





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        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->str:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:				Optional[int]        =TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")

   torch.manual_seed(0)
   lowerCamelCase__:				int        =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

   torch.manual_seed(0)
   lowerCamelCase__:				Union[str, Any]        =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)
   lowerCamelCase__:				List[Any]        =DDPMScheduler(
       num_train_timesteps=1_000	,			beta_schedule="squaredcos_cap_v2"	,			beta_start=0.0001	,			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)
   lowerCamelCase__:				List[Any]        =IFWatermarker()

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:				int        =TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")

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

   torch.manual_seed(0)
   lowerCamelCase__:				Tuple        =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=1.414	,			time_embedding_act_fn="gelu"	,			time_embedding_dim=32	,			)
   unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

   torch.manual_seed(0)
   lowerCamelCase__:				Tuple        =DDPMScheduler(
       num_train_timesteps=1_000	,			beta_schedule="squaredcos_cap_v2"	,			beta_start=0.0001	,			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)
   lowerCamelCase__:				Union[str, Any]        =DDPMScheduler(
       num_train_timesteps=1_000	,			beta_schedule="squaredcos_cap_v2"	,			beta_start=0.0001	,			beta_end=0.02	,			)

   torch.manual_seed(0)
   lowerCamelCase__:				Union[str, 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 							SCREAMING_SNAKE_CASE_ (self    :       int)	->str:



   '''simple docstring'''



   lowerCamelCase__:				int        =self.get_dummy_components()
   lowerCamelCase__:				Any        =self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:				str        =self.get_dummy_inputs(UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =inputs["prompt"]
   lowerCamelCase__:				Optional[Any]        =inputs["generator"]
   lowerCamelCase__:				Tuple        =inputs["num_inference_steps"]
   lowerCamelCase__:				Tuple        =inputs["output_type"]

   if "image" in inputs:
     lowerCamelCase__:				Optional[Any]        =inputs["image"]
   else:
     lowerCamelCase__:				Any        =None

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

   if "original_image" in inputs:
     lowerCamelCase__:				Any        =inputs["original_image"]
   else:
     lowerCamelCase__:				List[str]        =None

   lowerCamelCase__  , lowerCamelCase__:				Tuple        =pipe.encode_prompt(UpperCAmelCase_)

   # inputs with prompt converted to embeddings
   lowerCamelCase__:				int        ={
       "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:
     lowerCamelCase__:				Any        =image

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

   if original_image is not None:
     lowerCamelCase__:				List[str]        =original_image

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

   lowerCamelCase__:				Dict        =pipe(**UpperCAmelCase_)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(UpperCAmelCase_)
     lowerCamelCase__:				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."""	,			)

   lowerCamelCase__:				List[Any]        =self.get_dummy_inputs(UpperCAmelCase_)

   lowerCamelCase__:				List[Any]        =inputs["generator"]
   lowerCamelCase__:				Tuple        =inputs["num_inference_steps"]
   lowerCamelCase__:				List[str]        =inputs["output_type"]

   # inputs with prompt converted to embeddings
   lowerCamelCase__:				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:
     lowerCamelCase__:				Optional[int]        =image

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

   if original_image is not None:
     lowerCamelCase__:				List[Any]        =original_image

   lowerCamelCase__:				str        =pipe_loaded(**UpperCAmelCase_)[0]

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.get_dummy_components()
   lowerCamelCase__:				Tuple        =self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

   with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(UpperCAmelCase_)
     lowerCamelCase__:				Union[str, 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

   lowerCamelCase__:				Union[str, Any]        =self.get_dummy_inputs(UpperCAmelCase_)
   lowerCamelCase__:				int        =pipe_loaded(**UpperCAmelCase_)[0]

   lowerCamelCase__:				List[str]        =np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
   self.assertLess(UpperCAmelCase_	,			1E-4)








from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  print(F"""Vertex\tShortest Distance from vertex {src}"""		)
  for i, d in enumerate(__a		):
    print(F"""{i}\t\t{d}"""		)





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				str        =(graph[j][k] for k in ["src", "dst", "weight"])
    if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
      return True
  return False





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

      if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
        lowerCamelCase__:				int        =distance[u] + w

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     __A     =    int(input("Enter number of vertices: ").strip())
     __A     =    int(input("Enter number of edges: ").strip())

     __A     =    [{} for _ in range(E)]

     for i in range(E):
          print("Edge ", i + 1)
          __A ,     __A ,     __A     =    (
              int(x)
              for x in input("Enter source, destination, weight: ").strip().split(" ")
          )
          __A     =    {"src": src, "dst": dest, "weight": weight}

     __A     =    int(input("\nEnter shortest path source:").strip())
     __A     =    bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)

