





'''simple docstring'''




from pathlib import Path

import numpy as np
from PIL import Image






def        _lowerCAmelCase			(  __snake_case  : np.ndarray			)     ->     np.ndarray:
     __A ,__A ,__A						: Tuple      =       rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b






def        _lowerCAmelCase			(  __snake_case  : np.ndarray			)     ->     np.ndarray:
     return (gray > 1_27) & (gray <= 2_55)






def        _lowerCAmelCase			(  __snake_case  : np.ndarray ,							__snake_case  : np.ndarray			)     ->     np.ndarray:
     __A						: int      =       np.zeros_like(__snake_case			)
     __A						: Dict      =       np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

     # Copy image to padded image
     __A						: Any      =       image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]			):
          for y in range(image.shape[0]			):
               __A						: Optional[Any]      =       (
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __A						: Union[str, Any]      =       int(summation > 0			)
     return output


if __name__ == "__main__":
  # read original image
  lowercase__    :      Any						=    Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
  lowercase__    :      Tuple						=    np.array(Image.open(lena_path))
  # kernel to be applied
  lowercase__    :      List[Any]						=    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
  lowercase__    :      List[str]						=    dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
  # Save the output image
  lowercase__    :      Optional[int]						=    Image.fromarray(output).convert('''RGB''')
  pil_img.save('''result_dilation.png''')






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :      List[Any]						=    logging.get_logger(__name__)

lowercase__    :      str						=    {}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''llama'''
     lowerCAmelCase					=       ['''past_key_values''']


     def __init__(						self						,	_UpperCAmelCase=3_2000						,	_UpperCAmelCase=4096						,	_UpperCAmelCase=1_1008						,	_UpperCAmelCase=32						,	_UpperCAmelCase=32						,	_UpperCAmelCase=None						,	_UpperCAmelCase="silu"						,	_UpperCAmelCase=2048						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-6						,	_UpperCAmelCase=True						,	_UpperCAmelCase=0						,	_UpperCAmelCase=1						,	_UpperCAmelCase=2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=False						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          __A						: Union[str, Any]      =       vocab_size
          __A						: Union[str, Any]      =       max_position_embeddings
          __A						: Any      =       hidden_size
          __A						: Optional[Any]      =       intermediate_size
          __A						: str      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads

          # for backward compatibility
          if num_key_value_heads is None:
               __A						: List[Any]      =       num_attention_heads

          __A						: int      =       num_key_value_heads
          __A						: List[Any]      =       hidden_act
          __A						: Union[str, Any]      =       initializer_range
          __A						: List[Any]      =       rms_norm_eps
          __A						: Any      =       pretraining_tp
          __A						: Optional[Any]      =       use_cache
          __A						: Dict      =       rope_scaling
          self._rope_scaling_validation()

          super().__init__(
              pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	tie_word_embeddings=_UpperCAmelCase						,	**_UpperCAmelCase						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.rope_scaling is None:
               return

          if not isinstance(self.rope_scaling						,	_UpperCAmelCase) or len(self.rope_scaling) != 2:
               raise ValueError(
                   '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                   F'got {self.rope_scaling}')
          __A						: Optional[Any]      =       self.rope_scaling.get('type'						,	_UpperCAmelCase)
          __A						: Tuple      =       self.rope_scaling.get('factor'						,	_UpperCAmelCase)
          if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
               raise ValueError(
                   F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}')
          if rope_scaling_factor is None or not isinstance(_UpperCAmelCase						,	_UpperCAmelCase) or rope_scaling_factor <= 1.0:
               raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}')