'''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		:







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         torch.manual_seed(0    )
         __UpperCamelCase	:	int		    =  TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'    )

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

         torch.manual_seed(0    )
         __UpperCamelCase	:	str		    =  UNetaDConditionModel(
             sample_size=3_2     ,			layers_per_block=1     ,			block_out_channels=[3_2, 6_4]     ,			down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]     ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,			in_channels=3     ,			out_channels=6     ,			cross_attention_dim=3_2     ,			encoder_hid_dim=3_2     ,			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	:	List[str]		    =  DDPMScheduler(
             num_train_timesteps=1_0_0_0     ,			beta_schedule='squaredcos_cap_v2'     ,			beta_start=0.0_0_0_1     ,			beta_end=0.0_2     ,			thresholding=_lowerCamelCase     ,			dynamic_thresholding_ratio=0.9_5     ,			sample_max_value=1.0     ,			prediction_type='epsilon'     ,			variance_type='learned_range'     ,			)

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

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







     def lowerCAmelCase     (		self    ):
         torch.manual_seed(0    )
         __UpperCamelCase	:	Any		    =  TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'    )

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

         torch.manual_seed(0    )
         __UpperCamelCase	:	List[Any]		    =  UNetaDConditionModel(
             sample_size=3_2     ,			layers_per_block=[1, 2]     ,			block_out_channels=[3_2, 6_4]     ,			down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]     ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,			in_channels=6     ,			out_channels=6     ,			cross_attention_dim=3_2     ,			encoder_hid_dim=3_2     ,			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_1_4     ,			time_embedding_act_fn='gelu'     ,			time_embedding_dim=3_2     ,			)
         unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

         torch.manual_seed(0    )
         __UpperCamelCase	:	str		    =  DDPMScheduler(
             num_train_timesteps=1_0_0_0     ,			beta_schedule='squaredcos_cap_v2'     ,			beta_start=0.0_0_0_1     ,			beta_end=0.0_2     ,			thresholding=_lowerCamelCase     ,			dynamic_thresholding_ratio=0.9_5     ,			sample_max_value=1.0     ,			prediction_type='epsilon'     ,			variance_type='learned_range'     ,			)

         torch.manual_seed(0    )
         __UpperCamelCase	:	List[Any]		    =  DDPMScheduler(
             num_train_timesteps=1_0_0_0     ,			beta_schedule='squaredcos_cap_v2'     ,			beta_start=0.0_0_0_1     ,			beta_end=0.0_2     ,			)

         torch.manual_seed(0    )
         __UpperCamelCase	:	Tuple		    =  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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  self.get_dummy_components()
         __UpperCamelCase	:	str		    =  self.pipeline_class(**_lowerCamelCase    )
         pipe.to(_lowerCamelCase    )
         pipe.set_progress_bar_config(disable=_lowerCamelCase    )

         __UpperCamelCase	:	Any		    =  self.get_dummy_inputs(_lowerCamelCase    )

         __UpperCamelCase	:	Dict		    =  inputs["""prompt"""]
         __UpperCamelCase	:	Tuple		    =  inputs["""generator"""]
         __UpperCamelCase	:	Tuple		    =  inputs["""num_inference_steps"""]
         __UpperCamelCase	:	Tuple		    =  inputs["""output_type"""]

         if "image" in inputs:
             __UpperCamelCase	:	List[str]		    =  inputs["""image"""]
         else:
             __UpperCamelCase	:	Union[str, Any]		    =  None

         if "mask_image" in inputs:
             __UpperCamelCase	:	Optional[Any]		    =  inputs["""mask_image"""]
         else:
             __UpperCamelCase	:	Tuple		    =  None

         if "original_image" in inputs:
             __UpperCamelCase	:	List[str]		    =  inputs["""original_image"""]
         else:
             __UpperCamelCase	:	Optional[int]		    =  None

         __UpperCamelCase	:	str		    =  pipe.encode_prompt(_lowerCamelCase    )

         # inputs with prompt converted to embeddings
         __UpperCamelCase	:	Union[str, 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	:	int		    =  image

         if mask_image is not None:
             __UpperCamelCase	:	Optional[Any]		    =  mask_image

         if original_image is not None:
             __UpperCamelCase	:	str		    =  original_image

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

         __UpperCamelCase	:	Optional[int]		    =  pipe(**_lowerCamelCase    )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_lowerCamelCase    )
             __UpperCamelCase	:	Any		    =  self.pipeline_class.from_pretrained(_lowerCamelCase    )
             pipe_loaded.to(_lowerCamelCase    )
             pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase    )

