

import unittest

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

from diffusers import KandinskyPriorPipeline, 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  a     (		UpperCAmelCase							,       unittest.TestCase ):
						_lowercase			  =	KandinskyPriorPipeline
						_lowercase			  =	["prompt"]
						_lowercase			  =	["prompt", "negative_prompt"]
						_lowercase			  =	[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						_lowercase			  =	False


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return 32


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return 32


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.time_input_dim


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.time_input_dim * 4


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return 100


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										torch.manual_seed(0		)
										_UpperCAmelCase				:						List[Any]       						=			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(A_		)


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										torch.manual_seed(0		)

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

										_UpperCAmelCase				:						Tuple       						=			PriorTransformer(**A_		)
										# 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
										_UpperCAmelCase				:						Optional[Any]       						=			nn.Parameter(torch.ones(model.clip_std.shape		)		)
										return model


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										torch.manual_seed(0		)
										_UpperCAmelCase				:						int       						=			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  ,    )

										_UpperCAmelCase				:						Dict       						=			CLIPVisionModelWithProjection(A_		)
										return model


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			CLIPImageProcessor(
										    crop_size=224  ,    do_center_crop=A_  ,    do_normalize=A_  ,    do_resize=A_  ,    image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,    image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,    resample=3  ,    size=224  ,    )

										return image_processor


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			self.dummy_prior
										_UpperCAmelCase				:						List[Any]       						=			self.dummy_image_encoder
										_UpperCAmelCase				:						str       						=			self.dummy_text_encoder
										_UpperCAmelCase				:						Any       						=			self.dummy_tokenizer
										_UpperCAmelCase				:						int       						=			self.dummy_image_processor

										_UpperCAmelCase				:						Union[str, Any]       						=			UnCLIPScheduler(
										    variance_type="fixed_small_log"  ,    prediction_type="sample"  ,    num_train_timesteps=1000  ,    clip_sample=A_  ,    clip_sample_range=10.0  ,    )

										_UpperCAmelCase				:						Optional[int]       						=			{
										    "prior": prior,
										    "image_encoder": image_encoder,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "scheduler": scheduler,
										    "image_processor": image_processor,
										}

										return components


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_=0		):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			"cpu"

										_UpperCAmelCase				:						int       						=			self.get_dummy_components()

										_UpperCAmelCase				:						str       						=			self.pipeline_class(**A_		)
										_UpperCAmelCase				:						List[str]       						=			pipe.to(A_		)

										pipe.set_progress_bar_config(disable=A_		)

										_UpperCAmelCase				:						Union[str, Any]       						=			pipe(**self.get_dummy_inputs(A_		)		)
										_UpperCAmelCase				:						List[str]       						=			output.image_embeds

										_UpperCAmelCase				:						Tuple       						=			pipe(
										    **self.get_dummy_inputs(A_		)  ,    return_dict=A_  ,    )[0]

										_UpperCAmelCase				:						List[str]       						=			image[0, -10:]
										_UpperCAmelCase				:						Optional[int]       						=			image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										_UpperCAmelCase				:						Union[str, Any]       						=			np.array(
										    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]		)

										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 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			torch_device == "cpu"
										_UpperCAmelCase				:						Tuple       						=			True
										_UpperCAmelCase				:						Dict       						=			False

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





						@skip_mps
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			torch_device == "cpu"
										_UpperCAmelCase				:						Optional[int]       						=			False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=A_  ,    test_mean_pixel_difference=A_  ,    )





def    UpperCamelCase(					lowercase_							)	->				int:





  '''simple docstring'''





  return int(lowercase_							) if x - int(lowercase_							) <= 0 else int(lowercase_							) + 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()