"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					List[str]										=						KandinskyVaaControlnetPipeline
      lowerCamelCase__	:					List[str]										=						["image_embeds", "negative_image_embeds", "hint"]
      lowerCamelCase__	:					Tuple										=						["image_embeds", "negative_image_embeds", "hint"]
      lowerCamelCase__	:					List[str]										=						[
          "generator",
          "height",
          "width",
          "latents",
          "guidance_scale",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      lowerCamelCase__	:					Optional[Any]										=						False


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return 3_2


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return 3_2


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return self.time_input_dim


      @property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return self.time_input_dim * 4


      @property
      def 	_UpperCAmelCase (     self							) ->      Any:
             return 1_0_0


      @property
      def 	_UpperCAmelCase (     self							) ->      str:
             torch.manual_seed(0							)

             lowercase__    :	List[str]     					=			{
                 'in_channels': 8,
                 # Out channels is double in channels because predicts mean and variance
                 'out_channels': 8,
                 'addition_embed_type': 'image_hint',
                 'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
                 'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
                 'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
                 'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
                 'layers_per_block': 1,
                 'encoder_hid_dim': self.text_embedder_hidden_size,
                 'encoder_hid_dim_type': 'image_proj',
                 'cross_attention_dim': self.cross_attention_dim,
                 'attention_head_dim': 4,
                 'resnet_time_scale_shift': 'scale_shift',
                 'class_embed_type': None,
             }

             lowercase__    :	List[Any]     					=			UNetaDConditionModel(**a							)
             return model


      @property
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return {
                 "block_out_channels": [3_2, 3_2, 6_4, 6_4],
                 "down_block_types": [
                     "DownEncoderBlock2D",
                     "DownEncoderBlock2D",
                     "DownEncoderBlock2D",
                     "AttnDownEncoderBlock2D",
                 ],
                 "in_channels": 3,
                 "latent_channels": 4,
                 "layers_per_block": 1,
                 "norm_num_groups": 8,
                 "norm_type": "spatial",
                 "num_vq_embeddings": 1_2,
                 "out_channels": 3,
                 "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                 "vq_embed_dim": 4,
             }


      @property
      def 	_UpperCAmelCase (     self							) ->      str:
             torch.manual_seed(0							)
             lowercase__    :	Tuple     					=			VQModel(**self.dummy_movq_kwargs							)
             return model


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Tuple     					=			self.dummy_unet
             lowercase__    :	Tuple     					=			self.dummy_movq

             lowercase__    :	Dict     					=			DDIMScheduler(
                 num_train_timesteps=1_0_0_0	,    beta_schedule='linear'	,    beta_start=0.00_085	,    beta_end=0.012	,    clip_sample=a	,    set_alpha_to_one=a	,    steps_offset=1	,    prediction_type='epsilon'	,    thresholding=a	,    )

             lowercase__    :	Any     					=			{
                 'unet': unet,
                 'scheduler': scheduler,
                 'movq': movq,
             }
             return components


      def 	_UpperCAmelCase (     self	,    a	,    a=0							) ->      List[str]:
             lowercase__    :	Tuple     					=			floats_tensor((1, self.text_embedder_hidden_size)	,    rng=random.Random(a							)							).to(a							)
             lowercase__    :	Any     					=			floats_tensor((1, self.text_embedder_hidden_size)	,    rng=random.Random(seed + 1							)							).to(
                 a							)

             # create hint
             lowercase__    :	Union[str, Any]     					=			floats_tensor((1, 3, 6_4, 6_4)	,    rng=random.Random(a							)							).to(a							)

             if str(a							).startswith('mps'							):
                    lowercase__    :	str     					=			torch.manual_seed(a							)
             else:
                    lowercase__    :	str     					=			torch.Generator(device=a							).manual_seed(a							)
             lowercase__    :	List[Any]     					=			{
                 'image_embeds': image_embeds,
                 'negative_image_embeds': negative_image_embeds,
                 'hint': hint,
                 'generator': generator,
                 'height': 6_4,
                 'width': 6_4,
                 'guidance_scale': 4.0,
                 'num_inference_steps': 2,
                 'output_type': 'np',
             }
             return inputs


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	List[str]     					=			'cpu'

             lowercase__    :	Any     					=			self.get_dummy_components()

             lowercase__    :	int     					=			self.pipeline_class(**a							)
             lowercase__    :	str     					=			pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	Tuple     					=			pipe(**self.get_dummy_inputs(a							)							)
             lowercase__    :	Optional[Any]     					=			output.images

             lowercase__    :	Dict     					=			pipe(
                 **self.get_dummy_inputs(a							)	,    return_dict=a	,    )[0]

             lowercase__    :	Dict     					=			image[0, -3:, -3:, -1]
             lowercase__    :	Any     					=			image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             lowercase__    :	str     					=			np.array(
                 [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]							)

             assert (
                 np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
             ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

             assert (
                 np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2
             ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





@slow
@require_torch_gpu
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'							)

             lowercase__    :	Tuple     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/kandinskyv22/hint_image_cat.png'							)
             lowercase__    :	Optional[int]     					=			torch.from_numpy(np.array(a							)							).float() / 255.0
             lowercase__    :	str     					=			hint.permute(2	,    0	,    1							).unsqueeze(0							)

             lowercase__    :	str     					=			KandinskyVaaPriorPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-2-prior'	,    torch_dtype=torch.floataa							)
             pipe_prior.to(a							)

             lowercase__    :	Optional[Any]     					=			KandinskyVaaControlnetPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-2-controlnet-depth'	,    torch_dtype=torch.floataa							)
             lowercase__    :	str     					=			pipeline.to(a							)
             pipeline.set_progress_bar_config(disable=a							)

             lowercase__    :	Optional[int]     					=			'A robot, 4k photo'

             lowercase__    :	int     					=			torch.Generator(device='cuda'							).manual_seed(0							)
             lowercase__					,   lowercase__    :	Optional[int]     					=			pipe_prior(
                 a	,    generator=a	,    num_inference_steps=5	,    negative_prompt=''	,    ).to_tuple()

             lowercase__    :	Tuple     					=			torch.Generator(device='cuda'							).manual_seed(0							)
             lowercase__    :	Dict     					=			pipeline(
                 image_embeds=a	,    negative_image_embeds=a	,    hint=a	,    generator=a	,    num_inference_steps=1_0_0	,    output_type='np'	,    )

             lowercase__    :	Dict     					=			output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)

             assert_mean_pixel_difference(a	,    a							)


"""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, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


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


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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
             lowercase__    :	List[Any]     					=			image_processing(a	,    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
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	List[Any]     					=			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
             lowercase__    :	Any     					=			image_processing(a	,    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							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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
             lowercase__    :	Tuple     					=			image_processing(a	,    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'],
                 )	,    )

