






'''simple docstring'''




class       __lowercase					:

 def __init__(      self   :						str):
   UpperCamelCase__				:					Union[str, Any]					=					0
   UpperCamelCase__				:					Any					=					0
   UpperCamelCase__				:					int					=					{}

 def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						int):
   if vertex not in self.adjacency:
     UpperCamelCase__				:					Tuple					=					{}
     self.num_vertices += 1

 def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						List[Any]  ,					UpperCAmelCase_   :						Tuple):
   self.add_vertex(UpperCAmelCase_)
   self.add_vertex(UpperCAmelCase_)

   if head == tail:
     return

   UpperCamelCase__				:					Tuple					=					weight
   UpperCamelCase__				:					Optional[int]					=					weight

 def      __UpperCamelCase							(      self   :						Union[str, Any]):
   UpperCamelCase__				:					List[Any]					=					self.get_edges()
   for edge in edges:
     UpperCamelCase__				:					str					=					edge
     edges.remove((tail, head, weight))
   for i in range(len(UpperCAmelCase_)):
     UpperCamelCase__				:					Any					=					list(edges[i])

   edges.sort(key=lambda UpperCAmelCase_: e[2])
   for i in range(len(UpperCAmelCase_) - 1):
     if edges[i][2] >= edges[i + 1][2]:
       UpperCamelCase__				:					Optional[int]					=					edges[i][2] + 1
   for edge in edges:
     UpperCamelCase__				:					List[Any]					=					edge
     UpperCamelCase__				:					Dict					=					weight
     UpperCamelCase__				:					List[Any]					=					weight

 def __str__(      self   :						Any):
   UpperCamelCase__				:					List[Any]					=					''
   for tail in self.adjacency:
     for head in self.adjacency[tail]:
       UpperCamelCase__				:					Union[str, Any]					=					self.adjacency[head][tail]
       string += F'{head} -> {tail} == {weight}\n'
   return string.rstrip('\n')

 def      __UpperCamelCase							(      self   :						Optional[Any]):
   UpperCamelCase__				:					Optional[Any]					=					[]
   for tail in self.adjacency:
     for head in self.adjacency[tail]:
       output.append((tail, head, self.adjacency[head][tail]))
   return output

 def      __UpperCamelCase							(      self   :						List[str]):
   return self.adjacency.keys()

 @staticmethod
 def      __UpperCamelCase							(      UpperCAmelCase_   :						Any=None  ,					UpperCAmelCase_   :						str=None):
   UpperCamelCase__				:					Tuple					=					Graph()
   if vertices is None:
     UpperCamelCase__				:					Tuple					=					[]
   if edges is None:
     UpperCamelCase__				:					List[str]					=					[]
   for vertex in vertices:
     g.add_vertex(UpperCAmelCase_)
   for edge in edges:
     g.add_edge(*UpperCAmelCase_)
   return g







 class       __lowercase					:

  def __init__(      self   :						Tuple):
    UpperCamelCase__				:					int					=					{}
    UpperCamelCase__				:					List[str]					=					{}

  def __len__(      self   :						Any):
    return len(self.parent)

  def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Optional[Any]):
    if item in self.parent:
      return self.find(UpperCAmelCase_)

    UpperCamelCase__				:					List[Any]					=					item
    UpperCamelCase__				:					Dict					=					0
    return item

  def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Optional[int]):
    if item not in self.parent:
      return self.make_set(UpperCAmelCase_)
    if item != self.parent[item]:
      UpperCamelCase__				:					List[str]					=					self.find(self.parent[item])
    return self.parent[item]





  def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						int):
    UpperCamelCase__				:					List[str]					=					self.find(UpperCAmelCase_)
    UpperCamelCase__				:					Tuple					=					self.find(UpperCAmelCase_)

    if roota == roota:
      return roota

    if self.rank[roota] > self.rank[roota]:
      UpperCamelCase__				:					Union[str, Any]					=					roota
      return roota

    if self.rank[roota] < self.rank[roota]:
      UpperCamelCase__				:					Any					=					roota
      return roota

    if self.rank[roota] == self.rank[roota]:
      self.rank[roota] += 1
      UpperCamelCase__				:					Dict					=					roota
      return roota
    return None


