



'''simple docstring'''






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 @property
 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		str     	=					1
        _UpperCAmelCase    :		List[Any]     	=					3
        _UpperCAmelCase    :		Optional[Any]     	=					(32, 32)

        _UpperCAmelCase    :		str     	=					floats_tensor((batch_size, num_channels) + sizes    ,rng=random.Random(0				)				).to(a_				)
        return image







 @property
 def _snake_case					(				self				)						->  Any:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Optional[Any]     	=					UNetaDConditionModel(
            block_out_channels=(32, 64)    ,layers_per_block=2    ,sample_size=32    ,in_channels=4    ,out_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,cross_attention_dim=32    ,)
        return model







 @property
 def _snake_case					(				self				)						->  Dict:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Any     	=					AutoencoderKL(
            block_out_channels=[32, 64]    ,in_channels=3    ,out_channels=3    ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,latent_channels=4    ,)
        return model







 @property
 def _snake_case					(				self				)						->  Dict:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Optional[int]     	=					CLIPTextConfig(
            bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1E-0_5    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,)
        return CLIPTextModel(a_				)







 @property
 def _snake_case					(				self				)						->  List[Any]:
        def extract(*a_    ,**a_				):



               class 				lowercase			:


                """simple docstring"""


                def __init__(				self				)						->  Dict:
                       _UpperCAmelCase    :		Optional[int]     	=					torch.ones([0]				)







                def _snake_case					(				self    ,a_				)						->  str:
                       self.pixel_values.to(a_				)
                       return self







               return Out()

        return extract







 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		Tuple     	=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase    :		Optional[Any]     	=					self.dummy_cond_unet
        _UpperCAmelCase    :		List[str]     	=					DDIMScheduler(
            beta_start=0.0_0085    ,beta_end=0.012    ,beta_schedule="""scaled_linear"""    ,clip_sample=a_    ,set_alpha_to_one=a_    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					self.dummy_vae
        _UpperCAmelCase    :		str     	=					self.dummy_text_encoder
        _UpperCAmelCase    :		Any     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

        # make sure here that pndm scheduler skips prk
        _UpperCAmelCase    :		int     	=					StableDiffusionPipeline(
            unet=a_    ,scheduler=a_    ,vae=a_    ,text_encoder=a_    ,tokenizer=a_    ,safety_checker=a_    ,feature_extractor=self.dummy_extractor    ,)
        _UpperCAmelCase    :		Optional[int]     	=					sd_pipe.to(a_				)
        sd_pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		List[Any]     	=					"""A painting of a squirrel eating a burger"""

        _UpperCAmelCase    :		Any     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		Any     	=					sd_pipe([prompt]    ,generator=a_    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type="""np"""				)
        _UpperCAmelCase    :		Optional[Any]     	=					output.images

        _UpperCAmelCase    :		Optional[Any]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		Optional[int]     	=					sd_pipe(
            [prompt]    ,generator=a_    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type="""np"""    ,return_dict=a_    ,)[0]

        _UpperCAmelCase    :		Union[str, Any]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		List[Any]     	=					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase    :		Dict     	=					np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Tuple     	=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase    :		Optional[Any]     	=					self.dummy_cond_unet
        _UpperCAmelCase    :		int     	=					PNDMScheduler(skip_prk_steps=a_				)
        _UpperCAmelCase    :		Any     	=					self.dummy_vae
        _UpperCAmelCase    :		List[str]     	=					self.dummy_text_encoder
        _UpperCAmelCase    :		List[str]     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

        # make sure here that pndm scheduler skips prk
        _UpperCAmelCase    :		Any     	=					StableDiffusionPipeline(
            unet=a_    ,scheduler=a_    ,vae=a_    ,text_encoder=a_    ,tokenizer=a_    ,safety_checker=a_    ,feature_extractor=self.dummy_extractor    ,)
        _UpperCAmelCase    :		Tuple     	=					sd_pipe.to(a_				)
        sd_pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		int     	=					"""A painting of a squirrel eating a burger"""
        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		int     	=					sd_pipe([prompt]    ,generator=a_    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type="""np"""				)

        _UpperCAmelCase    :		List[str]     	=					output.images

        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		Union[str, Any]     	=					sd_pipe(
            [prompt]    ,generator=a_    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type="""np"""    ,return_dict=a_    ,)[0]

        _UpperCAmelCase    :		List[str]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Optional[Any]     	=					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase    :		Optional[int]     	=					np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Optional[Any]     	=					StableDiffusionPipeline.from_pretrained(
            """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""    ,safety_checker=a_				)
        assert isinstance(a_    ,a_				)
        assert isinstance(pipe.scheduler    ,a_				)
        assert pipe.safety_checker is None

        _UpperCAmelCase    :		Optional[Any]     	=					pipe("""example prompt"""    ,num_inference_steps=2				).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(a_				)
               _UpperCAmelCase    :		Union[str, Any]     	=					StableDiffusionPipeline.from_pretrained(a_				)

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        _UpperCAmelCase    :		Tuple     	=					pipe("""example prompt"""    ,num_inference_steps=2				).images[0]
        assert image is not None







