





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class        __lowerCAmelCase (					unittest.TestCase):







      @property
      def      _lowercase						(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        a__			:      List[str]            =1
        a__			:      Union[str, Any]            =3
        a__			:      Dict            =8
        a__			:      Optional[Any]            =8

        a__			:      Any            =torch.rand((batch_size, num_channels, height, width)							)

        return sample







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[str]:



        '''simple docstring'''




        if isinstance(image[0]	,   PIL.Image.Image							):
          return np.stack([np.array(lowerCAmelCase__							) for i in image]	,   axis=0							)
        elif isinstance(lowerCAmelCase__	,   torch.Tensor							):
          return image.cpu().numpy().transpose(0	,   2	,   3	,   1							)
        return image







      def      _lowercase						(					self							)     ->		Union[str, Any]:



        '''simple docstring'''




        a__			:      int            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)

        a__			:      Optional[int]            =self.dummy_sample
        a__			:      str            =self.to_np(lowerCAmelCase__							)

        for output_type in ["pt", "np", "pil"]:
          a__			:      Optional[int]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__	,   )
          a__			:      Dict            =self.to_np(lowerCAmelCase__							)
          a__			:      int            =(input_np * 2_5_5).round() if output_type == "pil" else input_np
          assert (
              np.abs(in_np - out_np							).max() < 1E-6
          ), F'''decoded output does not match input for output_type {output_type}'''







      def      _lowercase						(					self							)     ->		List[str]:



        '''simple docstring'''




        a__			:      int            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
        a__			:      Optional[int]            =self.dummy_sample.cpu().numpy().transpose(0	,   2	,   3	,   1							)

        for output_type in ["pt", "np", "pil"]:
          a__			:      List[Any]            =image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__							)

          a__			:      Union[str, Any]            =self.to_np(lowerCAmelCase__							)
          a__			:      Optional[int]            =(input_np * 2_5_5).round() if output_type == "pil" else input_np
          assert (
              np.abs(in_np - out_np							).max() < 1E-6
          ), F'''decoded output does not match input for output_type {output_type}'''







      def      _lowercase						(					self							)     ->		int:



        '''simple docstring'''




        a__			:      List[Any]            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)

        a__			:      Union[str, Any]            =self.dummy_sample.cpu().numpy().transpose(0	,   2	,   3	,   1							)
        a__			:      List[Any]            =image_processor.numpy_to_pil(lowerCAmelCase__							)

        for output_type in ["pt", "np", "pil"]:
          a__			:      List[str]            =image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__							)
          for i, o in zip(lowerCAmelCase__	,   lowerCAmelCase__							):
            a__			:      List[str]            =np.array(lowerCAmelCase__							)
            a__			:      Optional[int]            =self.to_np(lowerCAmelCase__							) if output_type == "pil" else (self.to_np(lowerCAmelCase__							) * 2_5_5).round()
            assert (
                np.abs(in_np - out_np							).max() < 1E-6
            ), F'''decoded output does not match input for output_type {output_type}'''







      def      _lowercase						(					self							)     ->		List[Any]:



        '''simple docstring'''




        a__			:      Tuple            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)

        a__			:      Optional[int]            =self.dummy_sample
        a__			:      List[str]            =input_pt_ad.squeeze(0							)

        a__			:      Any            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
        a__			:      Optional[int]            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )

        a__			:      Optional[Any]            =self.to_np(self.dummy_sample							)
        a__			:      Tuple            =input_np_ad.squeeze(0							)

        a__			:      Any            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
        a__			:      Optional[int]            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )

        assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
        assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6







      def      _lowercase						(					self							)     ->		int:



        '''simple docstring'''




        a__			:      Dict            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)

        a__			:      Union[str, Any]            =self.dummy_sample
        a__			:      Optional[int]            =list(lowerCAmelCase__							)

        a__			:      List[Any]            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )

        a__			:      Tuple            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )

        a__			:      Tuple            =self.to_np(self.dummy_sample							)
        list(lowerCAmelCase__							)

        a__			:      Optional[Any]            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )

        a__			:      Any            =image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )

        assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
        assert np.abs(out_np_ad - out_np_list							).max() < 1E-6













import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


UpperCAmelCase			:			List[Any]	  = logging.get_logger(__name__)







class        __lowerCAmelCase (					UpperCamelCase__):







      def __init__(					self	,   *lowerCAmelCase__	,   **lowerCAmelCase__							)     ->		None:



        '''simple docstring'''




        warnings.warn(
            "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use DetrImageProcessor instead."	,   lowerCAmelCase__	,   )
        super().__init__(*lowerCAmelCase__	,   **lowerCAmelCase__							)






