import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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 			__snake_case   (     lowerCamelCase__					,   lowerCamelCase__					,   unittest.TestCase   ):
	__lowerCamelCase    :   Optional[Any]     =      IFSuperResolutionPipeline
	__lowerCamelCase    :   str     =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
	__lowerCamelCase    :   Tuple     =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	__lowerCamelCase    :   Optional[int]     =      PipelineTesterMixin.required_optional_params - {"""latents"""}
	def 	UpperCAmelCase__       (  self						)	->							Tuple:
							'''simple docstring'''





							return self._get_superresolution_dummy_components()
	def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__=0						)	->							List[Any]:
							'''simple docstring'''





							if str(snake_case__						).startswith('''mps'''						):
													UpperCAmelCase    : str      =torch.manual_seed(snake_case__						)
							else:
													UpperCAmelCase    : str      =torch.Generator(device=snake_case__						).manual_seed(snake_case__						)

							UpperCAmelCase    : int      =floats_tensor((1, 3, 32, 32)		,   rng=random.Random(snake_case__						)						).to(snake_case__						)

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

							return inputs
	@unittest.skipIf(
	    torch_device != '''cuda''' or not is_xformers_available()		,   reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,   )
	def 	UpperCAmelCase__       (  self						)	->							List[str]:
							'''simple docstring'''





							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)
	def 	UpperCAmelCase__       (  self						)	->							Tuple:
							'''simple docstring'''





							self._test_save_load_optional_components()
	@unittest.skipIf(torch_device != '''cuda'''		,   reason='''float16 requires CUDA'''						)
	def 	UpperCAmelCase__       (  self						)	->							str:
							'''simple docstring'''





							super().test_save_load_floataa(expected_max_diff=1e-1						)
	def 	UpperCAmelCase__       (  self						)	->							int:
							'''simple docstring'''





							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)
	def 	UpperCAmelCase__       (  self						)	->							int:
							'''simple docstring'''





							self._test_save_load_local()






	def 	UpperCAmelCase__       (  self						)	->							Any:
							'''simple docstring'''





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

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

      UpperCAmelCase    : str      ={}
      for node in nodes:
            UpperCAmelCase    : int      =len(node.outbound						)

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

      for ri, row in enumerate(__lowerCAmelCase						):
            for ci, col in enumerate(__lowerCAmelCase						):
                  if col == 1:
                        nodes[ci].add_inbound(names[ri]						)
                        nodes[ri].add_outbound(names[ci]						)

      print('''======= Nodes ======='''						)
      for node in nodes:
            print(__lowerCAmelCase						)

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