 @unittest.skipIf(torch_device != """cuda"""    ,"""This test requires a GPU"""				)
 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		Any     	=					self.dummy_cond_unet
        _UpperCAmelCase    :		Optional[Any]     	=					PNDMScheduler(skip_prk_steps=a_				)
        _UpperCAmelCase    :		Dict     	=					self.dummy_vae
        _UpperCAmelCase    :		Dict     	=					self.dummy_text_encoder
        _UpperCAmelCase    :		Optional[int]     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

        # put models in fp16
        _UpperCAmelCase    :		List[Any]     	=					unet.half()
        _UpperCAmelCase    :		List[str]     	=					vae.half()
        _UpperCAmelCase    :		Dict     	=					bert.half()

        # make sure here that pndm scheduler skips prk
        _UpperCAmelCase    :		int     	=					StableDiffusionPipeline(
            unet=a_    ,scheduler=a_    ,vae=a_    ,text_encoder=a_    ,tokenizer=a_    ,safety_checker=a_    ,feature_extractor=self.dummy_extractor    ,)
        _UpperCAmelCase    :		Optional[int]     	=					sd_pipe.to(a_				)
        sd_pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		int     	=					"""A painting of a squirrel eating a burger"""
        _UpperCAmelCase    :		str     	=					sd_pipe([prompt]    ,num_inference_steps=2    ,output_type="""np"""				).images

        assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


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







 def _snake_case					(				self				)						->  Union[str, Any]:
        _UpperCAmelCase    :		int     	=					"""cuda"""
        _UpperCAmelCase    :		List[str]     	=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
        _UpperCAmelCase    :		Optional[int]     	=					pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Any     	=					"""a photo of a cat"""
        _UpperCAmelCase    :		int     	=					{
            """editing_prompt""": ["""sunglasses"""],
            """reverse_editing_direction""": [False],
            """edit_warmup_steps""": 10,
            """edit_guidance_scale""": 6,
            """edit_threshold""": 0.95,
            """edit_momentum_scale""": 0.5,
            """edit_mom_beta""": 0.6,
        }

        _UpperCAmelCase    :		Optional[int]     	=					3
        _UpperCAmelCase    :		str     	=					7

        # no sega enabled
        _UpperCAmelCase    :		int     	=					torch.Generator(a_				)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		str     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,)

        _UpperCAmelCase    :		str     	=					output.images
        _UpperCAmelCase    :		Optional[Any]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Tuple     	=					[
            0.3467_3113,
            0.3849_2733,
            0.3759_7352,
            0.3408_6335,
            0.3565_0748,
            0.3557_9205,
            0.338_4763,
            0.3434_0236,
            0.357_3271,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		Optional[Any]     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,**a_    ,)

        _UpperCAmelCase    :		List[str]     	=					output.images
        _UpperCAmelCase    :		Dict     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Optional[int]     	=					[
            0.4188_7826,
            0.3772_8766,
            0.3013_8272,
            0.4141_6335,
            0.4166_4985,
            0.3628_3392,
            0.3619_1246,
            0.4336_4465,
            0.4300_1732,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[Any]     	=					"""cuda"""
        _UpperCAmelCase    :		Union[str, Any]     	=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
        _UpperCAmelCase    :		Optional[Any]     	=					pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		int     	=					"""an image of a crowded boulevard, realistic, 4k"""
        _UpperCAmelCase    :		List[Any]     	=					{
            """editing_prompt""": """crowd, crowded, people""",
            """reverse_editing_direction""": True,
            """edit_warmup_steps""": 10,
            """edit_guidance_scale""": 8.3,
            """edit_threshold""": 0.9,
            """edit_momentum_scale""": 0.5,
            """edit_mom_beta""": 0.6,
        }

        _UpperCAmelCase    :		str     	=					9
        _UpperCAmelCase    :		Optional[Any]     	=					7

        # no sega enabled
        _UpperCAmelCase    :		Tuple     	=					torch.Generator(a_				)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		List[Any]     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,)

        _UpperCAmelCase    :		List[Any]     	=					output.images
        _UpperCAmelCase    :		Any     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		str     	=					[
            0.4349_7998,
            0.9181_4065,
            0.754_0739,
            0.5558_0205,
            0.846_7265,
            0.538_9691,
            0.6257_4506,
            0.5889_7763,
            0.5092_6757,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		Dict     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,**a_    ,)

        _UpperCAmelCase    :		int     	=					output.images
        _UpperCAmelCase    :		List[str]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Optional[int]     	=					[
            0.308_9719,
            0.3050_0144,
            0.2901_6042,
            0.3063_0964,
            0.32_5687,
            0.2941_9225,
            0.290_8091,
            0.2872_3598,
            0.2769_6294,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		List[str]     	=					"""cuda"""
        _UpperCAmelCase    :		Dict     	=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
        _UpperCAmelCase    :		Tuple     	=					pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		List[Any]     	=					"""a castle next to a river"""
        _UpperCAmelCase    :		Tuple     	=					{
            """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
            """reverse_editing_direction""": False,
            """edit_warmup_steps""": [15, 18],
            """edit_guidance_scale""": 6,
            """edit_threshold""": [0.9, 0.8],
            """edit_momentum_scale""": 0.5,
            """edit_mom_beta""": 0.6,
        }