 @staticmethod
 def      __UpperCamelCase							(      UpperCAmelCase_   :						Optional[Any]):
   UpperCamelCase__				:					Optional[Any]					=					graph.num_vertices

   UpperCamelCase__				:					Tuple					=					Graph.UnionFind()
   UpperCamelCase__				:					Optional[int]					=					[]
   while num_components > 1:
     UpperCamelCase__				:					Tuple					=					{}
     for vertex in graph.get_vertices():
       UpperCamelCase__				:					Optional[Any]					=					-1

     UpperCamelCase__				:					int					=					graph.get_edges()
     for edge in edges:
       UpperCamelCase__				:					str					=					edge
       edges.remove((tail, head, weight))
     for edge in edges:
       UpperCamelCase__				:					Optional[Any]					=					edge
       UpperCamelCase__				:					Union[str, Any]					=					union_find.find(UpperCAmelCase_)
       UpperCamelCase__				:					List[Any]					=					union_find.find(UpperCAmelCase_)
       if seta != seta:
         if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
           UpperCamelCase__				:					Optional[Any]					=					[head, tail, weight]

         if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
           UpperCamelCase__				:					Tuple					=					[head, tail, weight]
     for vertex in cheap_edge:
       if cheap_edge[vertex] != -1:
         UpperCamelCase__				:					Union[str, Any]					=					cheap_edge[vertex]
         if union_find.find(UpperCAmelCase_) != union_find.find(UpperCAmelCase_):
           union_find.union(UpperCAmelCase_  ,					UpperCAmelCase_)
           mst_edges.append(cheap_edge[vertex])
           UpperCamelCase__				:					int					=					num_components - 1
   UpperCamelCase__				:					Dict					=					Graph.build(edges=UpperCAmelCase_)
   return mst












'''simple docstring'''




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class       __lowercase					:

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Dict					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=1  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=3  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Dict):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=[1, 2]  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=6  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					class_embed_type='timestep'  ,					mid_block_scale_factor=1.4_14  ,					time_embedding_act_fn='gelu'  ,					time_embedding_dim=32  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					str					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "image_noising_scheduler": image_noising_scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Dict					=					self.get_dummy_components()
			UpperCamelCase__				:					List[Any]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					inputs['prompt']
			UpperCamelCase__				:					List[Any]					=					inputs['generator']
			UpperCamelCase__				:					Tuple					=					inputs['num_inference_steps']
			UpperCamelCase__				:					List[Any]					=					inputs['output_type']

			if "image" in inputs:
					UpperCamelCase__				:					Tuple					=					inputs['image']
			else:
					UpperCamelCase__				:					Union[str, Any]					=					None

			if "mask_image" in inputs:
					UpperCamelCase__				:					Optional[int]					=					inputs['mask_image']
			else:
					UpperCamelCase__				:					int					=					None

			if "original_image" in inputs:
					UpperCamelCase__				:					List[Any]					=					inputs['original_image']
			else:
					UpperCamelCase__				:					Optional[Any]					=					None

			UpperCamelCase__,	UpperCamelCase__				:					Any					=					pipe.encode_prompt(UpperCAmelCase_)

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					List[Any]					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Dict					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Optional[int]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					original_image

			# set all optional components to None
			for optional_component in pipe._optional_components:
					setattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(UpperCAmelCase_  ,					UpperCAmelCase_) is None  ,					F'`{optional_component}` did not stay set to None after loading.'  ,					)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					inputs['generator']
			UpperCamelCase__				:					List[Any]					=					inputs['num_inference_steps']
			UpperCamelCase__				:					Optional[int]					=					inputs['output_type']

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					Any					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Tuple					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					str					=					original_image

			UpperCamelCase__				:					Union[str, Any]					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Dict					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Any					=					self.get_dummy_components()
			UpperCamelCase__				:					List[str]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			UpperCamelCase__				:					Any					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Optional[int]					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)




