



'''simple docstring'''






from pathlib import Path

import numpy as np
from PIL import Image


def  snake_case_     (					_lowerCAmelCase  :						np.ndarray  )      ->   np.ndarray:
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Any 							=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b


def  snake_case_     (					_lowerCAmelCase  :						np.ndarray  )      ->   np.ndarray:
							return (gray > 127) & (gray <= 255)


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

							# Copy image to padded image
							UpperCAmelCase :       Dict 							=		image

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


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











import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__A        =    logging.get_logger(__name__)

__A        =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A        =    {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

__A        =    {
    "gpt-neox-20b": 20_48,
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       VOCAB_FILES_NAMES
			snake_case_   		=       PRETRAINED_VOCAB_FILES_MAP
			snake_case_   		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   		=       ['''input_ids''', '''attention_mask''']


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__=False			,						**lowerCamelCase__			,						)    ->      int:



							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__			,						lowerCamelCase__			,						tokenizer_file=lowerCamelCase__			,						unk_token=lowerCamelCase__			,						bos_token=lowerCamelCase__			,						eos_token=lowerCamelCase__			,						add_prefix_space=lowerCamelCase__			,						**lowerCamelCase__			,						)

							__lowerCamelCase		       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get('add_prefix_space'			,						lowerCamelCase__							) != add_prefix_space:
											__lowerCamelCase		       =    getattr(lowerCamelCase__			,						pre_tok_state.pop('type'							)							)
											__lowerCamelCase		       =    add_prefix_space
											__lowerCamelCase		       =    pre_tok_class(**lowerCamelCase__							)

							__lowerCamelCase		       =    add_prefix_space


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      Tuple[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self._tokenizer.model.save(lowerCamelCase__			,						name=lowerCamelCase__							)
							return tuple(lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__							) + [self.eos_token_id]							)

							if len(lowerCamelCase__							) > self.model_max_length:
											__lowerCamelCase		       =    input_ids[-self.model_max_length :]
							return input_ids


