



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							UpperCamelCase_      			=  None


UpperCamelCase_      			=  logging.get_logger(__name__)

UpperCamelCase_      			=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase_      			=  {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

UpperCamelCase_      			=  {
    "facebook/xglm-564M": 2_048,
}







class 				a_    (						_a							):
						UpperCamelCase__ :		List[str]										=VOCAB_FILES_NAMES
						UpperCamelCase__ :		str										=PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__ :		List[Any]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__ :		Tuple										=["""input_ids""", """attention_mask"""]
						UpperCamelCase__ :		Any										=XGLMTokenizer

						def __init__(       self      :int  ,		_lowercase      :List[Any]=None  ,		_lowercase      :Dict=None  ,		_lowercase      :Optional[int]="<s>"  ,		_lowercase      :List[str]="</s>"  ,		_lowercase      :Optional[Any]="</s>"  ,		_lowercase      :Optional[Any]="<s>"  ,		_lowercase      :Any="<unk>"  ,		_lowercase      :Optional[int]="<pad>"  ,		**_lowercase      :List[Any]  ,		)						->   Optional[int]:
												# Compatibility with the original tokenizer
												UpperCAmelCase_					=	7
												UpperCAmelCase_					=	[f"<madeupword{i}>" for i in range(self.num_madeup_words)]

												UpperCAmelCase_					=	kwargs.get('''additional_special_tokens'''  ,		[])
												kwargs["additional_special_tokens"] += [
												    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
												]

												super().__init__(
												    lowerCAmelCase__  ,		tokenizer_file=lowerCAmelCase__  ,		bos_token=lowerCAmelCase__  ,		eos_token=lowerCAmelCase__  ,		sep_token=lowerCAmelCase__  ,		cls_token=lowerCAmelCase__  ,		unk_token=lowerCAmelCase__  ,		pad_token=lowerCAmelCase__  ,		**lowerCAmelCase__  ,		)

												UpperCAmelCase_					=	vocab_file
												UpperCAmelCase_					=	False if not self.vocab_file else True

						def       __a			(       self      :Any  ,		_lowercase      :int  ,		_lowercase      :Optional[Any] = None)						->   str:

												if token_ids_a is None:
																		return [self.sep_token_id] + token_ids_a
												UpperCAmelCase_					=	[self.sep_token_id]
												return sep + token_ids_a + sep + sep + token_ids_a

						def       __a			(       self      :Dict  ,		_lowercase      :Tuple  ,		_lowercase      :Dict = None)						->   List[str]:
												UpperCAmelCase_					=	[self.sep_token_id]

												if token_ids_a is None:
																		return len(sep + token_ids_a) * [0]
												return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]




						def       __a			(       self      :Optional[Any]  ,		_lowercase      :Dict  ,		_lowercase      :Any = None)						->   Dict:
												if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.''')

												if not os.path.isdir(lowerCAmelCase__):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
																		return
												UpperCAmelCase_					=	os.path.join(
												    lowerCAmelCase__  ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__):
																		copyfile(self.vocab_file  ,		lowerCAmelCase__)

												return (out_vocab_file,)






import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

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


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

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