"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class  _snake_case     (					__snake_case				, unittest.TestCase		):
						snake_case__			  =			KandinskyVaaPriorPipeline
						snake_case__			  =			["prompt"]
						snake_case__			  =			["prompt", "negative_prompt"]
						snake_case__			  =			[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						snake_case__			  =			False






						@property
						def       lowerCamelCase__     (       self      :			Tuple    ):
													return 32






						@property
						def       lowerCamelCase__     (       self      :			int    ):
													return 32






						@property
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													return self.time_input_dim






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






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													return 100






						@property
						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		List[str]		   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
													return tokenizer






						@property
						def       lowerCamelCase__     (       self      :			Any    ):
													torch.manual_seed(0    )
													__lowerCamelCase					:		str		   =     CLIPTextConfig(
													    bos_token_id=0							,					eos_token_id=2							,					hidden_size=self.text_embedder_hidden_size							,					projection_dim=self.text_embedder_hidden_size							,					intermediate_size=37							,					layer_norm_eps=1E-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=1000							,					)
													return CLIPTextModelWithProjection(_lowercase    )






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													torch.manual_seed(0    )

													__lowerCamelCase					:		Dict		   =     {
													    """num_attention_heads""": 2,
													    """attention_head_dim""": 12,
													    """embedding_dim""": self.text_embedder_hidden_size,
													    """num_layers""": 1,
													}

													__lowerCamelCase					:		Tuple		   =     PriorTransformer(**_lowercase    )
													# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
													__lowerCamelCase					:		int		   =     nn.Parameter(torch.ones(model.clip_std.shape    )    )
													return model






						@property
						def       lowerCamelCase__     (       self      :			List[str]    ):
													torch.manual_seed(0    )
													__lowerCamelCase					:		List[str]		   =     CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size							,					image_size=224							,					projection_dim=self.text_embedder_hidden_size							,					intermediate_size=37							,					num_attention_heads=4							,					num_channels=3							,					num_hidden_layers=5							,					patch_size=14							,					)

													__lowerCamelCase					:		Optional[Any]		   =     CLIPVisionModelWithProjection(_lowercase    )
													return model






						@property
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[Any]		   =     CLIPImageProcessor(
													    crop_size=224							,					do_center_crop=_lowercase							,					do_normalize=_lowercase							,					do_resize=_lowercase							,					image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,					image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,					resample=3							,					size=224							,					)

													return image_processor






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.dummy_prior
													__lowerCamelCase					:		int		   =     self.dummy_image_encoder
													__lowerCamelCase					:		Any		   =     self.dummy_text_encoder
													__lowerCamelCase					:		int		   =     self.dummy_tokenizer
													__lowerCamelCase					:		Optional[Any]		   =     self.dummy_image_processor

													__lowerCamelCase					:		List[Any]		   =     UnCLIPScheduler(
													    variance_type="fixed_small_log"							,					prediction_type="sample"							,					num_train_timesteps=1000							,					clip_sample=_lowercase							,					clip_sample_range=1_0.0							,					)

													__lowerCamelCase					:		List[Any]		   =     {
													    """prior""": prior,
													    """image_encoder""": image_encoder,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """scheduler""": scheduler,
													    """image_processor""": image_processor,
													}

													return components






						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[int]=0    ):

													if str(_lowercase    ).startswith("mps"    ):
																				__lowerCamelCase					:		Dict		   =     torch.manual_seed(_lowercase    )
													else:
																				__lowerCamelCase					:		Union[str, Any]		   =     torch.Generator(device=_lowercase    ).manual_seed(_lowercase    )
													__lowerCamelCase					:		Union[str, Any]		   =     {
													    """prompt""": """horse""",
													    """generator""": generator,
													    """guidance_scale""": 4.0,
													    """num_inference_steps""": 2,
													    """output_type""": """np""",
													}
													return inputs






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		Union[str, Any]		   =     """cpu"""

													__lowerCamelCase					:		Union[str, Any]		   =     self.get_dummy_components()

													__lowerCamelCase					:		Dict		   =     self.pipeline_class(**_lowercase    )
													__lowerCamelCase					:		Tuple		   =     pipe.to(_lowercase    )

													pipe.set_progress_bar_config(disable=_lowercase    )

													__lowerCamelCase					:		Optional[int]		   =     pipe(**self.get_dummy_inputs(_lowercase    )    )
													__lowerCamelCase					:		str		   =     output.image_embeds

													__lowerCamelCase					:		Any		   =     pipe(
													    **self.get_dummy_inputs(_lowercase    )							,					return_dict=_lowercase							,					)[0]

													__lowerCamelCase					:		List[Any]		   =     image[0, -10:]
													__lowerCamelCase					:		List[Any]		   =     image_from_tuple[0, -10:]

													assert image.shape == (1, 32)

													__lowerCamelCase					:		Optional[Any]		   =     np.array(
													    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]    )

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






						@skip_mps
						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Any		   =     torch_device == """cpu"""
													__lowerCamelCase					:		Any		   =     True
													__lowerCamelCase					:		Any		   =     False

													self._test_inference_batch_single_identical(
													    test_max_difference=_lowercase							,					relax_max_difference=_lowercase							,					test_mean_pixel_difference=_lowercase							,					)







						@skip_mps
						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     torch_device == """cpu"""
													__lowerCamelCase					:		Union[str, Any]		   =     False

													self._test_attention_slicing_forward_pass(
													    test_max_difference=_lowercase							,					test_mean_pixel_difference=_lowercase							,					)
"""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 LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     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       lowerCamelCase__     (       self      :			Dict    ):
													pass






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

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     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
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					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       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

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

													# Test not batched input
													__lowerCamelCase					:		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
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					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"],
													    )							,					)