
"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class 						a__		( __lowercase		):
   def __init__(     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Any									=				dataset
          lowercase       :							str									=				process
          lowercase       :							Union[str, Any]									=				params
   def __len__(     self							):
          return len(self.dataset							)

   def __getitem__(     self      ,       _a							):
          lowercase       :							Union[str, Any]									=				self.dataset[i]
          lowercase       :							Dict									=				self.process(_a      ,       **self.params							)
          return processed


class 						a__		( __lowercase		):
   def __init__(     self      ,       _a      ,       _a      ,       _a      ,       _a=None							):
          lowercase       :							Tuple									=				loader
          lowercase       :							List[Any]									=				infer
          lowercase       :							Tuple									=				params
          if loader_batch_size == 1:
                 # Let's spare some time by deactivating altogether
                 lowercase       :							Any									=				None
          lowercase       :							Tuple									=				loader_batch_size

          # Internal bookkeeping
          lowercase       :							Union[str, Any]									=				None
          lowercase       :							Optional[Any]									=				None
   def __len__(     self							):
          return len(self.loader							)
   def __iter__(     self							):
          lowercase       :							Dict									=				iter(self.loader							)
          return self
   def      __magic_name__     (     self							):

          if isinstance(self._loader_batch_data      ,       torch.Tensor							):
                 # Batch data is simple tensor, just fetch the slice
                 lowercase       :							List[Any]									=				self._loader_batch_data[self._loader_batch_index]
          else:
                 # Batch data is assumed to be BaseModelOutput (or dict)
                 lowercase       :							Optional[Any]									=				{}
                 for k, element in self._loader_batch_data.items():
                        if isinstance(_a      ,       _a							):
                               # Convert ModelOutput to tuple first
                               lowercase       :							int									=				element.to_tuple()
                               if isinstance(element[0]      ,       torch.Tensor							):
                                      lowercase       :							List[str]									=				tuple(el[self._loader_batch_index].unsqueeze(0							) for el in element							)
                               elif isinstance(element[0]      ,       np.ndarray							):
                                      lowercase       :							Optional[Any]									=				tuple(np.expand_dims(el[self._loader_batch_index]      ,       0							) for el in element							)
                               continue
                        if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_a      ,       _a							):
                               # Those are stored as lists of tensors so need specific unbatching.
                               if isinstance(element[0]      ,       torch.Tensor							):
                                      lowercase       :							Any									=				tuple(el[self._loader_batch_index].unsqueeze(0							) for el in element							)
                               elif isinstance(element[0]      ,       np.ndarray							):
                                      lowercase       :							Optional[Any]									=				tuple(np.expand_dims(el[self._loader_batch_index]      ,       0							) for el in element							)
                               continue
                        if element is None:
                               # This can happen for optional data that get passed around
                               lowercase       :							Tuple									=				None
                        elif isinstance(element[self._loader_batch_index]      ,       torch.Tensor							):
                               # Take correct batch data, but make it looked like batch_size=1
                               # For compatibility with other methods within transformers

                               lowercase       :							List[str]									=				element[self._loader_batch_index].unsqueeze(0							)
                        elif isinstance(element[self._loader_batch_index]      ,       np.ndarray							):
                               # Take correct batch data, but make it looked like batch_size=1
                               # For compatibility with other methods within transformers
                               lowercase       :							Union[str, Any]									=				np.expand_dims(element[self._loader_batch_index]      ,       0							)
                        else:
                               # This is typically a list, so no need to `unsqueeze`.
                               lowercase       :							int									=				element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
                 lowercase       :							Union[str, Any]									=				self._loader_batch_data.__class__(_a							)
          self._loader_batch_index += 1
          return result

   def      __magic_name__     (     self							):

          if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                 # We are currently unrolling a batch so we just need to return
                 # the current item within a batch
                 return self.loader_batch_item()

