


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


A   :					Union[str, Any]	=      logging.get_logger(__name__)

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						r"\w+[.]\d+"
      lowerCamelCase__	:       Optional[Any]					=						re.findall(_A					,      _A		)
      for pat in pats:
            lowerCamelCase__	:       Tuple					=						key.replace(_A					,      "_".join(pat.split("."		)		)		)
      return key

def    lowercase_	(	_A    :					Any					,      _A    :					int					,      _A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						pt_tuple_key[:-1] + ("scale",)
      if (
          any("norm" in str_ for str_ in pt_tuple_key		)
          and (pt_tuple_key[-1] == "bias")
          and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
          and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
      ):
            lowerCamelCase__	:       Tuple					=						pt_tuple_key[:-1] + ("scale",)
            return renamed_pt_tuple_key, pt_tensor
      elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
            lowerCamelCase__	:       Optional[int]					=						pt_tuple_key[:-1] + ("scale",)
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
            lowerCamelCase__	:       Dict					=						pt_tuple_key[:-1] + ("embedding",)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      lowerCamelCase__	:       Optional[int]					=						pt_tuple_key[:-1] + ("kernel",)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
            lowerCamelCase__	:       List[str]					=						pt_tensor.transpose(2					,      3					,      1					,      0		)
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      lowerCamelCase__	:       str					=						pt_tuple_key[:-1] + ("kernel",)
      if pt_tuple_key[-1] == "weight":
            lowerCamelCase__	:       Optional[int]					=						pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      lowerCamelCase__	:       Optional[Any]					=						pt_tuple_key[:-1] + ("weight",)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      lowerCamelCase__	:       Union[str, Any]					=						pt_tuple_key[:-1] + ("bias",)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor


def    lowercase_	(	_A    :					Any					,      _A    :					str					,      _A    :					str=42		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						{k: v.numpy() for k, v in pt_state_dict.items()}

      # Step 2: Since the model is stateless, get random Flax params
      lowerCamelCase__	:       List[Any]					=						flax_model.init_weights(PRNGKey(_A		)		)

      lowerCamelCase__	:       int					=						flatten_dict(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						{}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            lowerCamelCase__	:       Any					=						rename_key(_A		)
            lowerCamelCase__	:       List[str]					=						tuple(renamed_pt_key.split("."		)		)

            # Correctly rename weight parameters
            lowerCamelCase__	:       List[str]					=						rename_key_and_reshape_tensor(_A					,      _A					,      _A		)

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."		)

        # also add unexpected weight so that warning is thrown
            lowerCamelCase__	:       Optional[Any]					=						jnp.asarray(_A		)

      return unflatten_dict(_A		)






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						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   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