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

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

         __UpperCamelCase	:	Optional[int]		    =  self.get_dummy_inputs(_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  inputs["""generator"""]
         __UpperCamelCase	:	List[str]		    =  inputs["""num_inference_steps"""]
         __UpperCamelCase	:	List[str]		    =  inputs["""output_type"""]

         # inputs with prompt converted to embeddings
         __UpperCamelCase	:	Dict		    =  {
             """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	:	Union[str, Any]		    =  image

         if mask_image is not None:
             __UpperCamelCase	:	List[str]		    =  mask_image

         if original_image is not None:
             __UpperCamelCase	:	int		    =  original_image

         __UpperCamelCase	:	List[str]		    =  pipe_loaded(**_lowerCamelCase    )[0]

         __UpperCamelCase	:	Optional[int]		    =  np.abs(to_np(_lowerCamelCase    ) - to_np(_lowerCamelCase    )    ).max()
         self.assertLess(_lowerCamelCase     ,			1E-4    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.get_dummy_components()
         __UpperCamelCase	:	Any		    =  self.pipeline_class(**_lowerCamelCase    )
         pipe.to(_lowerCamelCase    )
         pipe.set_progress_bar_config(disable=_lowerCamelCase    )

         __UpperCamelCase	:	Optional[Any]		    =  self.get_dummy_inputs(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  pipe(**_lowerCamelCase    )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_lowerCamelCase    )
             __UpperCamelCase	:	Tuple		    =  self.pipeline_class.from_pretrained(_lowerCamelCase    )
             pipe_loaded.to(_lowerCamelCase    )
             pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase    )

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

         __UpperCamelCase	:	Tuple		    =  self.get_dummy_inputs(_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  pipe_loaded(**_lowerCamelCase    )[0]

         __UpperCamelCase	:	List[Any]		    =  np.abs(to_np(_lowerCamelCase    ) - to_np(_lowerCamelCase    )    ).max()
         self.assertLess(_lowerCamelCase     ,			1E-4    )




'''simple docstring'''
class 			__lowercase		:







     """simple docstring"""






     def __init__(		self    ):
         __UpperCamelCase	:	Any		    =  0
         __UpperCamelCase	:	Any		    =  0
         __UpperCamelCase	:	Any		    =  {}







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         if vertex not in self.adjacency:
             __UpperCamelCase	:	Tuple		    =  {}
             self.num_vertices += 1







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         self.add_vertex(_lowerCamelCase    )
         self.add_vertex(_lowerCamelCase    )

         if head == tail:
             return

         __UpperCamelCase	:	List[Any]		    =  weight
         __UpperCamelCase	:	Optional[int]		    =  weight







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

         edges.sort(key=lambda _lowerCamelCase    : e[2]    )
         for i in range(len(_lowerCamelCase    ) - 1    ):
             if edges[i][2] >= edges[i + 1][2]:
                 __UpperCamelCase	:	Optional[int]		    =  edges[i][2] + 1
         for edge in edges:
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  edge
             __UpperCamelCase	:	Tuple		    =  weight
             __UpperCamelCase	:	Optional[Any]		    =  weight







     def __str__(		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  ''
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 __UpperCamelCase	:	Tuple		    =  self.adjacency[head][tail]
                 string += f"""{head} -> {tail} == {weight}\n"""
         return string.rstrip('\n'    )







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







     def lowerCAmelCase     (		self    ):
         return self.adjacency.keys()







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






     class 			__lowercase		:







          """simple docstring"""






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







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







          def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
              if item in self.parent:
                  return self.find(_lowerCamelCase    )

              __UpperCamelCase	:	Optional[Any]		    =  item
              __UpperCamelCase	:	List[str]		    =  0
              return item







          def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
              if item not in self.parent:
                  return self.make_set(_lowerCamelCase    )
              if item != self.parent[item]:
                  __UpperCamelCase	:	int		    =  self.find(self.parent[item]    )
              return self.parent[item]







          def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
              __UpperCamelCase	:	Tuple		    =  self.find(_lowerCamelCase    )
              __UpperCamelCase	:	Any		    =  self.find(_lowerCamelCase    )

              if roota == roota:
                  return roota

              if self.rank[roota] > self.rank[roota]:
                  __UpperCamelCase	:	Dict		    =  roota
                  return roota

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

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







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  graph.num_vertices

         __UpperCamelCase	:	str		    =  Graph.UnionFind()
         __UpperCamelCase	:	Tuple		    =  []
         while num_components > 1:
             __UpperCamelCase	:	Dict		    =  {}
             for vertex in graph.get_vertices():
                 __UpperCamelCase	:	str		    =  -1

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

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



