




"""simple docstring"""







from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase				:		Any												=				k_size // 2
		__lowercase						,__lowercase				:		Union[str, Any]												=				mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		__lowercase				:		List[Any]												=				1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase				) + square(_lowerCamelCase				)) / (2 * square(_lowerCamelCase				))				)
		return g

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Optional[Any]												=				image.shape[0], image.shape[1]
		# dst image height and width
		__lowercase				:		Union[str, Any]												=				height - k_size + 1
		__lowercase				:		Tuple												=				width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		__lowercase				:		str												=				zeros((dst_height * dst_width, k_size * k_size)				)
		__lowercase				:		str												=				0
		for i, j in product(range(_lowerCamelCase				),  range(_lowerCamelCase				)				):
				__lowercase				:		str												=				ravel(image[i : i + k_size, j : j + k_size]				)
				__lowercase				:		Union[str, Any]												=				window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		__lowercase				:		Dict												=				gen_gaussian_kernel(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		List[str]												=				ravel(_lowerCamelCase				)

		# reshape and get the dst image
		__lowercase				:		Union[str, Any]												=				dot(_lowerCamelCase,  _lowerCamelCase				).reshape(_lowerCamelCase,  _lowerCamelCase				).astype(_lowerCamelCase				)

		return dst


if __name__ == "__main__":
						# read original image
						__A   :  str					=      imread(R'../image_data/lena.jpg')
						# turn image in gray scale value
						__A   :  Dict					=      cvtColor(img, COLOR_BGR2GRAY)

						# get values with two different mask size
						__A   :  Optional[int]					=      gaussian_filter(gray, 3, sigma=1)
						__A   :  List[Any]					=      gaussian_filter(gray, 5, sigma=0.8)

						# show result images
						imshow('gaussian filter with 3x3 mask', gaussianaxa)
						imshow('gaussian filter with 5x5 mask', gaussianaxa)
						waitKey()






"""simple docstring"""







import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map

def 						snake_case__       (					_lowerCamelCase				)			->List[Tuple[int, ...]]:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				[]
		if isinstance(_lowerCamelCase,  _lowerCamelCase				):
				for v in tree.values():
						shapes.extend(_fetch_dims(_lowerCamelCase				)				)
		elif isinstance(_lowerCamelCase,  (list, tuple)				):
				for t in tree:
						shapes.extend(_fetch_dims(_lowerCamelCase				)				)
		elif isinstance(_lowerCamelCase,  torch.Tensor				):
				shapes.append(tree.shape				)
		else:
				raise ValueError("Not supported"				)

		return shapes

@torch.jit.ignore
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple[int, ...]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				[]
		for d in reversed(_lowerCamelCase				):
				idx.append(flat_idx % d				)
				__lowercase				:		List[Any]												=				flat_idx // d

		return tuple(reversed(_lowerCamelCase				)				)

@torch.jit.ignore
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = None,  _lowerCamelCase = None,  )			->List[Tuple[slice, ...]]:






		"""simple docstring"""


		def reduce_edge_list(_lowerCamelCase				) -> None:
				__lowercase				:		Optional[Any]												=				True
				for i in range(len(_lowerCamelCase				)				):
						__lowercase				:		Any												=				-1 * (i + 1)
						l[reversed_idx] &= tally
						__lowercase				:		List[str]												=				l[reversed_idx]

		if start_edges is None:
				__lowercase				:		Optional[Any]												=				[s == 0 for s in start]
				reduce_edge_list(_lowerCamelCase				)
		if end_edges is None:
				__lowercase				:		Dict												=				[e == (d - 1) for e, d in zip(_lowerCamelCase,  _lowerCamelCase				)]
				reduce_edge_list(_lowerCamelCase				)

		# Base cases. Either start/end are empty and we're done, or the final,
		# one-dimensional tensor can be simply sliced
		if len(_lowerCamelCase				) == 0:
				return [()]
		elif len(_lowerCamelCase				) == 1:
				return [(slice(start[0],  end[0] + 1				),)]

		__lowercase				:		List[Tuple[slice, ...]]												=				[]
		__lowercase				:		List[slice]												=				[]

		# Dimensions common to start and end can be selected directly
		for s, e in zip(_lowerCamelCase,  _lowerCamelCase				):
				if s == e:
						path_list.append(slice(_lowerCamelCase,  s + 1				)				)
				else:
						break

		__lowercase				:		Tuple[slice, ...]												=				tuple(_lowerCamelCase				)
		__lowercase				:		Any												=				len(_lowerCamelCase				)

