




from pathlib import Path

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	np.ndarray:



       """simple docstring"""

       A__							=   cva.getAffineTransform(lowercase_			,    lowercase_						)
       return cva.warpAffine(lowercase_			,    lowercase_			,    (rows, cols)						)


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

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

  # add all rotated images in a list
  _lowerCamelCase     :		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
  _lowerCamelCase     :		Dict								=       plt.figure(1)
  _lowerCamelCase     :		str								=       ["""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()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase     :		Tuple								=       {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		int								=       ["""Speech2TextTokenizer"""]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[int]								=       ["""Speech2TextFeatureExtractor"""]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[Any]								=       [
      """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFSpeech2TextForConditionalGeneration""",
      """TFSpeech2TextModel""",
      """TFSpeech2TextPreTrainedModel""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		List[Any]								=       [
      """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2TextForConditionalGeneration""",
      """Speech2TextModel""",
      """Speech2TextPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
  from .processing_speech_to_text import SpeechaTextProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_speech_to_text import SpeechaTextTokenizer

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_speech_to_text import (
        TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSpeechaTextForConditionalGeneration,
        TFSpeechaTextModel,
        TFSpeechaTextPreTrainedModel,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text import (
        SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextForConditionalGeneration,
        SpeechaTextModel,
        SpeechaTextPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Union[str, Any]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


