'''simple docstring'''




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class     a__      (   UpperCAmelCase__							,   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     int							=StableDiffusionPanoramaPipeline
     lowerCamelCase  :     Dict							=TEXT_TO_IMAGE_PARAMS
     lowerCamelCase  :     Union[str, Any]							=TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCamelCase  :     Dict							=TEXT_TO_IMAGE_IMAGE_PARAMS
     lowerCamelCase  :     int							=TEXT_TO_IMAGE_IMAGE_PARAMS






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         torch.manual_seed(0    )
         __lowerCamelCase		=  UNetaDConditionModel(
             block_out_channels=(32, 64)  ,				layers_per_block=1  ,				sample_size=32  ,				in_channels=4  ,				out_channels=4  ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,				cross_attention_dim=32  ,				)
         __lowerCamelCase		=  DDIMScheduler()
         torch.manual_seed(0    )
         __lowerCamelCase		=  AutoencoderKL(
             block_out_channels=[32, 64]  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,				latent_channels=4  ,				)
         torch.manual_seed(0    )
         __lowerCamelCase		=  CLIPTextConfig(
             bos_token_id=0  ,				eos_token_id=2  ,				hidden_size=32  ,				intermediate_size=37  ,				layer_norm_eps=1e-0_5  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				pad_token_id=1  ,				vocab_size=10_00  ,				)
         __lowerCamelCase		=  CLIPTextModel(a    )
         __lowerCamelCase		=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         __lowerCamelCase		=  {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]  ,				a		:		Dict  ,				a		:		Union[str, Any]=0    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.manual_seed(a    )
         __lowerCamelCase		=  {
             '''prompt''': '''a photo of the dolomites''',
             '''generator''': generator,
             # Setting height and width to None to prevent OOMs on CPU.
             '''height''': None,
             '''width''': None,
             '''num_inference_steps''': 1,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase		=  np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         super().test_inference_batch_consistent(batch_sizes=[1, 2]    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         super().test_inference_batch_single_identical(batch_size=2  ,				expected_max_diff=3.2_5e-3    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  '''french fries'''
         __lowerCamelCase		=  sd_pipe(**a  ,				negative_prompt=a    )
         __lowerCamelCase		=  output.images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase		=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a  ,				view_batch_size=2    )
         __lowerCamelCase		=  output.images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase		=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule='''scaled_linear'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase		=  np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  PNDMScheduler(
             beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule='''scaled_linear'''  ,				skip_prk_steps=a    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase		=  np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




@slow
@require_torch_gpu
class     a__      (   unittest.TestCase ):






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		str=0    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.manual_seed(a    )
         __lowerCamelCase		=  {
             '''prompt''': '''a photo of the dolomites''',
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''stabilityai/stable-diffusion-2-base'''
         __lowerCamelCase		=  DDIMScheduler.from_pretrained(a  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(a  ,				scheduler=a  ,				safety_checker=a    )
         pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 20_48, 3)

         __lowerCamelCase		=  np.array(
             [
                 0.36_96_83_92,
                 0.27_02_53_72,
                 0.32_44_67_66,
                 0.28_37_93_87,
                 0.36_36_32_74,
                 0.30_73_33_47,
                 0.27_10_00_27,
                 0.27_05_41_25,
                 0.25_53_60_96,
             ]    )

         assert np.abs(expected_slice - image_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-base'''  ,				safety_checker=a    )
         __lowerCamelCase		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 20_48, 3)

         __lowerCamelCase		=  np.array(
             [
                 [
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                 ]
             ]    )

         assert np.abs(expected_slice - image_slice    ).max() < 1e-3






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  0

         def callback_fn(a		:		int  ,				a		:		int  ,				a		:		torch.FloatTensor    ) -> None:
             __lowerCamelCase		=  True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                 __lowerCamelCase		=  latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 2_56)
                 __lowerCamelCase		=  latents[0, -3:, -3:, -1]

                 __lowerCamelCase		=  np.array(
                     [
                         0.18_68_18_69,
                         0.33_90_78_16,
                         0.5_36_12_76,
                         0.14_43_28_65,
                         -0.02_85_66_11,
                         -0.73_94_11_23,
                         0.23_39_79_87,
                         0.47_32_26_82,
                         -0.37_82_31_64,
                     ]    )
                 assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2
             elif step == 2:
                 __lowerCamelCase		=  latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 2_56)
                 __lowerCamelCase		=  latents[0, -3:, -3:, -1]

