"""simple docstring"""


import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        __UpperCAmelCase						(     __lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Tuple  							=	KandinskyVaaPriorEmbaEmbPipeline
				A__				:       int  							=	['''prompt''', '''image''']
				A__				:       Any  							=	['''prompt''', '''image''']
				A__				:       str  							=	[
				    '''num_images_per_prompt''',
				    '''strength''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				A__				:       List[Any]  							=	False

				@property
				def 		_a     (				self				):
										return 32

				@property
				def 		_a     (				self				):
										return 32

				@property
				def 		_a     (				self				):
										return self.time_input_dim

				@property
				def 		_a     (				self				):
										return self.time_input_dim * 4

				@property
				def 		_a     (				self				):
										return 100

				@property
				def 		_a     (				self				):
										lowerCamelCase__ =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
										return tokenizer

				@property
				def 		_a     (				self				):
										torch.manual_seed(0				)
										lowerCamelCase__ =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(_lowerCamelCase				)

				@property
				def 		_a     (				self				):
										torch.manual_seed(0				)

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

										lowerCamelCase__ =PriorTransformer(**_lowerCamelCase				)
										# 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__ =nn.Parameter(torch.ones(model.clip_std.shape				)				)
										return model

				@property
				def 		_a     (				self				):
										torch.manual_seed(0				)
										lowerCamelCase__ =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__ =CLIPVisionModelWithProjection(_lowerCamelCase				)
										return model

				@property
				def 		_a     (				self				):
										lowerCamelCase__ =CLIPImageProcessor(
										    crop_size=224     ,			do_center_crop=_lowerCamelCase     ,			do_normalize=_lowerCamelCase     ,			do_resize=_lowerCamelCase     ,			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 		_a     (				self				):
										lowerCamelCase__ =self.dummy_prior
										lowerCamelCase__ =self.dummy_image_encoder
										lowerCamelCase__ =self.dummy_text_encoder
										lowerCamelCase__ =self.dummy_tokenizer
										lowerCamelCase__ =self.dummy_image_processor

										lowerCamelCase__ =UnCLIPScheduler(
										    variance_type="fixed_small_log"     ,			prediction_type="sample"     ,			num_train_timesteps=1000     ,			clip_sample=_lowerCamelCase     ,			clip_sample_range=1_0.0     ,			)

										lowerCamelCase__ ={
										    "prior": prior,
										    "image_encoder": image_encoder,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "scheduler": scheduler,
										    "image_processor": image_processor,
										}

										return components

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)
										lowerCamelCase__ =image.cpu().permute(0     ,			2     ,			3     ,			1				)[0]
										lowerCamelCase__ =Image.fromarray(np.uinta(_lowerCamelCase				)				).convert("RGB"				).resize((256, 256)				)

										lowerCamelCase__ ={
										    "prompt": "horse",
										    "image": init_image,
										    "strength": 0.5,
										    "generator": generator,
										    "guidance_scale": 4.0,
										    "num_inference_steps": 2,
										    "output_type": "np",
										}
										return inputs

				def 		_a     (				self				):
										lowerCamelCase__ ="cpu"

										lowerCamelCase__ =self.get_dummy_components()

										lowerCamelCase__ =self.pipeline_class(**_lowerCamelCase				)
										lowerCamelCase__ =pipe.to(_lowerCamelCase				)

										pipe.set_progress_bar_config(disable=_lowerCamelCase				)

										lowerCamelCase__ =pipe(**self.get_dummy_inputs(_lowerCamelCase				)				)
										lowerCamelCase__ =output.image_embeds

										lowerCamelCase__ =pipe(
										    **self.get_dummy_inputs(_lowerCamelCase				)     ,			return_dict=_lowerCamelCase     ,			)[0]

										lowerCamelCase__ =image[0, -10:]
										lowerCamelCase__ =image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										lowerCamelCase__ =np.array(
										    [
										        0.1_0_7_1_2_8_4,
										        1.3_3_3_0_2_7_1,
										        0.6_1_2_6_0_2_2_3,
										        -0.6_6_9_1_0_6_5,
										        -0.3_8_4_6_8_5_2,
										        -1.0_3_0_3_6_6_1,
										        0.2_2_7_1_6_1_1_1,
										        0.0_3_3_4_8_9_0_1,
										        0.3_0_0_4_0_6_7_5,
										        -0.2_4_8_0_5_0_2_9,
										    ]				)

										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 		_a     (				self				):
										lowerCamelCase__ =torch_device == "cpu"
										lowerCamelCase__ =True
										lowerCamelCase__ =False

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






				@skip_mps
				def 		_a     (				self				):
										lowerCamelCase__ =torch_device == "cpu"
										lowerCamelCase__ =False

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



"""simple docstring"""


from __future__ import annotations

from collections.abc import Generator





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Generator[int, None, None]:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ) or n < 1:
												raise Exception("Sequence only defined for positive integers"    )

						yield n
						while n != 1:
												if n % 2 == 0:
																		n //= 2
												else:
																		lowerCamelCase__ =3 * n + 1
												yield n





def       lowerCamelCase_			( )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =43
						lowerCamelCase__ =tuple(collatz_sequence(__lowerCAmelCase    )    )
						print(__lowerCAmelCase    )
						print(F'''Collatz sequence from {n} took {len(__lowerCAmelCase    )} steps.'''    )


if __name__ == "__main__":
	main()


