

'''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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
							A_						=      []
							if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
														for v in tree.values():
																					shapes.extend(_fetch_dims(UpperCAmelCase__				)				)
							elif isinstance(UpperCAmelCase__,       (list, tuple)				):
														for t in tree:
																					shapes.extend(_fetch_dims(UpperCAmelCase__				)				)
							elif isinstance(UpperCAmelCase__,       torch.Tensor				):
														shapes.append(tree.shape				)
							else:
														raise ValueError("""Not supported"""				)

							return shapes
@torch.jit.ignore
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[int]:
							A_						=      []
							for d in reversed(UpperCAmelCase__				):
														idx.append(flat_idx % d				)
														A_						=      flat_idx // d

							return tuple(reversed(UpperCAmelCase__				)				)
@torch.jit.ignore
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       )			->			Tuple:




							def reduce_edge_list(UpperCAmelCase__				) -> None:
														A_						=      True
														for i in range(len(UpperCAmelCase__				)				):
																					A_						=      -1 * (i + 1)
																					l[reversed_idx] &= tally
																					A_						=      l[reversed_idx]

							if start_edges is None:
														A_						=      [s == 0 for s in start]
														reduce_edge_list(UpperCAmelCase__				)
							if end_edges is None:
														A_						=      [e == (d - 1) for e, d in zip(UpperCAmelCase__,       UpperCAmelCase__				)]
														reduce_edge_list(UpperCAmelCase__				)

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

							A_						=      []
							A_						=      []

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

							A_						=      tuple(UpperCAmelCase__				)
							A_						=      len(UpperCAmelCase__				)

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

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

														A_						=      start[divergence_idx]
														return tuple(
														    path + (slice(UpperCAmelCase__,       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

														A_						=      end[divergence_idx]
														return tuple(
														    path + (slice(UpperCAmelCase__,       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()				)
														A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			int:
							A_						=      t.shape[:no_batch_dims]
							A_						=      list(_flat_idx_to_idx(UpperCAmelCase__,       UpperCAmelCase__				)				)
							# _get_minimal_slice_set is inclusive
							A_						=      list(_flat_idx_to_idx(flat_end - 1,       UpperCAmelCase__				)				)

							# Get an ordered list of slices to perform
							A_						=      _get_minimal_slice_set(
							    UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

							A_						=      [t[s] for s in slices]

							return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]				) for s in sliced_tensors]				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = False,       UpperCAmelCase__ = None,       UpperCAmelCase__ = False,       )			->			Dict:




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

							A_						=      [shape[:no_batch_dims] for shape in _fetch_dims(UpperCAmelCase__				)]
							A_						=      tuple([max(UpperCAmelCase__				) for s in zip(*UpperCAmelCase__				)]				)

							def _prep_inputs(UpperCAmelCase__				) -> torch.Tensor:
														if not low_mem:
																					if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
																												A_						=      t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
																					A_						=      t.reshape(-1,       *t.shape[no_batch_dims:]				)
														else:
																					A_						=      t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
														return t

							A_						=      tensor_tree_map(_prep_inputs,       UpperCAmelCase__				)
							A_						=      None
							if _out is not None:
														A_						=      tensor_tree_map(lambda UpperCAmelCase__				: t.view([-1] + list(t.shape[no_batch_dims:]				)				),       _out				)

							A_						=      1
							for d in orig_batch_dims:
														flat_batch_dim *= d

							A_						=      flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

							A_						=      0
							A_						=      prepped_outputs
							for _ in range(UpperCAmelCase__				):
														# Chunk the input
														if not low_mem:
																					A_						=      _select_chunk
														else:
																					A_						=      partial(
																					    _chunk_slice,       flat_start=UpperCAmelCase__,       flat_end=min(UpperCAmelCase__,       i + chunk_size				),       no_batch_dims=len(UpperCAmelCase__				),       )

														A_						=      tensor_tree_map(UpperCAmelCase__,       UpperCAmelCase__				)

														# Run the layer on the chunk
														A_						=      layer(**UpperCAmelCase__				)

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

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

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

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

														i += chunk_size

							A_						=      tensor_tree_map(lambda UpperCAmelCase__				: t.view(orig_batch_dims + t.shape[1:]				),       UpperCAmelCase__				)

							return out







class 					A__      :



				def __init__(					self		,				UpperCamelCase__ = 512		,				)						->      Any:





											'''simple docstring'''
											A_						=      max_chunk_size
											A_						=      None
											A_						=      None



				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





											'''simple docstring'''
											logging.info("""Tuning chunk size..."""       )

											if min_chunk_size >= self.max_chunk_size:
																		return min_chunk_size

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

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

											A_						=      0
											A_						=      len(UpperCamelCase__       ) - 1
											while i > min_viable_chunk_size_index:
																		A_						=      test_chunk_size(candidates[i]       )
																		if not viable:
																									A_						=      (min_viable_chunk_size_index + i) // 2
																		else:
																									A_						=      i
																									A_						=      (i + len(UpperCamelCase__       ) - 1) // 2

											return candidates[min_viable_chunk_size_index]



				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      bool:





											'''simple docstring'''
											A_						=      True
											for aa, aa in zip(UpperCamelCase__		,				UpperCamelCase__       ):
																		assert type(UpperCamelCase__       ) == type(UpperCamelCase__       )
																		if isinstance(UpperCamelCase__		,				(list, tuple)       ):
																									consistent &= self._compare_arg_caches(UpperCamelCase__		,				UpperCamelCase__       )
																		elif isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
																									A_						=      [v for _, v in sorted(aa.items()		,				key=lambda UpperCamelCase__       : x[0]       )]
																									A_						=      [v for _, v in sorted(aa.items()		,				key=lambda UpperCamelCase__       : x[0]       )]
																									consistent &= self._compare_arg_caches(UpperCamelCase__		,				UpperCamelCase__       )
																		else:
																									consistent &= aa == aa

											return consistent







				def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				)						->      int:





											'''simple docstring'''
											A_						=      True
											A_						=      tree_map(lambda UpperCamelCase__       : a.shape if isinstance(UpperCamelCase__		,				torch.Tensor       ) else a		,				UpperCamelCase__		,				UpperCamelCase__       )
											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(UpperCamelCase__       )
																		A_						=      self._compare_arg_caches(self.cached_arg_data		,				UpperCamelCase__       )
											else:
																		# Otherwise, we can reuse the precomputed value
																		A_						=      False

											if not consistent:
																		A_						=      self._determine_favorable_chunk_size(
																		    UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				)
																		A_						=      arg_data

											assert self.cached_chunk_size is not None

											return self.cached_chunk_size



'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

       # Reading the image and converting it to grayscale.
       A_						=      imread(UpperCAmelCase__,       0				)

       # Test for get_neighbors_pixel function() return not None
       A_						=      0
       A_						=      0
       A_						=      image[x_coordinate][y_coordinate]

       A_						=      lbp.get_neighbors_pixel(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert neighbors_pixels is not None

       # Test for local_binary_pattern function()
       # Create a numpy array as the same height and width of read image
       A_						=      np.zeros((image.shape[0], image.shape[1])				)

       # Iterating through the image and calculating the local binary pattern value
       # for each pixel.
       for i in range(0,       image.shape[0]				):
              for j in range(0,       image.shape[1]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
