

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					cva.getAffineTransform(_A   ,	_A       )
     return cva.warpAffine(_A   ,	_A   ,	(rows, cols)       )


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

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

       # add all rotated images in a list
       _SCREAMING_SNAKE_CASE  :						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
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    plt.figure(1)
       _SCREAMING_SNAKE_CASE  :						Dict         =    ['''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()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

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


