
import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=None )	->     int:
   UpperCamelCase__    :		List[Any]     =				None
   # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
   if str(weight.dtype ) == "torch.bfloat16":
      # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
      UpperCamelCase__    :		List[Any]     =				weight.view(torch.intaa )
      UpperCamelCase__    :		Any     =				'bfloat16'
   UpperCamelCase__    :		Union[str, Any]     =				weight.cpu().numpy()
   UpperCamelCase__    :		List[Any]     =				os.path.join(__SCREAMING_SNAKE_CASE				,    F"""{weight_name}.dat""" )
   if index is not None:
      if dtype is None:
         UpperCamelCase__    :		Any     =				str(array.dtype )
      UpperCamelCase__    :		Any     =				{'dtype': dtype, 'shape': list(array.shape )}
   if array.ndim == 0:
      UpperCamelCase__    :		Optional[Any]     =				array[None]
   UpperCamelCase__    :		Optional[Any]     =				np.memmap(__SCREAMING_SNAKE_CASE				,    dtype=array.dtype				,    mode='w+'				,    shape=array.shape )
   UpperCamelCase__    :		Any     =				array[:]
   file_array.flush()
   return index



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     int:
   UpperCamelCase__    :		List[Any]     =				tuple(weight_info['shape'] )
   if shape == ():
      # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
      UpperCamelCase__    :		Optional[Any]     =				(1,)

   UpperCamelCase__    :		Dict     =				weight_info['dtype']
   if dtype == "bfloat16":
      # NumPy does not support bfloat16 so this was saved as a int16
      UpperCamelCase__    :		Optional[int]     =				'int16'

   UpperCamelCase__    :		Optional[Any]     =				np.memmap(__SCREAMING_SNAKE_CASE				,    dtype=__SCREAMING_SNAKE_CASE				,    shape=__SCREAMING_SNAKE_CASE				,    mode='r' )

   if len(weight_info['shape'] ) == 0:
      UpperCamelCase__    :		Tuple     =				weight[0]
   UpperCamelCase__    :		int     =				torch.tensor(__SCREAMING_SNAKE_CASE )
   if weight_info["dtype"] == "bfloat16":
      UpperCamelCase__    :		int     =				weight.view(torch.bfloataa )

   return weight



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     List[str]:
   if index is None or len(__SCREAMING_SNAKE_CASE ) == 0:
      # Nothing to save
      return

   UpperCamelCase__    :		str     =				os.path.join(__SCREAMING_SNAKE_CASE				,    'index.json' )
   if os.path.isfile(__SCREAMING_SNAKE_CASE ):
      with open(__SCREAMING_SNAKE_CASE				,    'r'				,    encoding='utf-8' ) as f:
         UpperCamelCase__    :		Optional[Any]     =				json.load(__SCREAMING_SNAKE_CASE )
   else:
      UpperCamelCase__    :		int     =				{}
   current_index.update(__SCREAMING_SNAKE_CASE )

   with open(__SCREAMING_SNAKE_CASE				,    'w'				,    encoding='utf-8' ) as f:
      json.dump(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    indent=2 )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
   os.makedirs(__SCREAMING_SNAKE_CASE				,    exist_ok=__SCREAMING_SNAKE_CASE )
   UpperCamelCase__    :		Tuple     =				{}
   for name, parameter in state_dict.items():
      UpperCamelCase__    :		Optional[int]     =				offload_weight(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    index=__SCREAMING_SNAKE_CASE )

   # Update index
   save_offload_index(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )



class 			UpperCamelCase_			(						__UpperCamelCase   ):




       '''simple docstring'''

       def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase) ->	List[Any]:
          UpperCamelCase__    :		Dict     =				dataset
          UpperCamelCase__    :		str     =				prefix
       def __getitem__(				self     ,						UpperCamelCase) ->	int:
          return self.dataset[F"""{self.prefix}{key}"""]
       def __iter__(				self) ->	List[str]:
          return iter([key for key in self.dataset if key.startswith(self.prefix)])






       def __len__(				self) ->	Union[str, Any]:
          return len(self.dataset)



class 			UpperCamelCase_			(						__UpperCamelCase   ):




       '''simple docstring'''

       def __init__(				self     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase=None     ,						) ->	Optional[int]:
          if state_dict is None and save_folder is None:
             raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.')