		# start == end, and we're done
		if divergence_idx == len(_lowerCamelCase				):
				return [path]

		def upper() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				__lowercase				:		Any												=				start[divergence_idx]
				return tuple(
				    path + (slice(_lowerCamelCase,  sdi + 1				),) + s
				    for s in _get_minimal_slice_set(
				        start[divergence_idx + 1 :],  [d - 1 for d in dims[divergence_idx + 1 :]],  dims[divergence_idx + 1 :],  start_edges=start_edges[divergence_idx + 1 :],  end_edges=[True for _ in end_edges[divergence_idx + 1 :]],  )				)

		def lower() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				__lowercase				:		int												=				end[divergence_idx]
				return tuple(
				    path + (slice(_lowerCamelCase,  edi + 1				),) + s
				    for s in _get_minimal_slice_set(
				        [0 for _ in start[divergence_idx + 1 :]],  end[divergence_idx + 1 :],  dims[divergence_idx + 1 :],  start_edges=[True for _ in start_edges[divergence_idx + 1 :]],  end_edges=end_edges[divergence_idx + 1 :],  )				)

		# If both start and end are at the edges of the subtree rooted at
		# divergence_idx, we can just select the whole subtree at once
		if start_edges[divergence_idx] and end_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx],  end[divergence_idx] + 1				),)				)
		# If just start is at the edge, we can grab almost all of the subtree,
		# treating only the ragged bottom edge as an edge case
		elif start_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx],  end[divergence_idx]				),)				)
				slices.extend(lower()				)
		# Analogous to the previous case, but the top is ragged this time
		elif end_edges[divergence_idx]:
				slices.extend(upper()				)
				slices.append(path + (slice(start[divergence_idx] + 1,  end[divergence_idx] + 1				),)				)
		# If both sides of the range are ragged, we need to handle both sides
		# separately. If there's contiguous meat in between them, we can index it
		# in one big chunk
		else:
				slices.extend(upper()				)
				__lowercase				:		int												=				end[divergence_idx] - start[divergence_idx]
				if middle_ground > 1:
						slices.append(path + (slice(start[divergence_idx] + 1,  end[divergence_idx]				),)				)
				slices.extend(lower()				)

		return slices

@torch.jit.ignore
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->torch.Tensor:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				t.shape[:no_batch_dims]
		__lowercase				:		Dict												=				list(_flat_idx_to_idx(_lowerCamelCase,  _lowerCamelCase				)				)
		# _get_minimal_slice_set is inclusive
		__lowercase				:		List[str]												=				list(_flat_idx_to_idx(flat_end - 1,  _lowerCamelCase				)				)

		# Get an ordered list of slices to perform
		__lowercase				:		Dict												=				_get_minimal_slice_set(
		    _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )

		__lowercase				:		Optional[Any]												=				[t[s] for s in slices]

		return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]				) for s in sliced_tensors]				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = False,  _lowerCamelCase = None,  _lowerCamelCase = False,  )			->Any:






		"""simple docstring"""


		if not (len(_lowerCamelCase				) > 0):
				raise ValueError("Must provide at least one input"				)

		__lowercase				:		Dict												=				[shape[:no_batch_dims] for shape in _fetch_dims(_lowerCamelCase				)]
		__lowercase				:		List[Any]												=				tuple([max(_lowerCamelCase				) for s in zip(*_lowerCamelCase				)]				)

		def _prep_inputs(_lowerCamelCase				) -> torch.Tensor:
				if not low_mem:
						if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
								__lowercase				:		Optional[int]												=				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
						__lowercase				:		Optional[Any]												=				t.reshape(-1,  *t.shape[no_batch_dims:]				)
				else:
						__lowercase				:		Union[str, Any]												=				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
				return t

		__lowercase				:		Dict[str, Any]												=				tensor_tree_map(_prep_inputs,  _lowerCamelCase				)
		__lowercase				:		str												=				None
		if _out is not None:
				__lowercase				:		Union[str, Any]												=				tensor_tree_map(lambda _lowerCamelCase				: t.view([-1] + list(t.shape[no_batch_dims:]				)				),  _out				)

		__lowercase				:		int												=				1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		__lowercase				:		Dict												=				flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

		def _select_chunk(_lowerCamelCase				) -> torch.Tensor:
				return t[i : i + chunk_size] if t.shape[0] != 1 else t

		__lowercase				:		Dict												=				0
		__lowercase				:		List[str]												=				prepped_outputs
		for _ in range(_lowerCamelCase				):
				# Chunk the input
				if not low_mem:
						__lowercase				:		Dict												=				_select_chunk
				else:
						__lowercase				:		List[Any]												=				partial(
						    _chunk_slice,  flat_start=_lowerCamelCase,  flat_end=min(_lowerCamelCase,  i + chunk_size				),  no_batch_dims=len(_lowerCamelCase				),  )

