

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class   __lowercase				(_UpperCAmelCase		,       _UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  IFImgaImgPipeline
 _UpperCamelCase        =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
 _UpperCamelCase        =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _UpperCamelCase        =  PipelineTesterMixin.required_optional_params - {"""latents"""}
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       return self._get_dummy_components()
 def      UpperCamelCase__  (							self				,							A_				,							A_=0 ) ->Optional[Any]:



       '''simple docstring'''





       if str(A_ ).startswith('''mps''' ):
             __lowerCAmelCase :						Any = torch.manual_seed(A_ )
       else:
             __lowerCAmelCase :						int = torch.Generator(device=A_ ).manual_seed(A_ )

       __lowerCAmelCase :						List[str] = floats_tensor((1, 3, 32, 32)				,							rng=random.Random(A_ ) ).to(A_ )

       __lowerCAmelCase :						Optional[Any] = {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       self._test_save_load_optional_components()
 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()				,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,							)
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
 @unittest.skipIf(torch_device != '''cuda'''				,							reason='''float16 requires CUDA''' )
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       super().test_save_load_floataa(expected_max_diff=1e-1 )
 @unittest.skipIf(torch_device != '''cuda'''				,							reason='''float16 requires CUDA''' )
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       super().test_floataa_inference(expected_max_diff=1e-1 )
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
 def      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       self._test_save_load_local()





 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2				,							)





import heapq







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = [(0, start)]  # cost from start node,end node
      __lowerCAmelCase :						Any = set()
      while heap:
            ((__lowerCAmelCase),							(__lowerCAmelCase)) :						str = heapq.heappop(lowercase__						)
            if u in visited:
                  continue
            visited.add(lowercase__						)
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  __lowerCAmelCase :						str = cost + c
                  heapq.heappush(lowercase__       ,     (next_item, v)						)
      return -1


_UpperCamelCase			      =		{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_UpperCamelCase			      =		dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
 import doctest

 doctest.testmod()


