


"""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_(       a__							,					unittest.TestCase			):
     __UpperCamelCase  =					KandinskyVaaPriorPipeline
     __UpperCamelCase  =					['''prompt''']
     __UpperCamelCase  =					['''prompt''', '''negative_prompt''']
     __UpperCamelCase  =					[
         '''num_images_per_prompt''',
         '''generator''',
         '''num_inference_steps''',
         '''latents''',
         '''negative_prompt''',
         '''guidance_scale''',
         '''output_type''',
         '''return_dict''',
     ]
     __UpperCamelCase  =					False






     @property
     def      lowerCamelCase__     (	self  :					List[Any]				):
           return 3_2






     @property
     def      lowerCamelCase__     (	self  :					str				):
           return 3_2






     @property
     def      lowerCamelCase__     (	self  :					Tuple				):
           return self.time_input_dim






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






     @property
     def      lowerCamelCase__     (	self  :					int				):
           return 1_0_0






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






     @property
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           torch.manual_seed(0				)
           lowerCAmelCase					:	Tuple       =   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=3_7					,				layer_norm_eps=1E-05					,				num_attention_heads=4					,				num_hidden_layers=5					,				pad_token_id=1					,				vocab_size=1_0_0_0					,				)
           return CLIPTextModelWithProjection(UpperCamelCase_				)






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

           lowerCAmelCase					:	Any       =   {
               '''num_attention_heads''': 2,
               '''attention_head_dim''': 1_2,
               '''embedding_dim''': self.text_embedder_hidden_size,
               '''num_layers''': 1,
           }

           lowerCAmelCase					:	List[Any]       =   PriorTransformer(**UpperCamelCase_				)
           # 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  :					Union[str, Any]				):
           torch.manual_seed(0				)
           lowerCAmelCase					:	int       =   CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size					,				image_size=2_2_4					,				projection_dim=self.text_embedder_hidden_size					,				intermediate_size=3_7					,				num_attention_heads=4					,				num_channels=3					,				num_hidden_layers=5					,				patch_size=1_4					,				)

           lowerCAmelCase					:	Union[str, Any]       =   CLIPVisionModelWithProjection(UpperCamelCase_				)
           return model






     @property
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	Dict       =   CLIPImageProcessor(
               crop_size=2_2_4					,				do_center_crop=UpperCamelCase_					,				do_normalize=UpperCamelCase_					,				do_resize=UpperCamelCase_					,				image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,				image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,				resample=3					,				size=2_2_4					,				)

           return image_processor






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	Optional[int]       =   self.dummy_prior
           lowerCAmelCase					:	List[str]       =   self.dummy_image_encoder
           lowerCAmelCase					:	Tuple       =   self.dummy_text_encoder
           lowerCAmelCase					:	List[Any]       =   self.dummy_tokenizer
           lowerCAmelCase					:	List[str]       =   self.dummy_image_processor

           lowerCAmelCase					:	Optional[int]       =   UnCLIPScheduler(
               variance_type='''fixed_small_log'''					,				prediction_type='''sample'''					,				num_train_timesteps=1_0_0_0					,				clip_sample=UpperCamelCase_					,				clip_sample_range=10.0					,				)

           lowerCAmelCase					:	int       =   {
               '''prior''': prior,
               '''image_encoder''': image_encoder,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''scheduler''': scheduler,
               '''image_processor''': image_processor,
           }

           return components






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Tuple=0				):
           if str(UpperCamelCase_				).startswith('''mps'''				):
                 lowerCAmelCase					:	Dict       =   torch.manual_seed(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	List[Any]       =   torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
           lowerCAmelCase					:	str       =   {
               '''prompt''': '''horse''',
               '''generator''': generator,
               '''guidance_scale''': 4.0,
               '''num_inference_steps''': 2,
               '''output_type''': '''np''',
           }
           return inputs






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Optional[Any]       =   '''cpu'''

           lowerCAmelCase					:	List[str]       =   self.get_dummy_components()

           lowerCAmelCase					:	List[Any]       =   self.pipeline_class(**UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   pipe.to(UpperCamelCase_				)

           pipe.set_progress_bar_config(disable=UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   pipe(**self.get_dummy_inputs(UpperCamelCase_				)				)
           lowerCAmelCase					:	Tuple       =   output.image_embeds

           lowerCAmelCase					:	Optional[int]       =   pipe(
               **self.get_dummy_inputs(UpperCamelCase_				)					,				return_dict=UpperCamelCase_					,				)[0]

           lowerCAmelCase					:	Dict       =   image[0, -1_0:]
           lowerCAmelCase					:	str       =   image_from_tuple[0, -1_0:]

           assert image.shape == (1, 3_2)

           lowerCAmelCase					:	List[str]       =   np.array(
               [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]				)

           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  :					int				):
           lowerCAmelCase					:	Optional[Any]       =   torch_device == '''cpu'''
           lowerCAmelCase					:	Tuple       =   True
           lowerCAmelCase					:	Union[str, Any]       =   False

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






     @skip_mps
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	List[Any]       =   torch_device == '''cpu'''
           lowerCAmelCase					:	Tuple       =   False

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






"""simple docstring"""





# using dfs for finding eulerian path traversal


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			str ,   _snake_case				:			List[Any]=None			):
      lowerCAmelCase					:	Any       =   (path or []) + [u]
      for v in graph[u]:
            if visited_edge[u][v] is False:
                  lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   True, True
                  lowerCAmelCase					:	int       =   dfs(_snake_case ,   _snake_case ,   _snake_case ,   _snake_case			)
      return path


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	Tuple       =   0
      lowerCAmelCase					:	Optional[Any]       =   -1
      for i in range(_snake_case			):
            if i not in graph.keys():
                  continue
            if len(graph[i]			) % 2 == 1:
                  odd_degree_nodes += 1
                  lowerCAmelCase					:	Optional[Any]       =   i
      if odd_degree_nodes == 0:
            return 1, odd_node
      if odd_degree_nodes == 2:
            return 2, odd_node
      return 3, odd_node


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			List[Any]			):
      lowerCAmelCase					:	Any       =   [[False for _ in range(max_node + 1			)] for _ in range(max_node + 1			)]
      lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   check_circuit_or_path(_snake_case ,   _snake_case			)
      if check == 3:
            print('''graph is not Eulerian'''			)
            print('''no path'''			)
            return
      lowerCAmelCase					:	Dict       =   1
      if check == 2:
            lowerCAmelCase					:	int       =   odd_node
            print('''graph has a Euler path'''			)
      if check == 1:
            print('''graph has a Euler cycle'''			)
      lowerCAmelCase					:	List[str]       =   dfs(_snake_case ,   _snake_case ,   _snake_case			)
      print(_snake_case			)


def      _snake_case						(						):
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
      lowerCAmelCase					:	Union[str, Any]       =   {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
      lowerCAmelCase					:	List[Any]       =   {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3], 2: [1, 3], 3: [1, 2]}
      lowerCAmelCase					:	Any       =   {
          1: [],
          2: []
          # all degree is zero
      }
      lowerCAmelCase					:	List[str]       =   10
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)


if __name__ == "__main__":
 main()


