
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	_lowerCAmelCase   ):

					def __init__(     self       :   Tuple							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   float							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			feature_size
										lowercase_  							=			sampling_rate
										lowercase_  							=			padding_value

										lowercase_  							=			kwargs.pop("""padding_side"""							,					"""right"""       )
										lowercase_  							=			kwargs.pop("""return_attention_mask"""							,					_lowerCAmelCase       )

										super().__init__(**_lowerCAmelCase       )

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]							,					UpperCamelCase__       :   Union[bool, str, PaddingStrategy] = True							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   bool = False							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					):






										'''simple docstring'''






										if isinstance(_lowerCAmelCase							,					(list, tuple)       ) and isinstance(processed_features[0]							,					(dict, BatchFeature)       ):
															lowercase_  							=			{
															    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
															}

										# The model's main input name, usually `input_values`, has be passed for padding
										if self.model_input_names[0] not in processed_features:
															raise ValueError(
															    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
															    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
															    F''' {list(processed_features.keys()       )}'''       )

										lowercase_  							=			processed_features[self.model_input_names[0]]
										lowercase_  							=			(
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(_lowerCAmelCase       ) == 0:
															if return_attention_mask:
																				lowercase_  							=			[]
															return processed_features

										# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
										# and rebuild them afterwards if no return_tensors is specified
										# Note that we lose the specific device the tensor may be on for PyTorch

										lowercase_  							=			required_input[0]
										if isinstance(_lowerCAmelCase							,					(list, tuple)       ):
															# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
															lowercase_  							=			0
															while len(required_input[index]       ) == 0:
																				index += 1
															if index < len(_lowerCAmelCase       ):
																				lowercase_  							=			required_input[index][0]

										if return_tensors is None:
															if is_tf_tensor(_lowerCAmelCase       ):
																				lowercase_  							=			"""tf"""
															elif is_torch_tensor(_lowerCAmelCase       ):
																				lowercase_  							=			"""pt"""
															elif isinstance(_lowerCAmelCase							,					(int, float, list, tuple, np.ndarray)       ):
																				lowercase_  							=			"""np"""
															else:
																				raise ValueError(
																				    F'''type of {first_element} unknown: {type(_lowerCAmelCase       )}. '''
																				    """Should be one of a python, numpy, pytorch or tensorflow object."""       )

										for key, value in processed_features.items():
															if isinstance(value[0]							,					(int, float)       ):
																				lowercase_  							=			to_numpy(_lowerCAmelCase       )
															else:
																				lowercase_  							=			[to_numpy(_lowerCAmelCase       ) for v in value]

        # Convert padding_strategy in PaddingStrategy
										lowercase_  							=			self._get_padding_strategies(padding=_lowerCAmelCase							,					max_length=_lowerCAmelCase       )

										lowercase_  							=			processed_features[self.model_input_names[0]]

										lowercase_  							=			len(_lowerCAmelCase       )
										if not all(len(_lowerCAmelCase       ) == batch_size for v in processed_features.values()       ):
															raise ValueError("""Some items in the output dictionary have a different batch size than others."""       )

										lowercase_  							=			[]
										for i in range(_lowerCAmelCase       ):
															lowercase_  							=			{k: v[i] for k, v in processed_features.items()}
															# truncation
															lowercase_  							=			self._truncate(
															    _lowerCAmelCase							,					max_length=_lowerCAmelCase							,					pad_to_multiple_of=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					)
															truncated_inputs.append(_lowerCAmelCase       )

										if padding_strategy == PaddingStrategy.LONGEST:
															# make sure that `max_length` cannot be longer than the longest truncated length
															lowercase_  							=			max(len(input_slice[self.model_input_names[0]]       ) for input_slice in truncated_inputs       )
															lowercase_  							=			PaddingStrategy.MAX_LENGTH

										lowercase_  							=			{}
										for i in range(_lowerCAmelCase       ):
															# padding
															lowercase_  							=			self._pad(
															    truncated_inputs[i]							,					max_length=_lowerCAmelCase							,					padding_strategy=_lowerCAmelCase							,					pad_to_multiple_of=_lowerCAmelCase							,					return_attention_mask=_lowerCAmelCase							,					)

															for key, value in outputs.items():
																				if key not in batch_outputs:
																									lowercase_  							=			[]
																				if value.dtype is np.dtype(np.floataa       ):
																									lowercase_  							=			value.astype(np.floataa       )
																				batch_outputs[key].append(_lowerCAmelCase       )

										return BatchFeature(_lowerCAmelCase							,					tensor_type=_lowerCAmelCase       )

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Union[Dict[str, np.ndarray], BatchFeature]							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					):