          # We're out of items within a batch
          lowercase       :							List[Any]									=				next(self.iterator							)
          lowercase       :							Union[str, Any]									=				self.infer(_a      ,       **self.params							)
          # We now have a batch of "inferred things".
          if self.loader_batch_size is not None:
                 # Try to infer the size of the batch
                 if isinstance(_a      ,       torch.Tensor							):
                        lowercase       :							List[Any]									=				processed
                 else:
                        lowercase       :							Optional[Any]									=				list(processed.keys()							)[0]
                        lowercase       :							Dict									=				processed[key]
                 if isinstance(_a      ,       _a							):
                        lowercase       :							List[str]									=				len(_a							)
                 else:
                        lowercase       :							int									=				first_tensor.shape[0]
                 if 0 < observed_batch_size < self.loader_batch_size:
                        # could be last batch so we can't unroll as many
                        # elements.
                        lowercase       :							Optional[int]									=				observed_batch_size
                 # Setting internal index to unwrap the batch
                 lowercase       :							Union[str, Any]									=				processed
                 lowercase       :							Any									=				0
                 return self.loader_batch_item()
          else:
                 # We're not unrolling batches
                 return processed


class 						a__		( __lowercase		):
   def __init__(     self      ,       _a      ,       _a      ,       _a      ,       _a=None							):
          super().__init__(_a      ,       _a      ,       _a							)
   def __iter__(     self							):
          lowercase       :							Dict									=				iter(self.loader							)
          lowercase       :							List[str]									=				None
          return self

   def      __magic_name__     (     self							):

          if self.subiterator is None:
                 lowercase       :							Any									=				self.infer(next(self.iterator							)      ,       **self.params							)
          try:
                 # Try to return next item
                 lowercase       :							List[Any]									=				next(self.subiterator							)
          except StopIteration:
                 # When a preprocess iterator ends, we can start lookig at the next item
                 # ChunkIterator will keep feeding until ALL elements of iterator
                 # all have created their subiterator and have been iterating against.
                 #
                 # Another way to look at it, is we're basically flattening lists of lists
                 # into a single list, but with generators
                 lowercase       :							List[Any]									=				self.infer(next(self.iterator							)      ,       **self.params							)
                 lowercase       :							List[str]									=				next(self.subiterator							)
          return processed


class 						a__		( __lowercase		):
   def __iter__(     self							):
          lowercase       :							Tuple									=				iter(self.loader							)
          return self

   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				False
          lowercase       :							Union[str, Any]									=				[]
          if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                 while self._loader_batch_index < self.loader_batch_size:
                        lowercase       :							Union[str, Any]									=				self.loader_batch_item()
                        lowercase       :							List[str]									=				item.pop("is_last"							)
                        accumulator.append(_a							)
                        if is_last:
                               return accumulator

          while not is_last:
                 lowercase       :							Union[str, Any]									=				self.infer(next(self.iterator							)      ,       **self.params							)
                 if self.loader_batch_size is not None:
                        if isinstance(_a      ,       torch.Tensor							):
                               lowercase       :							str									=				processed
                        else:
                               lowercase       :							Optional[Any]									=				list(processed.keys()							)[0]
                               lowercase       :							List[str]									=				processed[key]
                        if isinstance(_a      ,       _a							):
                               lowercase       :							Tuple									=				len(_a							)
                        else:
                               lowercase       :							Dict									=				first_tensor.shape[0]
                        if 0 < observed_batch_size < self.loader_batch_size:
                               # could be last batch so we can't unroll as many
                               # elements.
                               lowercase       :							List[str]									=				observed_batch_size
                        lowercase       :							List[Any]									=				processed
                        lowercase       :							str									=				0
                        while self._loader_batch_index < self.loader_batch_size:
                               lowercase       :							List[Any]									=				self.loader_batch_item()
                               lowercase       :							List[Any]									=				item.pop("is_last"							)
                               accumulator.append(_a							)
                               if is_last:
                                      return accumulator
                 else:
                        lowercase       :							Tuple									=				processed
                        lowercase       :							List[Any]									=				item.pop("is_last"							)
                        accumulator.append(_a							)
          return accumulator


class 						a__		( __lowercase		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[Any]									=				dataset
          lowercase       :							Optional[int]									=				key
   def __len__(     self							):
          return len(self.dataset							)

   def __getitem__(     self      ,       _a							):
          return self.dataset[i][self.key]


class 						a__		( __lowercase		):
   def __init__(     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				dataset
          lowercase       :							Any									=				keya
          lowercase       :							int									=				keya
   def __len__(     self							):
          return len(self.dataset							)

   def __getitem__(     self      ,       _a							):
          return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     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)  ,      2_55			)
					return color_img, corner_list


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


