

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 						a_			(				lowerCamelCase_					,     unittest.TestCase	):



      """simple docstring"""







      __UpperCAmelCase     						= KandinskyPipeline
      __UpperCAmelCase     						= [
          'prompt',
          'image_embeds',
          'negative_image_embeds',
      ]
      __UpperCAmelCase     						= ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
      __UpperCAmelCase     						= [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      __UpperCAmelCase     						= False
      @property
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           return 32
      @property
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           return 32
      @property
      def 			_lowerCAmelCase	( self	:				str							):
           return self.time_input_dim
      @property
      def 			_lowerCAmelCase	( self	:				str							):
           return self.time_input_dim * 4
      @property
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           return 100
      @property
      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		=XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'							)
           return tokenizer
      @property
      def 			_lowerCAmelCase	( self	:				str							):
           torch.manual_seed(0							)
           SCREAMING_SNAKE_CASE 		=MCLIPConfig(
               numDims=self.cross_attention_dim     ,transformerDimensions=self.text_embedder_hidden_size     ,hidden_size=self.text_embedder_hidden_size     ,intermediate_size=37     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=1005     ,)

           SCREAMING_SNAKE_CASE 		=MultilingualCLIP(snake_case							)
           SCREAMING_SNAKE_CASE 		=text_encoder.eval()

           return text_encoder
      @property
      def 			_lowerCAmelCase	( self	:				int							):
           torch.manual_seed(0							)

           SCREAMING_SNAKE_CASE 		={
               'in_channels': 4,
               # Out channels is double in channels because predicts mean and variance
               'out_channels': 8,
               'addition_embed_type': 'text_image',
               'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
               'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
               'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
               'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
               'layers_per_block': 1,
               'encoder_hid_dim': self.text_embedder_hidden_size,
               'encoder_hid_dim_type': 'text_image_proj',
               'cross_attention_dim': self.cross_attention_dim,
               'attention_head_dim': 4,
               'resnet_time_scale_shift': 'scale_shift',
               'class_embed_type': None,
           }

           SCREAMING_SNAKE_CASE 		=UNetaDConditionModel(**snake_case							)
           return model
      @property
      def 			_lowerCAmelCase	( self	:				int							):
           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }
      @property
      def 			_lowerCAmelCase	( self	:				str							):
           torch.manual_seed(0							)
           SCREAMING_SNAKE_CASE 		=VQModel(**self.dummy_movq_kwargs							)
           return model
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		=self.dummy_text_encoder
           SCREAMING_SNAKE_CASE 		=self.dummy_tokenizer
           SCREAMING_SNAKE_CASE 		=self.dummy_unet
           SCREAMING_SNAKE_CASE 		=self.dummy_movq

           SCREAMING_SNAKE_CASE 		=DDIMScheduler(
               num_train_timesteps=1000     ,beta_schedule='linear'     ,beta_start=0.00_085     ,beta_end=0.012     ,clip_sample=snake_case     ,set_alpha_to_one=snake_case     ,steps_offset=1     ,prediction_type='epsilon'     ,thresholding=snake_case     ,)

           SCREAMING_SNAKE_CASE 		={
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }
           return components
      def 			_lowerCAmelCase	( self	:				int     ,snake_case	:				Union[str, Any]     ,snake_case	:				List[str]=0							):
           SCREAMING_SNAKE_CASE 		=floats_tensor((1, self.cross_attention_dim)     ,rng=random.Random(snake_case							)							).to(snake_case							)
           SCREAMING_SNAKE_CASE 		=floats_tensor((1, self.cross_attention_dim)     ,rng=random.Random(seed + 1							)							).to(snake_case							)
           if str(snake_case							).startswith('mps'							):
                SCREAMING_SNAKE_CASE 		=torch.manual_seed(snake_case							)
           else:
                SCREAMING_SNAKE_CASE 		=torch.Generator(device=snake_case							).manual_seed(snake_case							)
           SCREAMING_SNAKE_CASE 		={
               'prompt': 'horse',
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'generator': generator,
               'height': 64,
               'width': 64,
               'guidance_scale': 4.0,
               'num_inference_steps': 2,
               'output_type': 'np',
           }
           return inputs


      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		='cpu'

