
'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class 	A							:



			def __init__(    self				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				)	->						str:




									'''simple docstring'''



									if dst_width < 0 or dst_height < 0:
															raise ValueError("""Destination width/height should be > 0"""				)

									lowercase__							       =				img
									lowercase__							       =				img.shape[1]
									lowercase__							       =				img.shape[0]
									lowercase__							       =				dst_width
									lowercase__							       =				dst_height

									lowercase__							       =				self.src_w / self.dst_w
									lowercase__							       =				self.src_h / self.dst_h

									lowercase__							       =				(
									    np.ones((self.dst_h, self.dst_w, 3)				,   np.uinta				) * 255
									)



			def A__       (    self				)	->						Union[str, Any]:




									'''simple docstring'''



									for i in range(self.dst_h				):
															for j in range(self.dst_w				):
																					lowercase__							       =				self.img[self.get_y(__A				)][self.get_x(__A				)]



			def A__       (    self				,   lowerCamelCase__				)	->						Tuple:




									'''simple docstring'''



									return int(self.ratio_x * x				)



			def A__       (    self				,   lowerCamelCase__				)	->						int:




									'''simple docstring'''



									return int(self.ratio_y * y				)


if __name__ == "__main__":
				__A			,				__A								=			800, 600
				__A								=			imread("image_data/lena.jpg", 1)
				__A								=			NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
				)
				waitKey(0)
				destroyAllWindows()










import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		BioGptTokenizer
   __A :      Optional[int]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase :					Union[str, Any]    =				[
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "w</w>",
         "r</w>",
         "t</w>",
         "lo",
         "low",
         "er</w>",
         "low</w>",
         "lowest</w>",
         "newer</w>",
         "wider</w>",
         "<unk>",
     ]
     lowerCamelCase :					str    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					Dict    =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

     lowerCamelCase :					Dict    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w" ) as fp:
       fp.write(json.dumps(__A ) )
     with open(self.merges_file						,				"w" ) as fp:
       fp.write("\n".join(__A ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"lower newer"
     lowerCamelCase :					Union[str, Any]    =				"lower newer"
     return input_text, output_text






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer(self.vocab_file						,				self.merges_file )

     lowerCamelCase :					Optional[int]    =				"lower"
     lowerCamelCase :					Any    =				["low", "er</w>"]
     lowerCamelCase :					List[str]    =				tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokens + ["<unk>"]
     lowerCamelCase :					List[str]    =				[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )						,				__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer.from_pretrained("microsoft/biogpt" )

     lowerCamelCase :					Optional[int]    =				tokenizer.encode("sequence builders"						,				add_special_tokens=__A )
     lowerCamelCase :					Tuple    =				tokenizer.encode("multi-sequence build"						,				add_special_tokens=__A )

     lowerCamelCase :					Tuple    =				tokenizer.build_inputs_with_special_tokens(__A )
     lowerCamelCase :					List[str]    =				tokenizer.build_inputs_with_special_tokens(__A						,				__A )

     self.assertTrue(encoded_sentence == [2] + text )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a )



