






"""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 			UpperCamelCase_       (__A				,						unittest.TestCase     ):
	__magic_name__	      =				KandinskyVaaPriorPipeline
	__magic_name__	      =				['''prompt''']
	__magic_name__	      =				['''prompt''', '''negative_prompt''']
	__magic_name__	      =				[
	    '''num_images_per_prompt''',
	    '''generator''',
	    '''num_inference_steps''',
	    '''latents''',
	    '''negative_prompt''',
	    '''guidance_scale''',
	    '''output_type''',
	    '''return_dict''',
	]
	__magic_name__	      =				False





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			str:
				return 32





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Tuple:
				return 32





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





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			List[Any]:
				return self.time_input_dim * 4





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Optional[int]:
				return 100





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Union[str, Any]:
				UpperCAmelCase_     : List[Any]			     =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
				return tokenizer





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			List[str]:
				torch.manual_seed(0						)
				UpperCAmelCase_     : 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=1_000	,	)
				return CLIPTextModelWithProjection(lowerCAmelCase_						)





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

				UpperCAmelCase_     : Optional[Any]			     =					{
				    "num_attention_heads": 2,
				    "attention_head_dim": 12,
				    "embedding_dim": self.text_embedder_hidden_size,
				    "num_layers": 1,
				}

				UpperCAmelCase_     : int			     =					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
				UpperCAmelCase_     : Any			     =					nn.Parameter(torch.ones(model.clip_std.shape						)						)
				return model





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			str:
				torch.manual_seed(0						)
				UpperCAmelCase_     : Optional[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_     : Tuple			     =					CLIPVisionModelWithProjection(lowerCAmelCase_						)
				return model





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			List[Any]:
				UpperCAmelCase_     : Tuple			     =					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      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				UpperCAmelCase_     : Any			     =					self.dummy_prior
				UpperCAmelCase_     : Dict			     =					self.dummy_image_encoder
				UpperCAmelCase_     : List[Any]			     =					self.dummy_text_encoder
				UpperCAmelCase_     : List[str]			     =					self.dummy_tokenizer
				UpperCAmelCase_     : str			     =					self.dummy_image_processor

				UpperCAmelCase_     : List[Any]			     =					UnCLIPScheduler(
				    variance_type="fixed_small_log"	,	prediction_type="sample"	,	num_train_timesteps=1_000	,	clip_sample=lowerCAmelCase_	,	clip_sample_range=1_0.0	,	)

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

				return components





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Tuple=0						)						->			Optional[Any]:
				if str(lowerCAmelCase_						).startswith("mps"						):
							UpperCAmelCase_     : str			     =					torch.manual_seed(lowerCAmelCase_						)
				else:
							UpperCAmelCase_     : List[str]			     =					torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					{
				    "prompt": "horse",
				    "generator": generator,
				    "guidance_scale": 4.0,
				    "num_inference_steps": 2,
				    "output_type": "np",
				}
				return inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Any			     =					"cpu"

				UpperCAmelCase_     : List[Any]			     =					self.get_dummy_components()

				UpperCAmelCase_     : str			     =					self.pipeline_class(**lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					pipe.to(lowerCAmelCase_						)

				pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

				UpperCAmelCase_     : str			     =					pipe(**self.get_dummy_inputs(lowerCAmelCase_						)						)
				UpperCAmelCase_     : List[str]			     =					output.image_embeds

				UpperCAmelCase_     : List[str]			     =					pipe(
				    **self.get_dummy_inputs(lowerCAmelCase_						)	,	return_dict=lowerCAmelCase_	,	)[0]

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

				assert image.shape == (1, 32)

				UpperCAmelCase_     : Dict			     =					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      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			List[Any]:
				UpperCAmelCase_     : Any			     =					torch_device == "cpu"
				UpperCAmelCase_     : Any			     =					True
				UpperCAmelCase_     : Optional[int]			     =					False

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





	@skip_mps
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Any:
				UpperCAmelCase_     : str			     =					torch_device == "cpu"
				UpperCAmelCase_     : Union[str, Any]			     =					False

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











"""simple docstring"""
lowerCamelCase_					=      [
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]

def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[str]			     =					{"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
			UpperCAmelCase_     : Optional[Any]			     =					0
			UpperCAmelCase_     : Tuple			     =					0
			while place < len(A__						):
						if (place + 1 < len(A__						)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					[]
			for arabic, roman in ROMAN:
						((UpperCAmelCase_)     ,	(UpperCAmelCase_))     : str			     =					divmod(A__	,A__						)
						result.append(roman * factor						)
						if number == 0:
									break
			return "".join(A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



