
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
     if dst_width < 0 or dst_height < 0:
         raise ValueError("Destination width/height should be > 0")

     snake_case_ :    Union[str, Any]     			=						img
     snake_case_ :    List[str]     			=						img.shape[1]
     snake_case_ :    List[str]     			=						img.shape[0]
     snake_case_ :    str     			=						dst_width
     snake_case_ :    Optional[int]     			=						dst_height

     snake_case_ :    Optional[Any]     			=						self.src_w / self.dst_w
     snake_case_ :    Any     			=						self.src_h / self.dst_h

     snake_case_ :    List[str]     			=						(
         np.ones((self.dst_h, self.dst_w, 3) ,       np.uinta) * 2_55
     )





 def        snake_case__		(    self):
     for i in range(self.dst_h):
         for j in range(self.dst_w):
             snake_case_ :    List[str]     			=						self.img[self.get_y(snake_case_)][self.get_x(snake_case_)]





 def        snake_case__		(    self ,       lowercase_):
     return int(self.ratio_x * x)





 def        snake_case__		(    self ,       lowercase_):
     return int(self.ratio_y * y)


if __name__ == "__main__":
  a_											=						800, 600
  a_											=						imread("image_data/lena.jpg", 1)
  a_											=						NearestNeighbour(im, dst_w, dst_h)
  n.process()

  imshow(
      f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
  )
  waitKey(0)
  destroyAllWindows()


'''simple docstring'''
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_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    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)
    ):
        snake_case_ :    int     			=						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:
        snake_case_ :    Dict     			=						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:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

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

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

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

    # old PyTorch layer norm bias
    snake_case_ :    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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






    snake_case_ :    int     			=						{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

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

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        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
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




