






'''simple docstring'''

import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class     A    :






	"""simple docstring"""





	def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
							if dst_width < 0 or dst_height < 0:
													raise ValueError("""Destination width/height should be > 0""" )

							UpperCamelCase_				:   Optional[int]					    =							img
							UpperCamelCase_				:   Optional[Any]					    =							img.shape[1]
							UpperCamelCase_				:   Tuple					    =							img.shape[0]
							UpperCamelCase_				:   Dict					    =							dst_width
							UpperCamelCase_				:   Tuple					    =							dst_height

							UpperCamelCase_				:   Union[str, Any]					    =							self.src_w / self.dst_w
							UpperCamelCase_				:   str					    =							self.src_h / self.dst_h

							UpperCamelCase_				:   Optional[Any]					    =							(
							    np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta ) * 2_55
							)
	def   _UpperCAmelCase       (							self ):
							for i in range(self.dst_h ):
													for j in range(self.dst_w ):
																			UpperCamelCase_				:   Union[str, Any]					    =							self.img[self.get_y(__lowerCAmelCase )][self.get_x(__lowerCAmelCase )]
	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
							return int(self.ratio_x * x )





	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
							return int(self.ratio_y * y )


if __name__ == "__main__":
						UpperCamelCase				,		UpperCamelCase							=800, 600
						UpperCamelCase							=imread("image_data/lena.jpg", 1)
						UpperCamelCase							=NearestNeighbour(im, dst_w, dst_h)
						n.process()

						imshow(
						    f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
						)
						waitKey(0)
						destroyAllWindows()














'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase							={
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase							=[
          "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecAudioForAudioFrameClassification",
          "Data2VecAudioForCTC",
          "Data2VecAudioForSequenceClassification",
          "Data2VecAudioForXVector",
          "Data2VecAudioModel",
          "Data2VecAudioPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecTextForCausalLM",
          "Data2VecTextForMaskedLM",
          "Data2VecTextForMultipleChoice",
          "Data2VecTextForQuestionAnswering",
          "Data2VecTextForSequenceClassification",
          "Data2VecTextForTokenClassification",
          "Data2VecTextModel",
          "Data2VecTextPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecVisionForImageClassification",
          "Data2VecVisionForMaskedImageModeling",
          "Data2VecVisionForSemanticSegmentation",
          "Data2VecVisionModel",
          "Data2VecVisionPreTrainedModel",
      ]

if is_tf_available():
      UpperCamelCase							=[
          "TFData2VecVisionForImageClassification",
          "TFData2VecVisionForSemanticSegmentation",
          "TFData2VecVisionModel",
          "TFData2VecVisionPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






