





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase	:    Tuple          =      {
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase	:    Union[str, Any]          =      ['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase	:    List[str]          =      [
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			_lowerCamelCase	:    Optional[int]          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

from pathlib import Path

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






def 	a_		(	__lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     int	, __lowercase       :     int			)			->  np.ndarray:
			_snake_case          =      cva.getAffineTransform(__lowercase	, __lowercase			)
			return cva.warpAffine(__lowercase	, __lowercase	, (rows, cols)			)


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

			# set different points to rotate image
			_lowerCamelCase	:    str          =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			_lowerCamelCase	:    Optional[Any]          =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			_lowerCamelCase	:    List[str]          =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			_lowerCamelCase	:    Dict          =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			_lowerCamelCase	:    int          =      [
			    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
			_lowerCamelCase	:    Any          =      plt.figure(1)
			_lowerCamelCase	:    List[Any]          =      ['''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=0.0_5, right=1.0, top=0.9_5)
			plt.show()