				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		)    ->       Dict:
            _A   : int       					=				self._read_tsv(__lowerCamelCase)
            if skip_first_row:
                   _A   : int       					=				lines[1:]
            _A   : Optional[Any]       					=				[]
            _A   : List[str]       					=				[]
            _A   : Dict       					=				[]
            for i, line in enumerate(__lowerCamelCase):
                   texts.append(line[column_text])
                   labels.append(line[column_label])
                   if column_id is not None:
                          ids.append(line[column_id])
                   else:
                          _A   : str       					=				F"{split_name}-{i}" if split_name else str(__lowerCamelCase)
                          ids.append(__lowerCamelCase)

            return self.add_examples(
                __lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		overwrite_labels=__lowerCamelCase				,		overwrite_examples=__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=False)    ->       Tuple:
            if labels is not None and len(__lowerCamelCase) != len(__lowerCamelCase):
                   raise ValueError(
                       F"Text and labels have mismatched lengths {len(__lowerCamelCase)} and {len(__lowerCamelCase)}")
            if ids is not None and len(__lowerCamelCase) != len(__lowerCamelCase):
                   raise ValueError(F"Text and ids have mismatched lengths {len(__lowerCamelCase)} and {len(__lowerCamelCase)}")
            if ids is None:
                   _A   : Optional[Any]       					=				[None] * len(__lowerCamelCase)
            if labels is None:
                   _A   : Union[str, Any]       					=				[None] * len(__lowerCamelCase)
            _A   : Optional[int]       					=				[]
            _A   : List[Any]       					=				set()
            for text_or_text_and_label, label, guid in zip(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase):
                   if isinstance(__lowerCamelCase				,		(tuple, list)) and label is None:
                          _A   : List[str]       					=				text_or_text_and_label
                   else:
                          _A   : Union[str, Any]       					=				text_or_text_and_label
                   added_labels.add(__lowerCamelCase)
                   examples.append(InputExample(guid=__lowerCamelCase				,		text_a=__lowerCamelCase				,		text_b=__lowerCamelCase				,		label=__lowerCamelCase))

            # Update examples
            if overwrite_examples:
                   _A   : Optional[Any]       					=				examples
            else:
                   self.examples.extend(__lowerCamelCase)

            # Update labels
            if overwrite_labels:
                   _A   : Optional[Any]       					=				list(__lowerCamelCase)
            else:
                   _A   : Any       					=				list(set(self.labels).union(__lowerCamelCase))

            return self.examples


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
            if max_length is None:
                   _A   : Optional[int]       					=				tokenizer.max_len

            _A   : Any       					=				{label: i for i, label in enumerate(self.labels)}

            _A   : Optional[Any]       					=				[]
            for ex_index, example in enumerate(self.examples):
                   if ex_index % 1_0_0_0_0 == 0:
                          logger.info(F"Tokenizing example {ex_index}")

                   _A   : Any       					=				tokenizer.encode(
                       example.text_a				,		add_special_tokens=__lowerCamelCase				,		max_length=min(__lowerCamelCase				,		tokenizer.max_len)				,		)
                   all_input_ids.append(__lowerCamelCase)

            _A   : List[str]       					=				max(len(__lowerCamelCase) for input_ids in all_input_ids)

            _A   : Any       					=				[]
            for ex_index, (input_ids, example) in enumerate(zip(__lowerCamelCase				,		self.examples)):
                   if ex_index % 1_0_0_0_0 == 0:
                          logger.info(F"Writing example {ex_index}/{len(self.examples)}")
                   # The mask has 1 for real tokens and 0 for padding tokens. Only real
                   # tokens are attended to.
                   _A   : Optional[int]       					=				[1 if mask_padding_with_zero else 0] * len(__lowerCamelCase)

                   # Zero-pad up to the sequence length.
                   _A   : Optional[Any]       					=				batch_length - len(__lowerCamelCase)
                   if pad_on_left:
                          _A   : List[str]       					=				([pad_token] * padding_length) + input_ids
                          _A   : Dict       					=				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                   else:
                          _A   : Dict       					=				input_ids + ([pad_token] * padding_length)
                          _A   : int       					=				attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

                   if len(__lowerCamelCase) != batch_length:
                          raise ValueError(F"Error with input length {len(__lowerCamelCase)} vs {batch_length}")
                   if len(__lowerCamelCase) != batch_length:
                          raise ValueError(F"Error with input length {len(__lowerCamelCase)} vs {batch_length}")

                   if self.mode == "classification":
                          _A   : Tuple       					=				label_map[example.label]
                   elif self.mode == "regression":
                          _A   : Optional[int]       					=				float(example.label)
                   else:
                          raise ValueError(self.mode)

                   if ex_index < 5 and self.verbose:
                          logger.info("*** Example ***")
                          logger.info(F"guid: {example.guid}")
                          logger.info(F"input_ids: {' '.join([str(__lowerCamelCase) for x in input_ids])}")
                          logger.info(F"attention_mask: {' '.join([str(__lowerCamelCase) for x in attention_mask])}")
                          logger.info(F"label: {example.label} (id = {label})")

                   features.append(InputFeatures(input_ids=__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		label=__lowerCamelCase))

            if return_tensors is None:
                   return features
            elif return_tensors == "tf":
                   if not is_tf_available():
                          raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
                   import tensorflow as tf

                   def gen():
                          for ex in features:
                                 yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

                   _A   : Tuple       					=				tf.data.Dataset.from_generator(
                       __lowerCamelCase				,		({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)				,		({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([]))				,		)
                   return dataset
            elif return_tensors == "pt":
                   if not is_torch_available():
                          raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
                   import torch
                   from torch.utils.data import TensorDataset

                   _A   : Dict       					=				torch.tensor([f.input_ids for f in features]				,		dtype=torch.long)
                   _A   : Any       					=				torch.tensor([f.attention_mask for f in features]				,		dtype=torch.long)
                   if self.mode == "classification":
                          _A   : int       					=				torch.tensor([f.label for f in features]				,		dtype=torch.long)
                   elif self.mode == "regression":
                          _A   : int       					=				torch.tensor([f.label for f in features]				,		dtype=torch.float)

                   _A   : Dict       					=				TensorDataset(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
                   return dataset
            else:
                   raise ValueError("return_tensors should be one of 'tf' or 'pt'")



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

lowerCAmelCase__   	=				1_80
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
       _A   : int       					=				k_size // 2
       _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _A   : Union[str, Any]       					=				(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
       )
       return g
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
       _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)

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

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

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

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

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

       return destination
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any		):
       for row in range(1       ,							image_shape[0] - 1		):
              for col in range(1       ,							image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _A   : Optional[int]       					=				strong
                     elif destination[row, col] <= threshold_low:
                            _A   : Optional[int]       					=				0
                     else:
                            _A   : Union[str, Any]       					=				weak
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						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],
                            ):
                                   _A   : str       					=				strong
                            else:
                                   _A   : Any       					=				0
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
       # gaussian_filter
       _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
       _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)

       _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       detect_high_low_threshold(
           image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       return destination


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