
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      _SCREAMING_SNAKE_CASE				(		A__,     unittest.TestCase				):
				lowerCamelCase_	  =  KandinskyVaaPriorPipeline
				lowerCamelCase_	  =  ['prompt']
				lowerCamelCase_	  =  ['prompt', 'negative_prompt']
				lowerCamelCase_	  =  [
				    'num_images_per_prompt',
				    'generator',
				    'num_inference_steps',
				    'latents',
				    'negative_prompt',
				    'guidance_scale',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase_	  =  False





				@property
				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								return 32





				@property
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								return 32





				@property
				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								return self.time_input_dim





				@property
				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								return self.time_input_dim * 4





				@property
				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								return 100





				@property
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								A  :	Any				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
								return tokenizer





				@property
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								torch.manual_seed(0				)
								A  :	Optional[int]				=					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			:		int				):


								"""simple docstring"""

								torch.manual_seed(0				)

								A  :	int				=					{
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 12,
								    '''embedding_dim''': self.text_embedder_hidden_size,
								    '''num_layers''': 1,
								}

								A  :	str				=					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
								A  :	int				=					nn.Parameter(torch.ones(model.clip_std.shape				)				)
								return model





				@property
				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								torch.manual_seed(0				)
								A  :	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       ,   )

								A  :	List[Any]				=					CLIPVisionModelWithProjection(a__				)
								return model





				@property
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								A  :	Dict				=					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			:		Any				):


								"""simple docstring"""

								A  :	List[Any]				=					self.dummy_prior
								A  :	Any				=					self.dummy_image_encoder
								A  :	Dict				=					self.dummy_text_encoder
								A  :	int				=					self.dummy_tokenizer
								A  :	Tuple				=					self.dummy_image_processor

								A  :	List[Any]				=					UnCLIPScheduler(
								    variance_type='''fixed_small_log'''       ,   prediction_type='''sample'''       ,   num_train_timesteps=1000       ,   clip_sample=a__       ,   clip_sample_range=10.0       ,   )

								A  :	Dict				=					{
								    '''prior''': prior,
								    '''image_encoder''': image_encoder,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''scheduler''': scheduler,
								    '''image_processor''': image_processor,
								}

								return components





				def 							_UpperCAmelCase    (		self			:		List[str]       ,   snake_case_			:		List[str]       ,   snake_case_			:		Tuple=0				):


								"""simple docstring"""

								if str(a__				).startswith('''mps'''				):
												A  :	List[str]				=					torch.manual_seed(a__				)
								else:
												A  :	List[str]				=					torch.Generator(device=a__				).manual_seed(a__				)
								A  :	str				=					{
								    '''prompt''': '''horse''',
								    '''generator''': generator,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs





				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					'''cpu'''

								A  :	Tuple				=					self.get_dummy_components()

								A  :	List[Any]				=					self.pipeline_class(**a__				)
								A  :	str				=					pipe.to(a__				)

								pipe.set_progress_bar_config(disable=a__				)

								A  :	str				=					pipe(**self.get_dummy_inputs(a__				)				)
								A  :	List[Any]				=					output.image_embeds

								A  :	Dict				=					pipe(
								    **self.get_dummy_inputs(a__				)       ,   return_dict=a__       ,   )[0]

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

								assert image.shape == (1, 32)

								A  :	Dict				=					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			:		Optional[int]				):


								"""simple docstring"""

								A  :	str				=					torch_device == '''cpu'''
								A  :	Tuple				=					True
								A  :	List[str]				=					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			:		List[str]				):


								"""simple docstring"""

								A  :	int				=					torch_device == '''cpu'''
								A  :	List[str]				=					False

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




from __future__ import annotations



def 		lowerCAmelCase__     (       UpperCamelCase_					:							dict			,							UpperCamelCase_					:							str						)->	set[str]:
    A__			,						A__					=       set(UpperCamelCase_						), [start]

    while stack:
        A__					=       stack.pop()
        explored.add(UpperCamelCase_						)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]						):
            if adj not in explored:
                stack.append(UpperCamelCase_						)
    return explored


_lowercase    	=		{
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(depth_first_search(G, "A"))
