



'''simple docstring'''





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 CLIPImageProcessor


class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=7					,    _lowerCamelCase=3					,    _lowerCamelCase=18					,    _lowerCamelCase=30					,    _lowerCamelCase=400					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=True					,    _lowerCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]					,    _lowerCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,    _lowerCamelCase=True					,    )    ->    Optional[int]:
    A_  :		Tuple						=					size if size is not None else {"""shortest_edge""": 20}
    A_  :		Dict						=					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
    A_  :		Tuple						=					parent
    A_  :		List[Any]						=					batch_size
    A_  :		List[Any]						=					num_channels
    A_  :		str						=					image_size
    A_  :		Union[str, Any]						=					min_resolution
    A_  :		str						=					max_resolution
    A_  :		Union[str, Any]						=					do_resize
    A_  :		Optional[Any]						=					size
    A_  :		Optional[Any]						=					do_center_crop
    A_  :		str						=					crop_size
    A_  :		Tuple						=					do_normalize
    A_  :		str						=					image_mean
    A_  :		List[str]						=					image_std
    A_  :		Dict						=					do_convert_rgb







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase=False					,    _lowerCamelCase=False					,    _lowerCamelCase=False  )    ->    Optional[int]:
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    if equal_resolution:
       A_  :		Union[str, Any]						=					[]
       for i in range(self.batch_size  ):
          image_inputs.append(
              np.random.randint(
                  255					,    size=(self.num_channels, self.max_resolution, self.max_resolution)					,    dtype=np.uinta  )  )
    else:
       A_  :		int						=					[]
       for i in range(self.batch_size  ):
          A_						,  A_  :		Dict						=					np.random.choice(np.arange(self.min_resolution					,    self.max_resolution  )					,    2  )
          image_inputs.append(np.random.randint(255					,    size=(self.num_channels, width, height)					,    dtype=np.uinta  )  )

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

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

    return image_inputs


@require_torch
@require_vision
class 							_lowerCAmelCase	(  __A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     CLIPImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		int						=					CLIPImageProcessingTester(self  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		Optional[int]						=					self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_resize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_center_crop"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """center_crop"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_normalize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_mean"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_std"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_convert_rgb"""  )  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 20}  )
    self.assertEqual(image_processor.crop_size					,    {"""height""": 18, """width""": 18}  )

    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42					,    crop_size=84  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}  )
    self.assertEqual(image_processor.crop_size					,    {"""height""": 84, """width""": 84}  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    pass







 def       UpperCAmelCase_       (      self  )    ->    int:
    # Initialize image_processing
    A_  :		Optional[Any]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		str						=					self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

    # Test not batched input
    A_  :		Optional[int]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )

    # Test batched
    A_  :		List[Any]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    int:
    # Initialize image_processing
    A_  :		Union[str, Any]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    A_  :		List[Any]						=					self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase					,    numpify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    np.ndarray  )

    # Test not batched input
    A_  :		List[str]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )

    # Test batched
    A_  :		Dict						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    # Initialize image_processing
    A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    A_  :		int						=					self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase					,    torchify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    torch.Tensor  )

    # Test not batched input
    A_  :		int						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )

    # Test batched
    A_  :		List[str]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )


@require_torch
@require_vision
class 							_lowerCAmelCase	(  __A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     CLIPImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Optional[int]						=					CLIPImageProcessingTester(self					,    num_channels=4  )
    A_  :		str						=					3







 @property
 def       UpperCAmelCase_       (      self  )    ->    str:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_resize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_center_crop"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """center_crop"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_normalize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_mean"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_std"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_convert_rgb"""  )  )







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







 def       UpperCAmelCase_       (      self  )    ->    str:
    # Initialize image_processing
    A_  :		Dict						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		str						=					self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

    # Test not batched input
    A_  :		Dict						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            1,
            self.expected_encoded_image_num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )

    # Test batched
    A_  :		Tuple						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.expected_encoded_image_num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )					,    )





'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
