




from pathlib import Path

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       np.ndarray	,							lowercase	:       np.ndarray	,							lowercase	:       np.ndarray	,							lowercase	:       int	,							lowercase	:       int     ):


      '''simple docstring'''



      lowerCamelCase_		     =    cva.getAffineTransform(lowercase	,							lowercase     )
      return cva.warpAffine(lowercase	,							lowercase	,							(rows, cols)     )


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

 # set different points to rotate image
 lowerCamelCase				:		int     =   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
 lowerCamelCase				:		int     =   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
 lowerCamelCase				:		Dict     =   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
 lowerCamelCase				:		Tuple     =   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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Optional[Any]     =   {
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   ["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:		Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