           SCREAMING_SNAKE_CASE 		=self.get_dummy_components()

           SCREAMING_SNAKE_CASE 		=self.pipeline_class(**snake_case							)
           SCREAMING_SNAKE_CASE 		=pipe.to(snake_case							)

           pipe.set_progress_bar_config(disable=snake_case							)

           SCREAMING_SNAKE_CASE 		=pipe(**self.get_dummy_inputs(snake_case							)							)
           SCREAMING_SNAKE_CASE 		=output.images

           SCREAMING_SNAKE_CASE 		=pipe(
               **self.get_dummy_inputs(snake_case							)     ,return_dict=snake_case     ,)[0]

           SCREAMING_SNAKE_CASE 		=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE 		=image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE 		=np.array(
               [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]							)

           assert (
               np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
           ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2
           ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class 						a_			(				unittest.TestCase	):



      """simple docstring"""







      def 			_lowerCAmelCase	( self	:				str							):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 			_lowerCAmelCase	( self	:				Tuple							):
           SCREAMING_SNAKE_CASE 		=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_text2img_cat_fp16.npy'							)

           SCREAMING_SNAKE_CASE 		=KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior'     ,torch_dtype=torch.floataa							)
           pipe_prior.to(snake_case							)

           SCREAMING_SNAKE_CASE 		=KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'     ,torch_dtype=torch.floataa							)
           SCREAMING_SNAKE_CASE 		=pipeline.to(snake_case							)
           pipeline.set_progress_bar_config(disable=snake_case							)

           SCREAMING_SNAKE_CASE 		='red cat, 4k photo'

           SCREAMING_SNAKE_CASE 		=torch.Generator(device='cuda'							).manual_seed(0							)
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=pipe_prior(
               snake_case     ,generator=snake_case     ,num_inference_steps=5     ,negative_prompt=''     ,).to_tuple()

           SCREAMING_SNAKE_CASE 		=torch.Generator(device='cuda'							).manual_seed(0							)
           SCREAMING_SNAKE_CASE 		=pipeline(
               snake_case     ,image_embeds=snake_case     ,negative_image_embeds=snake_case     ,generator=snake_case     ,num_inference_steps=100     ,output_type='np'     ,)

           SCREAMING_SNAKE_CASE 		=output.images[0]

           assert image.shape == (512, 512, 3)

           assert_mean_pixel_difference(snake_case     ,snake_case							)




_lowerCamelCase							=[
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "bh-e12", "cd-e2", "ce-e4",
     "de-e1", "df-e8", "dg-e5", "dh-e10", "ef-e3", "eg-e2", "fg-e6", "gh-e6", "hi-e3"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "cd-e2", "de-e1", "df-e8",
     "ef-e3", "eg-e2", "fg-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "de-e1", "df-e8", "dg-e5", "ef-e3", "eg-e2",
     "eh-e12", "fg-e6", "fh-e10", "gh-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "bh-e12", "cd-e2", "df-e8", "dh-e10"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "cd-e2", "ce-e4", "de-e1", "df-e8",
     "dg-e5", "ef-e3", "eg-e2", "fg-e6"]
]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=set()
     for row in edge_array:
          for item in row:
               distinct_edge.add(item[0]     )
     return list(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=['0'] * len(lowerCAmelCase_     )
     for i, row in enumerate(lowerCAmelCase_     ):
          for item in row:
               if distinct_edge in item[0]:
                    SCREAMING_SNAKE_CASE 		='1'
                    break
     return "".join(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=get_distinct_edge(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		={}

     for item in distinct_edge:
          SCREAMING_SNAKE_CASE 		=get_bitcode(lowerCAmelCase_,       lowerCAmelCase_     )
          # print('bit',bit)
          # bt=''.join(bit)
          SCREAMING_SNAKE_CASE 		=bit.count('1'     )
          SCREAMING_SNAKE_CASE 		=[s, bit]
     # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
     SCREAMING_SNAKE_CASE 		=[
         [k, v[0], v[1]]
         for k, v in sorted(frequency_table.items(),       key=lambda lowerCAmelCase_     : v[1][0],       reverse=lowerCAmelCase_     )
     ]
     return sorted_frequency_table







