


'''simple docstring'''




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


_a      :      Dict       				=      logging.get_logger(__name__)

_a      :      List[Any]       				=      {
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     int									=  "opt"
   _SCREAMING_SNAKE_CASE     :     str									=  ["past_key_values"]
   def __init__(	self					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Tuple=5_0272					,      SCREAMING_SNAKE_CASE_					:			Tuple=768					,      SCREAMING_SNAKE_CASE_					:			Dict=12					,      SCREAMING_SNAKE_CASE_					:			Dict=3072					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=2048					,      SCREAMING_SNAKE_CASE_					:			Dict=True					,      SCREAMING_SNAKE_CASE_					:			Dict=False					,      SCREAMING_SNAKE_CASE_					:			Dict=None					,      SCREAMING_SNAKE_CASE_					:			Tuple=0.1					,      SCREAMING_SNAKE_CASE_					:			Dict=0.0					,      SCREAMING_SNAKE_CASE_					:			List[str]=12					,      SCREAMING_SNAKE_CASE_					:			int="relu"					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=0.0					,      SCREAMING_SNAKE_CASE_					:			List[str]=0.0_2					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Tuple=1					,      SCREAMING_SNAKE_CASE_					:			Tuple=2					,      SCREAMING_SNAKE_CASE_					:			Dict=2					,      SCREAMING_SNAKE_CASE_					:			List[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=True					,      **SCREAMING_SNAKE_CASE_					:			List[str]					,      )   ->       int:
        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_					,      bos_token_id=SCREAMING_SNAKE_CASE_					,      eos_token_id=SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_					,      )
        __snake_case    			=	vocab_size
        __snake_case    			=	max_position_embeddings
        __snake_case    			=	num_attention_heads
        __snake_case    			=	word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        __snake_case    			=	ffn_dim
        __snake_case    			=	hidden_size
        __snake_case    			=	num_hidden_layers
        __snake_case    			=	dropout
        __snake_case    			=	attention_dropout
        __snake_case    			=	activation_function
        __snake_case    			=	init_std
        __snake_case    			=	layerdrop
        __snake_case    			=	use_cache
        __snake_case    			=	do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        __snake_case    			=	enable_bias
        __snake_case    			=	layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        __snake_case    			=	_remove_final_layer_norm








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]	):
          for y in range(image.shape[0]	):
               __snake_case    			=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __snake_case    			=	int(summation == 5	)
     return output


# kernel to be applied
_a      :      Tuple       				=      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
       # read original image
       _a      :      Any       				=      np.array(Image.open(R"..\image_data\lena.jpg"))
       # Apply erosion operation to a binary image
       _a      :      List[Any]       				=      erosion(grayabinary(rgbagray(image)), structuring_element)
       # Save the output image
       _a      :      int       				=      Image.fromarray(output).convert("RGB")
       pil_img.save("result_erosion.png")




