


"""simple docstring"""







from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				cva.getAffineTransform(lowerCAmelCase_  ,						lowerCAmelCase_   )
					return cva.warpAffine(lowerCAmelCase_  ,						lowerCAmelCase_  ,						(rows, cols)   )


if __name__ == "__main__":
		# read original image
		a__				:							Any						=     cva.imread(
		    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
		)
		# turn image in gray scale value
		a__				:							Optional[Any]						=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
		# get image shape
		a__			,     a__				:							Union[str, Any]						=     gray_img.shape

		# set different points to rotate image
		a__				:							Optional[Any]						=     np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
		a__				:							List[str]						=     np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
		a__				:							Optional[Any]						=     np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
		a__				:							Tuple						=     np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)

		# add all rotated images in a list
		a__				:							Union[str, Any]						=     [
		    gray_img,
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		]

		# plot different image rotations
		a__				:							Any						=     plt.figure(1)
		a__				:							Optional[int]						=     ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
		for i, image in enumerate(images):
				plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
				plt.title(titles[i])
				plt.axis('''off''')
				plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
		plt.show()








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{'vocab_file': 'vocab.txt'}

__UpperCAmelCase								=						{
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

__UpperCAmelCase								=						{
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


__UpperCAmelCase								=						{
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  VOCAB_FILES_NAMES
		_snake_case    :    int       						=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case    :    Dict       						=  PRETRAINED_INIT_CONFIGURATION
		_snake_case    :    List[str]       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case    :    Any       						=  ConvBertTokenizer



		def __init__(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		_UpperCamelCase="[UNK]"				,		_UpperCamelCase="[SEP]"				,		_UpperCamelCase="[PAD]"				,		_UpperCamelCase="[CLS]"				,		_UpperCamelCase="[MASK]"				,		_UpperCamelCase=True				,		_UpperCamelCase=None				,		**_UpperCamelCase				,		)	->							Dict:
							super().__init__(
							    _UpperCamelCase				,		tokenizer_file=_UpperCamelCase				,		do_lower_case=_UpperCamelCase				,		unk_token=_UpperCamelCase				,		sep_token=_UpperCamelCase				,		pad_token=_UpperCamelCase				,		cls_token=_UpperCamelCase				,		mask_token=_UpperCamelCase				,		tokenize_chinese_chars=_UpperCamelCase				,		strip_accents=_UpperCamelCase				,		**_UpperCamelCase				,		)

							UpperCAmelCase_    :      Optional[int]            =						json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
							if (
							    normalizer_state.get('lowercase'				,		_UpperCamelCase   ) != do_lower_case
							    or normalizer_state.get('strip_accents'				,		_UpperCamelCase   ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'				,		_UpperCamelCase   ) != tokenize_chinese_chars
							):
												UpperCAmelCase_    :      Any            =						getattr(_UpperCamelCase				,		normalizer_state.pop('type'   )   )
												UpperCAmelCase_    :      str            =						do_lower_case
												UpperCAmelCase_    :      List[Any]            =						strip_accents
												UpperCAmelCase_    :      str            =						tokenize_chinese_chars
												UpperCAmelCase_    :      Tuple            =						normalizer_class(**_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						do_lower_case



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							List[str]:
							UpperCAmelCase_    :      int            =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

							return output



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							List[int]:
							UpperCAmelCase_    :      Union[str, Any]            =						[self.sep_token_id]
							UpperCAmelCase_    :      int            =						[self.cls_token_id]
							if token_ids_a is None:
												return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							Tuple[str]:
							UpperCAmelCase_    :      Any            =						self._tokenizer.model.save(_UpperCamelCase				,		name=_UpperCamelCase   )
							return tuple(_UpperCamelCase   )