        _UpperCAmelCase    :		List[Any]     	=					48
        _UpperCAmelCase    :		str     	=					7

        # no sega enabled
        _UpperCAmelCase    :		str     	=					torch.Generator(a_				)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		Dict     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,)

        _UpperCAmelCase    :		Optional[Any]     	=					output.images
        _UpperCAmelCase    :		Union[str, Any]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		List[Any]     	=					[
            0.7516_3555,
            0.7603_7145,
            0.6_1785,
            0.918_9673,
            0.862_7701,
            0.8518_9694,
            0.851_2813,
            0.8701_2076,
            0.831_2857,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		Dict     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,**a_    ,)

        _UpperCAmelCase    :		Dict     	=					output.images
        _UpperCAmelCase    :		Optional[int]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		List[str]     	=					[
            0.7355_3365,
            0.753_7271,
            0.7434_1905,
            0.6648_0356,
            0.647_2925,
            0.6303_9416,
            0.6481_2905,
            0.674_9717,
            0.651_7102,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		Union[str, Any]     	=					"""cuda"""
        _UpperCAmelCase    :		List[str]     	=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    ,torch_dtype=torch.floataa				)
        _UpperCAmelCase    :		List[str]     	=					pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Union[str, Any]     	=					"""a photo of a cat"""
        _UpperCAmelCase    :		Tuple     	=					{
            """editing_prompt""": ["""sunglasses"""],
            """reverse_editing_direction""": [False],
            """edit_warmup_steps""": 10,
            """edit_guidance_scale""": 6,
            """edit_threshold""": 0.95,
            """edit_momentum_scale""": 0.5,
            """edit_mom_beta""": 0.6,
        }

        _UpperCAmelCase    :		Any     	=					3
        _UpperCAmelCase    :		Dict     	=					7

        # no sega enabled
        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(a_				)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		Any     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,)

        _UpperCAmelCase    :		str     	=					output.images
        _UpperCAmelCase    :		str     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		List[str]     	=					[
            0.3488_7695,
            0.387_6953,
            0.375,
            0.3442_3828,
            0.358_1543,
            0.3571_7773,
            0.338_3789,
            0.3457_0312,
            0.35_9375,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(a_				)
        _UpperCAmelCase    :		List[str]     	=					pipe(
            [prompt]    ,generator=a_    ,guidance_scale=a_    ,num_inference_steps=50    ,output_type="""np"""    ,width=512    ,height=512    ,**a_    ,)

        _UpperCAmelCase    :		List[str]     	=					output.images
        _UpperCAmelCase    :		Tuple     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Union[str, Any]     	=					[
            0.4228_5156,
            0.3691_4062,
            0.2907_7148,
            0.4204_1016,
            0.4191_8945,
            0.3549_8047,
            0.361_8164,
            0.442_3828,
            0.4311_5234,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2








'''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 BlipImageProcessor



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


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







 def _snake_case					(				self				)						->  List[str]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }







 def _snake_case					(				self    ,a_=False    ,a_=False    ,a_=False				)						->  Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCAmelCase    :		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:
               _UpperCAmelCase    :		Optional[int]     	=					[]
               for i in range(self.batch_size				):
                      _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					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
               _UpperCAmelCase    :		Any     	=					[Image.fromarray(np.moveaxis(a_    ,0    ,-1				)				) for x in image_inputs]

        if torchify:
               _UpperCAmelCase    :		Optional[int]     	=					[torch.from_numpy(a_				) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  List[Any]:
        _UpperCAmelCase    :		Dict     	=					BlipImageProcessingTester(self				)







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







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		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_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







 def _snake_case					(				self				)						->  Any:
        pass







 def _snake_case					(				self				)						->  Union[str, Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		List[str]     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		str     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

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

        # Test batched
        _UpperCAmelCase    :		List[str]     	=					image_processor(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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







 def _snake_case					(				self				)						->  Optional[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		str     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        _UpperCAmelCase    :		str     	=					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
        _UpperCAmelCase    :		Dict     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		int     	=					image_processor(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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







 def _snake_case					(				self				)						->  List[str]:
        # Initialize image_processor
        _UpperCAmelCase    :		Dict     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        _UpperCAmelCase    :		Union[str, Any]     	=					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
        _UpperCAmelCase    :		str     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		Optional[Any]     	=					image_processor(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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







@require_torch
@require_vision
class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					BlipImageProcessingTester(self    ,num_channels=4				)
        _UpperCAmelCase    :		List[Any]     	=					3







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







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(a_    ,"""do_resize"""				)				)
        self.assertTrue(hasattr(a_    ,"""size"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







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







 def _snake_case					(				self				)						->  List[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		Any     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

        # Test not batched input
        _UpperCAmelCase    :		List[Any]     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		Dict     	=					image_processor(a_    ,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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)



