
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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       _snake_case   (	UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     Dict        =			KandinskyImgaImgPipeline
 __lowerCAmelCase  :     Tuple        =			['prompt', 'image_embeds', 'negative_image_embeds', 'image']
 __lowerCAmelCase  :     Optional[Any]        =			[
     'prompt',
     'negative_prompt',
     'image_embeds',
     'negative_image_embeds',
     'image',
 ]
 __lowerCAmelCase  :     str        =			[
     'generator',
     'height',
     'width',
     'strength',
     'guidance_scale',
     'negative_prompt',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 __lowerCAmelCase  :     int        =			False


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 32


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 32


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return self.time_input_dim


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return self.time_input_dim * 4


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 1_00


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
   return tokenizer


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__  :   List[str]    		=      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=10_05					,  )

   lowercase__  :   Optional[int]    		=      MultilingualCLIP(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      text_encoder.eval()

   return text_encoder


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   torch.manual_seed(0)

   lowercase__  :   int    		=      {
       """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,
   }

   lowercase__  :   List[Any]    		=      UNetaDConditionModel(**SCREAMING_SNAKE_CASE_)
   return model


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   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      lowercase__     (					self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__  :   int    		=      VQModel(**self.dummy_movq_kwargs)
   return model


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.dummy_text_encoder
   lowercase__  :   str    		=      self.dummy_tokenizer
   lowercase__  :   Optional[Any]    		=      self.dummy_unet
   lowercase__  :   List[Any]    		=      self.dummy_movq

   lowercase__  :   Dict    		=      {
       """num_train_timesteps""": 10_00,
       """beta_schedule""": """linear""",
       """beta_start""": 0.0_0_0_8_5,
       """beta_end""": 0.0_1_2,
       """clip_sample""": False,
       """set_alpha_to_one""": False,
       """steps_offset""": 0,
       """prediction_type""": """epsilon""",
       """thresholding""": False,
   }

   lowercase__  :   Any    		=      DDIMScheduler(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      {
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }

   return components


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=0):


   '''simple docstring'''




   lowercase__  :   str    		=      floats_tensor((1, self.cross_attention_dim)					,  rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      floats_tensor((1, self.cross_attention_dim)					,  rng=random.Random(seed + 1)).to(SCREAMING_SNAKE_CASE_)
   # create init_image
   lowercase__  :   Union[str, Any]    		=      floats_tensor((1, 3, 64, 64)					,  rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[Any]    		=      image.cpu().permute(0					,  2					,  3					,  1)[0]
   lowercase__  :   Dict    		=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_)).convert("""RGB""").resize((2_56, 2_56))

   if str(SCREAMING_SNAKE_CASE_).startswith("""mps"""):
     lowercase__  :   Optional[Any]    		=      torch.manual_seed(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__  :   List[Any]    		=      torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[Any]    		=      {
       """prompt""": """horse""",
       """image""": init_image,
       """image_embeds""": image_embeds,
       """negative_image_embeds""": negative_image_embeds,
       """generator""": generator,
       """height""": 64,
       """width""": 64,
       """num_inference_steps""": 10,
       """guidance_scale""": 7.0,
       """strength""": 0.2,
       """output_type""": """np""",
   }
   return inputs



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      """cpu"""

   lowercase__  :   Optional[int]    		=      self.get_dummy_components()

   lowercase__  :   int    		=      self.pipeline_class(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      pipe.to(SCREAMING_SNAKE_CASE_)

   pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[Any]    		=      pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_))
   lowercase__  :   List[str]    		=      output.images

   lowercase__  :   Any    		=      pipe(
       **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_)					,  return_dict=SCREAMING_SNAKE_CASE_					,  )[0]

   lowercase__  :   Dict    		=      image[0, -3:, -3:, -1]
   lowercase__  :   Dict    		=      image_from_tuple[0, -3:, -3:, -1]

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

   lowercase__  :   Any    		=      np.array(
       [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3])
   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       _snake_case   (	unittest.TestCase							):


 def      lowercase__     (					self):


   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinsky/kandinsky_img2img_frog.npy""")

   lowercase__  :   Tuple    		=      load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
   lowercase__  :   List[str]    		=      """A red cartoon frog, 4k"""

   lowercase__  :   Union[str, Any]    		=      KandinskyPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-prior"""					,  torch_dtype=torch.floataa)
   pipe_prior.to(SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      KandinskyImgaImgPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1"""					,  torch_dtype=torch.floataa)
   lowercase__  :   Optional[int]    		=      pipeline.to(SCREAMING_SNAKE_CASE_)

   pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Dict    		=      torch.Generator(device="""cpu""").manual_seed(0)
   lowercase__  :   List[Any]    		=      pipe_prior(
       SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  num_inference_steps=5					,  negative_prompt=""""""					,  ).to_tuple()

   lowercase__  :   List[str]    		=      pipeline(
       SCREAMING_SNAKE_CASE_					,  image=SCREAMING_SNAKE_CASE_					,  image_embeds=SCREAMING_SNAKE_CASE_					,  negative_image_embeds=SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  num_inference_steps=1_00					,  height=7_68					,  width=7_68					,  strength=0.2					,  output_type="""np"""					,  )

   lowercase__  :   Any    		=      output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)








lowerCamelCase__    :		str          =   [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   Any    		=      [False] * len(lowercase_   )
  lowercase__  :   List[Any]    		=      [s]
  lowercase__  :   int    		=      True

  while queue:
    lowercase__  :   Dict    		=      queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase_   )
        lowercase__  :   Optional[Any]    		=      True
        lowercase__  :   int    		=      u

  return visited[t]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      [-1] * (len(lowercase_   ))
  lowercase__  :   Optional[Any]    		=      0
  lowercase__  :   List[Any]    		=      []
  lowercase__  :   Optional[Any]    		=      [i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase_ , lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   Optional[int]    		=      float("""Inf"""   )
    lowercase__  :   Tuple    		=      sink

    while s != source:
      # Find the minimum value in select path
      lowercase__  :   List[str]    		=      min(lowercase_ , graph[parent[s]][s]   )
      lowercase__  :   List[str]    		=      parent[s]

    max_flow += path_flow
    lowercase__  :   str    		=      sink

    while v != source:
      lowercase__  :   int    		=      parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase__  :   Union[str, Any]    		=      parent[v]

  for i in range(len(lowercase_   )   ):
    for j in range(len(graph[0]   )   ):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)   )

  return res


if __name__ == "__main__":
     print(mincut(test_graph, source=0, sink=5))