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		={}
     for _, item in enumerate(lowerCAmelCase_     ):
          nodes.setdefault(item[2],       []     ).append(item[0]     )
     return nodes







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		={}
     for key, value in nodes.items():
          SCREAMING_SNAKE_CASE 		=value
     return cluster







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     return [i * 100 / len(lowerCAmelCase_     ) for i in cluster]







def      snake_case__							(  ):





     """simple docstring"""







     print('\nNodes\n'     )
     for key, value in nodes.items():
          print(lowerCAmelCase_,       lowerCAmelCase_     )
     print('\nSupport\n'     )
     print(lowerCAmelCase_     )
     print('\n Cluster \n'     )
     for key, value in sorted(cluster.items(),       reverse=lowerCAmelCase_     ):
          print(lowerCAmelCase_,       lowerCAmelCase_     )
     print('\n Graph\n'     )
     for key, value in graph.items():
          print(lowerCAmelCase_,       lowerCAmelCase_     )
     print('\n Edge List of Frequent subgraphs \n'     )
     for edge_list in freq_subgraph_edge_list:
          print(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     for i in cluster[ca]:
          SCREAMING_SNAKE_CASE 		=0
          SCREAMING_SNAKE_CASE 		=ca + 1
          while ca < max(cluster.keys()     ):
               for j in cluster[ca]:
                    if int(lowerCAmelCase_,       2     ) & int(lowerCAmelCase_,       2     ) == int(lowerCAmelCase_,       2     ):
                         if tuple(nodes[i]     ) in graph:
                              graph[tuple(nodes[i]     )].append(nodes[j]     )
                         else:
                              SCREAMING_SNAKE_CASE 		=[nodes[j]]
                         count += 1
               if count == 0:
                    SCREAMING_SNAKE_CASE 		=ca + 1
               else:
                    break







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=cluster[max(cluster.keys()     )]
     SCREAMING_SNAKE_CASE 		='Header'
     SCREAMING_SNAKE_CASE 		={}
     for i in x:
          if (["Header"],) in graph:
               graph[(["Header"],)].append(x[i]     )
          else:
               SCREAMING_SNAKE_CASE 		=[x[i]]
     for i in x:
          SCREAMING_SNAKE_CASE 		=[['Header']]
     SCREAMING_SNAKE_CASE 		=1
     while i < max(lowerCAmelCase_     ) - 1:
          create_edge(lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     )
          SCREAMING_SNAKE_CASE 		=i + 1
     return graph







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=(path or []) + [start]
     if start == end:
          paths.append(lowerCAmelCase_     )
     for node in graph[start]:
          if tuple(lowerCAmelCase_     ) not in path:
               my_dfs(lowerCAmelCase_,       tuple(lowerCAmelCase_     ),       lowerCAmelCase_,       lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=int(s / 100 * (len(lowerCAmelCase_     ) - 1)     )
     for i in cluster[k]:
          my_dfs(lowerCAmelCase_,       tuple(cluster[k][i]     ),       (['Header'],)     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[]
     for edges in paths:
          SCREAMING_SNAKE_CASE 		=[]
          for j in range(len(lowerCAmelCase_     ) - 1     ):
               SCREAMING_SNAKE_CASE 		=list(edges[j]     )
               for e in temp:
                    SCREAMING_SNAKE_CASE 		=(e[0], e[1])
                    el.append(lowerCAmelCase_     )
          freq_sub_el.append(lowerCAmelCase_     )
     return freq_sub_el







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     for i in range(len(lowerCAmelCase_     )     ):
          for j in range(len(edge_array[i]     )     ):
               SCREAMING_SNAKE_CASE 		=edge_array[i][j].split('-'     )
               SCREAMING_SNAKE_CASE 		=t


if __name__ == "__main__":
 preprocess(edge_array)
 _lowerCamelCase							=get_frequency_table(edge_array)
 _lowerCamelCase							=get_nodes(frequency_table)
 _lowerCamelCase							=get_cluster(nodes)
 _lowerCamelCase							=get_support(cluster)
 _lowerCamelCase							=construct_graph(cluster, nodes)
 find_freq_subgraph_given_support(60, cluster, graph)
 _lowerCamelCase							=[]
 _lowerCamelCase							=freq_subgraphs_edge_list(paths)
 print_all()