				__lowercase				:		Dict[str, Any]												=				tensor_tree_map(_lowerCamelCase,  _lowerCamelCase				)

				# Run the layer on the chunk
				__lowercase				:		int												=				layer(**_lowerCamelCase				)

				# Allocate space for the output
				if out is None:
						__lowercase				:		int												=				tensor_tree_map(lambda _lowerCamelCase				: t.new_zeros((flat_batch_dim,) + t.shape[1:]				),  _lowerCamelCase				)

				# Put the chunk in its pre-allocated space
				if isinstance(_lowerCamelCase,  _lowerCamelCase				):

						def assign(_lowerCamelCase,  _lowerCamelCase				) -> None:
								for k, v in da.items():
										if isinstance(_lowerCamelCase,  _lowerCamelCase				):
												assign(_lowerCamelCase,  da[k]				)
										else:
												if _add_into_out:
														v[i : i + chunk_size] += da[k]
												else:
														__lowercase				:		Optional[Any]												=				da[k]

						assign(_lowerCamelCase,  _lowerCamelCase				)
				elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
						for xa, xa in zip(_lowerCamelCase,  _lowerCamelCase				):
								if _add_into_out:
										xa[i : i + chunk_size] += xa
								else:
										__lowercase				:		Optional[int]												=				xa
				elif isinstance(_lowerCamelCase,  torch.Tensor				):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								__lowercase				:		int												=				output_chunk
				else:
						raise ValueError("Not supported"				)

				i += chunk_size

		__lowercase				:		int												=				tensor_tree_map(lambda _lowerCamelCase				: t.view(orig_batch_dims + t.shape[1:]				),  _lowerCamelCase				)

		return out





class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	int = 5_1_2    ,   ):
						__lowercase				:		str												=				max_chunk_size
						__lowercase				:		Optional[int]												=				None
						__lowercase				:		Optional[tuple]												=				None



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int						):
						logging.info("Tuning chunk size..."						)

						if min_chunk_size >= self.max_chunk_size:
								return min_chunk_size

						__lowercase				:		List[int]												=				[2**l for l in range(int(math.log(self.max_chunk_size    ,   2						)						) + 1						)]
						__lowercase				:		Optional[int]												=				[c for c in candidates if c > min_chunk_size]
						__lowercase				:		str												=				[min_chunk_size] + candidates
						candidates[-1] += 4

						def test_chunk_size(lowercase__					:	int						) -> bool:
								try:
										with torch.no_grad():
												fn(*lowercase__    ,   chunk_size=lowercase__						)
										return True
								except RuntimeError:
										return False

						__lowercase				:		Union[str, Any]												=				0
						__lowercase				:		Optional[Any]												=				len(lowercase__						) - 1
						while i > min_viable_chunk_size_index:
								__lowercase				:		List[Any]												=				test_chunk_size(candidates[i]						)
								if not viable:
										__lowercase				:		Tuple												=				(min_viable_chunk_size_index + i) // 2
								else:
										__lowercase				:		Optional[Any]												=				i
										__lowercase				:		List[Any]												=				(i + len(lowercase__						) - 1) // 2

						return candidates[min_viable_chunk_size_index]



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Iterable    ,   lowercase__					:	Iterable						):
						__lowercase				:		List[Any]												=				True
						for aa, aa in zip(lowercase__    ,   lowercase__						):
								assert type(lowercase__						) == type(lowercase__						)
								if isinstance(lowercase__    ,   (list, tuple)						):
										consistent &= self._compare_arg_caches(lowercase__    ,   lowercase__						)
								elif isinstance(lowercase__    ,   lowercase__						):
										__lowercase				:		Dict												=				[v for _, v in sorted(aa.items()    ,   key=lambda lowercase__						: x[0]						)]
										__lowercase				:		Optional[int]												=				[v for _, v in sorted(aa.items()    ,   key=lambda lowercase__						: x[0]						)]
										consistent &= self._compare_arg_caches(lowercase__    ,   lowercase__						)
								else:
										consistent &= aa == aa

						return consistent



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int    ,   ):
						__lowercase				:		Tuple												=				True
						__lowercase				:		tuple												=				tree_map(lambda lowercase__						: a.shape if isinstance(lowercase__    ,   torch.Tensor						) else a    ,   lowercase__    ,   lowercase__						)
						if self.cached_arg_data is not None:
								# If args have changed shape/value, we need to re-tune
								assert len(self.cached_arg_data						) == len(lowercase__						)
								__lowercase				:		Optional[int]												=				self._compare_arg_caches(self.cached_arg_data    ,   lowercase__						)
						else:
								# Otherwise, we can reuse the precomputed value
								__lowercase				:		int												=				False

						if not consistent:
								__lowercase				:		List[Any]												=				self._determine_favorable_chunk_size(
								    lowercase__    ,   lowercase__    ,   lowercase__    ,   )
								__lowercase				:		Union[str, Any]												=				arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size