										'''simple docstring'''






										lowercase_  							=			processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
															lowercase_  							=			len(_lowerCAmelCase       )

										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
															lowercase_  							=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										lowercase_  							=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowerCAmelCase       ) < max_length

										if return_attention_mask and "attention_mask" not in processed_features:
															lowercase_  							=			np.ones(len(_lowerCAmelCase       )							,					dtype=np.intaa       )

										if needs_to_be_padded:
															lowercase_  							=			max_length - len(_lowerCAmelCase       )
															if self.padding_side == "right":
																				if return_attention_mask:
																									lowercase_  							=			np.pad(
																									    processed_features["""attention_mask"""]							,					(0, difference)       )
																				lowercase_  							=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																				lowercase_  							=			np.pad(
																				    _lowerCAmelCase							,					_lowerCAmelCase							,					"""constant"""							,					constant_values=self.padding_value       )
															elif self.padding_side == "left":
																				if return_attention_mask:
																									lowercase_  							=			np.pad(
																									    processed_features["""attention_mask"""]							,					(difference, 0)       )
																				lowercase_  							=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																				lowercase_  							=			np.pad(
																				    _lowerCAmelCase							,					_lowerCAmelCase							,					"""constant"""							,					constant_values=self.padding_value       )
															else:
																				raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

										return processed_features

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   Union[Dict[str, np.ndarray], BatchFeature]							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					):






										'''simple docstring'''






										if not truncation:
															return processed_features
										elif truncation and max_length is None:
															raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""       )

										lowercase_  							=			processed_features[self.model_input_names[0]]

										# find `max_length` that fits `pad_to_multiple_of`
										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
															lowercase_  							=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										lowercase_  							=			len(_lowerCAmelCase       ) > max_length

										if needs_to_be_truncated:
															lowercase_  							=			processed_features[self.model_input_names[0]][:max_length]
															if "attention_mask" in processed_features:
																				lowercase_  							=			processed_features["""attention_mask"""][:max_length]

										return processed_features







					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   List[Any]=False							,					UpperCamelCase__       :   Tuple=None       ):






										'''simple docstring'''






										if padding is not False:
															if padding is True:
																				lowercase_  							=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
															elif not isinstance(_lowerCAmelCase							,					_lowerCAmelCase       ):
																				lowercase_  							=			PaddingStrategy(_lowerCAmelCase       )
															elif isinstance(_lowerCAmelCase							,					_lowerCAmelCase       ):
																				lowercase_  							=			padding
										else:
															lowercase_  							=			PaddingStrategy.DO_NOT_PAD

										# Set max length if needed
										if max_length is None:
															if padding_strategy == PaddingStrategy.MAX_LENGTH:
																				raise ValueError(
																				    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''       )

        # Test if we have a padding value
										if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
															raise ValueError(
															    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
															    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""       )

										return padding_strategy






import cva
import numpy as np







class        UpperCamelCase__   :

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										if k in (0.04, 0.06):
															lowercase_  							=			k
															lowercase_  							=			window_size
										else:
															raise ValueError("""invalid k value"""       )

					def __str__(     self       :   Optional[int]       ):






										'''simple docstring'''






										return str(self.k       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			cva.imread(UpperCamelCase__							,					0       )
										lowercase_							,					lowercase_  							=			img.shape
										lowercase_  							=			[]
										lowercase_  							=			img.copy()
										lowercase_  							=			cva.cvtColor(UpperCamelCase__							,					cva.COLOR_GRAY2RGB       )
										lowercase_							,					lowercase_  							=			np.gradient(UpperCamelCase__       )
										lowercase_  							=			dx**2
										lowercase_  							=			dy**2
										lowercase_  							=			dx * dy
										lowercase_  							=			0.04
										lowercase_  							=			self.window_size // 2
										for y in range(UpperCamelCase__							,					h - offset       ):
															for x in range(UpperCamelCase__							,					w - offset       ):
																				lowercase_  							=			ixx[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowercase_  							=			iyy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowercase_  							=			ixy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()

																				lowercase_  							=			(wxx * wyy) - (wxy**2)
																				lowercase_  							=			wxx + wyy
																				lowercase_  							=			det - k * (trace**2)
																				# Can change the value
																				if r > 0.5:
																									corner_list.append([x, y, r]       )
																									color_img.itemset((y, x, 0)							,					0       )
																									color_img.itemset((y, x, 1)							,					0       )
																									color_img.itemset((y, x, 2)							,					255       )
										return color_img, corner_list


if __name__ == "__main__":
							a					  =     HarrisCorner(0.04, 3)
							a  ,					a					  =     edge_detect.detect('path_to_image')
							cva.imwrite('detect.png', color_img)


