
import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class 	__A(				tf.keras.layers.Layer ):

  """simple docstring"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None						):
       super().__init__()
       UpperCamelCase__				=				pad_token_id
       UpperCamelCase__				=				max_length
       UpperCamelCase__				=				vocab
       UpperCamelCase__				=				merges
       UpperCamelCase__				=				BytePairTokenizer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			sequence_length=_UpperCAmelCase						)







  @classmethod
  def        UpperCAmelCase_					(cls       ,			SCREAMING_SNAKE_CASE_       ,			*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				[' '.join(_UpperCAmelCase						) for m in tokenizer.bpe_ranks.keys()]
       UpperCamelCase__				=				tokenizer.get_vocab()
       return cls(_UpperCAmelCase       ,			_UpperCAmelCase       ,			*_UpperCAmelCase       ,			**_UpperCAmelCase						)







  @classmethod
  def        UpperCAmelCase_					(cls       ,			SCREAMING_SNAKE_CASE_       ,			*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				GPTaTokenizer.from_pretrained(_UpperCAmelCase       ,			*_UpperCAmelCase       ,			**_UpperCAmelCase						)
       return cls.from_tokenizer(_UpperCAmelCase       ,			*_UpperCAmelCase       ,			**_UpperCAmelCase						)







  @classmethod
  def        UpperCAmelCase_					(cls       ,			SCREAMING_SNAKE_CASE_						):
       return cls(**_UpperCAmelCase						)







  def        UpperCAmelCase_					(self						):
       return {
           "vocab": self.vocab,
           "merges": self.merges,
           "max_length": self.max_length,
           "pad_token_id": self.pad_token_id,
       }







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=				self.tf_tokenizer(_UpperCAmelCase						)
       UpperCamelCase__				=				tf.ones_like(_UpperCAmelCase						)

       if self.pad_token_id is not None:
            # pad the tokens up to max length
            UpperCamelCase__				=				max_length if max_length is not None else self.max_length

            if max_length is not None:
                 UpperCamelCase__				=				pad_model_inputs(
                     _UpperCAmelCase       ,			max_seq_length=_UpperCAmelCase       ,			pad_value=self.pad_token_id						)

       return {"attention_mask": attention_mask, "input_ids": input_ids}












'''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        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[int]			)     ->     List[str]:
     __A						: List[str]      =       k_size // 2
     __A ,__A						: List[Any]      =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						: List[Any]      =       1 / (2 * pi * sigma) * exp(-(square(__snake_case			) + square(__snake_case			)) / (2 * square(__snake_case			))			)
     return g






def        _lowerCAmelCase			(  __snake_case  : List[str] ,							__snake_case  : List[str] ,							__snake_case  : int			)     ->     Union[str, Any]:
     __A ,__A						: Tuple      =       image.shape[0], image.shape[1]
     # dst image height and width
     __A						: Tuple      =       height - k_size + 1
     __A						: Optional[Any]      =       width - k_size + 1

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

     #  turn the kernel into shape(k*k, 1)
     __A						: List[Any]      =       gen_gaussian_kernel(__snake_case ,							__snake_case			)
     __A						: Any      =       ravel(__snake_case			)

     # reshape and get the dst image
     __A						: Dict      =       dot(__snake_case ,							__snake_case			).reshape(__snake_case ,							__snake_case			).astype(__snake_case			)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__    :      List[Any]						=    imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__    :      Dict						=    cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__    :      Any						=    gaussian_filter(gray, 3, sigma=1)
  lowercase__    :      str						=    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()