          UpperCamelCase__    :		int     =				{} if state_dict is None else state_dict
          UpperCamelCase__    :		Tuple     =				save_folder
          if index is None and save_folder is not None:
             with open(os.path.join(UpperCamelCase     ,						'index.json')) as f:
                UpperCamelCase__    :		Tuple     =				json.load(UpperCamelCase)
          UpperCamelCase__    :		int     =				{} if index is None else index
          UpperCamelCase__    :		Any     =				list(self.state_dict.keys())
          self.all_keys.extend([key for key in self.index if key not in self.all_keys])
          UpperCamelCase__    :		List[str]     =				device
       def __getitem__(				self     ,						UpperCamelCase) ->	Optional[int]:
          # State dict gets priority
          if key in self.state_dict:
             return self.state_dict[key]
          UpperCamelCase__    :		Dict     =				self.index[key]
          if weight_info.get('safetensors_file') is not None:
             if not is_safetensors_available():
                raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.')

             from safetensors import safe_open

             UpperCamelCase__    :		Optional[Any]     =				'cpu' if self.device is None else self.device
             with safe_open(weight_info['safetensors_file']     ,						framework='pt'     ,						device=UpperCamelCase) as f:
                UpperCamelCase__    :		Dict     =				f.get_tensor(weight_info.get('weight_name'     ,						UpperCamelCase))

             if "dtype" in weight_info:
                return tensor.to(getattr(UpperCamelCase     ,						weight_info['dtype']))
             else:
                return tensor

          UpperCamelCase__    :		Any     =				os.path.join(self.save_folder     ,						F"""{key}.dat""")
          return load_offloaded_weight(UpperCamelCase     ,						UpperCamelCase)
       def __iter__(				self) ->	str:
          return iter(self.all_keys)






       def __len__(				self) ->	int:
          return len(self.all_keys)



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Optional[Any]:
   UpperCamelCase__    :		Any     =				{}
   for module_name in submodule_names:
      # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
      # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
      result.update(
          {
              key: param
              for key, param in state_dict.items()
              if key == module_name or key.startswith(module_name + '.' )
          } )
   return result





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

UpperCAmelCase__				:  str       					=				180



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			UpperCamelCase__    :		Dict     =				k_size // 2
			UpperCamelCase__   ,			UpperCamelCase__    :		Any     =				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			UpperCamelCase__    :		int     =				(
			    1
			    / (2 * np.pi * sigma)
			    * np.exp(-(np.square(__SCREAMING_SNAKE_CASE ) + np.square(__SCREAMING_SNAKE_CASE )) / (2 * np.square(__SCREAMING_SNAKE_CASE )) )
			)
			return g



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			UpperCamelCase__    :		Dict     =				np.zeros(__SCREAMING_SNAKE_CASE )

			for row in range(1				,    image_shape[0] - 1 ):
						for col in range(1				,    image_shape[1] - 1 ):
									UpperCamelCase__    :		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
									):
												UpperCamelCase__    :		List[str]     =				sobel_grad[row, col - 1]
												UpperCamelCase__    :		Dict     =				sobel_grad[row, col + 1]
												if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
															UpperCamelCase__    :		Tuple     =				sobel_grad[row, col]

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

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

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

			return destination



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Tuple:
			for row in range(1				,    image_shape[0] - 1 ):
						for col in range(1				,    image_shape[1] - 1 ):
									if destination[row, col] >= threshold_high:
												UpperCamelCase__    :		str     =				strong
									elif destination[row, col] <= threshold_low:
												UpperCamelCase__    :		List[str]     =				0
									else:
												UpperCamelCase__    :		int     =				weak



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     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],
												):
															UpperCamelCase__    :		List[str]     =				strong
												else:
															UpperCamelCase__    :		Union[str, Any]     =				0



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=15				,    __SCREAMING_SNAKE_CASE=30				,    __SCREAMING_SNAKE_CASE=128				,    __SCREAMING_SNAKE_CASE=255 )	->     Any:
			# gaussian_filter
			UpperCamelCase__    :		Optional[int]     =				img_convolve(__SCREAMING_SNAKE_CASE				,    gen_gaussian_kernel(9				,    sigma=1.4 ) )
			# get the gradient and degree by sobel_filter
			UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				sobel_filter(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Tuple     =				PI + np.radadeg(__SCREAMING_SNAKE_CASE )

			UpperCamelCase__    :		Any     =				suppress_non_maximum(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			detect_high_low_threshold(
			    image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			track_edge(image.shape				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			return destination


if __name__ == "__main__":
							# read original image in gray mode
							UpperCAmelCase__				:  List[str]       					=				cva.imread(r'''../image_data/lena.jpg''', 0)
							# canny edge detection
							UpperCAmelCase__				:  Tuple       					=				canny(lena)
							cva.imshow('''canny''', canny_destination)
							cva.waitKey(0)



