
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import SwinaSRImageProcessor
  from transformers.image_transforms import get_image_size

class 		__magic_name__				( unittest.TestCase					):


  """simple docstring"""





  def __init__(					self	,		a__	,		a__=7	,		a__=3	,		a__=18	,		a__=30	,		a__=4_00	,		a__=True	,		a__=1 / 2_55	,		a__=True	,		a__=8	,		):
    _lowerCamelCase      =						parent
    _lowerCamelCase      =						batch_size
    _lowerCamelCase      =						num_channels
    _lowerCamelCase      =						image_size
    _lowerCamelCase      =						min_resolution
    _lowerCamelCase      =						max_resolution
    _lowerCamelCase      =						do_rescale
    _lowerCamelCase      =						rescale_factor
    _lowerCamelCase      =						do_pad
    _lowerCamelCase      =						pad_size


  def     _UpperCAmelCase  (					self		):
    return {
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_pad": self.do_pad,
        "pad_size": self.pad_size,
    }


  def     _UpperCAmelCase  (					self	,		a__=False	,		a__=False	,		a__=False		):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    if equal_resolution:
      _lowerCamelCase      =						[]
      for i in range(self.batch_size		):
        image_inputs.append(
            np.random.randint(
                2_55	,		size=(self.num_channels, self.max_resolution, self.max_resolution)	,		dtype=np.uinta		)		)
    else:
      _lowerCamelCase      =						[]
      for i in range(self.batch_size		):
        _lowerCamelCase    ,    _lowerCamelCase      =						np.random.choice(np.arange(self.min_resolution	,		self.max_resolution		)	,		2		)
        image_inputs.append(np.random.randint(2_55	,		size=(self.num_channels, width, height)	,		dtype=np.uinta		)		)

    if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      _lowerCamelCase      =						[Image.fromarray(np.moveaxis(a__	,		0	,		-1		)		) for x in image_inputs]

    if torchify:
      _lowerCamelCase      =						[torch.from_numpy(a__		) for x in image_inputs]

    return image_inputs







@require_torch
@require_vision
class 		__magic_name__				( lowercase_						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = SwinaSRImageProcessor if is_vision_available() else None


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						SwinaSRImageProcessingTester(self		)


  @property
  def     _UpperCAmelCase  (					self		):
    return self.image_processor_tester.prepare_image_processor_dict()


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    self.assertTrue(hasattr(a__	,		'''do_rescale'''		)		)
    self.assertTrue(hasattr(a__	,		'''rescale_factor'''		)		)
    self.assertTrue(hasattr(a__	,		'''do_pad'''		)		)
    self.assertTrue(hasattr(a__	,		'''pad_size'''		)		)


  def     _UpperCAmelCase  (					self		):
    pass


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase    ,    _lowerCamelCase      =						get_image_size(a__		)
    _lowerCamelCase      =						self.image_processor_tester.pad_size

    _lowerCamelCase      =						(old_height // size + 1) * size - old_height
    _lowerCamelCase      =						(old_width // size + 1) * size - old_width
    return old_height + pad_height, old_width + pad_width


  def     _UpperCAmelCase  (					self		):
    # Initialize image_processor
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    # create random PIL images
    _lowerCamelCase      =						self.image_processor_tester.prepare_inputs(equal_resolution=a__		)
    for image in image_inputs:
      self.assertIsInstance(a__	,		Image.Image		)

    # Test not batched input
    _lowerCamelCase      =						image_processor(image_inputs[0]	,		return_tensors='''pt'''		).pixel_values
    _lowerCamelCase    ,    _lowerCamelCase      =						self.calculate_expected_size(np.array(image_inputs[0]		)		)
    self.assertEqual(
        encoded_images.shape	,		(
            1,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )	,		)


  def     _UpperCAmelCase  (					self		):
    # Initialize image_processor
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    # create random numpy tensors
    _lowerCamelCase      =						self.image_processor_tester.prepare_inputs(equal_resolution=a__	,		numpify=a__		)
    for image in image_inputs:
      self.assertIsInstance(a__	,		np.ndarray		)

    # Test not batched input
    _lowerCamelCase      =						image_processor(image_inputs[0]	,		return_tensors='''pt'''		).pixel_values
    _lowerCamelCase    ,    _lowerCamelCase      =						self.calculate_expected_size(image_inputs[0]		)
    self.assertEqual(
        encoded_images.shape	,		(
            1,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )	,		)


  def     _UpperCAmelCase  (					self		):
    # Initialize image_processor
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    _lowerCamelCase      =						self.image_processor_tester.prepare_inputs(equal_resolution=a__	,		torchify=a__		)
    for image in image_inputs:
      self.assertIsInstance(a__	,		torch.Tensor		)

    # Test not batched input
    _lowerCamelCase      =						image_processor(image_inputs[0]	,		return_tensors='''pt'''		).pixel_values
    _lowerCamelCase    ,    _lowerCamelCase      =						self.calculate_expected_size(image_inputs[0]		)
    self.assertEqual(
        encoded_images.shape	,		(
            1,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )	,		)






import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    warnings.warn(
        '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use ConditionalDetrImageProcessor instead.'''	,		a__	,		)
    super().__init__(*a__	,		**a__		)