                 __lowerCamelCase		=  np.array(
                     [
                         0.18_53_96_45,
                         0.33_98_72_48,
                         0.5_37_85_59,
                         0.14_43_71_42,
                         -0.02_45_52_61,
                         -0.7_33_83_17,
                         0.23_99_07_55,
                         0.47_35_62_72,
                         -0.3_78_65_05,
                     ]    )

                 assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2

         __lowerCamelCase		=  False

         __lowerCamelCase		=  '''stabilityai/stable-diffusion-2-base'''
         __lowerCamelCase		=  DDIMScheduler.from_pretrained(a  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(a  ,				scheduler=a  ,				safety_checker=a    )
         __lowerCamelCase		=  pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  self.get_inputs()
         pipe(**a  ,				callback=a  ,				callback_steps=1    )
         assert callback_fn.has_been_called
         assert number_of_steps == 3






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase		=  '''stabilityai/stable-diffusion-2-base'''
         __lowerCamelCase		=  DDIMScheduler.from_pretrained(a  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(a  ,				scheduler=a  ,				safety_checker=a    )
         __lowerCamelCase		=  pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing(1    )
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    )

         __lowerCamelCase		=  torch.cuda.max_memory_allocated()
         # make sure that less than 5.2 GB is allocated
         assert mem_bytes < 5.5 * 10**9


'''simple docstring'''




class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   Any					=						None
         A       :   Optional[Any]					=						None
         A       :   Tuple					=						graph

         self._normalize_graph(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						len(SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						None





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         if sources is int:
             A       :   Dict					=						[sources]
         if sinks is int:
             A       :   str					=						[sinks]

         if len(SCREAMING_SNAKE_CASE	) == 0 or len(SCREAMING_SNAKE_CASE	) == 0:
             return

         A       :   Optional[int]					=						sources[0]
         A       :   Union[str, Any]					=						sinks[0]

         # make fake vertex if there are more
         # than one source or sink
         if len(SCREAMING_SNAKE_CASE	) > 1 or len(SCREAMING_SNAKE_CASE	) > 1:
             A       :   Optional[int]					=						0
             for i in sources:
                 max_input_flow += sum(self.graph[i]	)

             A       :   Dict					=						len(self.graph	) + 1
             for room in self.graph:
                 room.insert(0						,			0	)
             self.graph.insert(0						,			[0] * size	)
             for i in sources:
                 A       :   Dict					=						max_input_flow
             A       :   Tuple					=						0

             A       :   Tuple					=						len(self.graph	) + 1
             for room in self.graph:
                 room.append(0	)
             self.graph.append([0] * size	)
             for i in sinks:
                 A       :   Optional[Any]					=						max_input_flow
             A       :   Optional[Any]					=						size - 1





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         if self.maximum_flow_algorithm is None:
             raise Exception('''You need to set maximum flow algorithm before.'''	)
         if self.source_index is None or self.sink_index is None:
             return 0

         self.maximum_flow_algorithm.execute()
         return self.maximum_flow_algorithm.getMaximumFlow()





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						algorithm(self	)





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						flow_network
         A       :   Optional[Any]					=						flow_network.verticesCount
         A       :   Tuple					=						flow_network.sourceIndex
         A       :   Dict					=						flow_network.sinkIndex
         # it's just a reference, so you shouldn't change
         # it in your algorithms, use deep copy before doing that
         A       :   str					=						flow_network.graph
         A       :   Optional[Any]					=						False





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         if not self.executed:
             self._algorithm()
             A       :   Optional[int]					=						True





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         pass





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)
         # use this to save your result
         A       :   List[str]					=						-1





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         if not self.executed:
             raise Exception('''You should execute algorithm before using its result!'''	)

         return self.maximum_flow





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						[[0] * self.verticies_count for i in range(self.verticies_count	)]

         A       :   Union[str, Any]					=						[0] * self.verticies_count
         A       :   List[Any]					=						[0] * self.verticies_count





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Tuple					=						self.verticies_count

         # push some substance to graph
         for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]	):
             self.preflow[self.source_index][nextvertex_index] += bandwidth
             self.preflow[nextvertex_index][self.source_index] -= bandwidth
             self.excesses[nextvertex_index] += bandwidth

         # Relabel-to-front selection rule
         A       :   Optional[Any]					=						[
             i
             for i in range(self.verticies_count	)
             if i != self.source_index and i != self.sink_index
         ]

         # move through list
         A       :   Union[str, Any]					=						0
         while i < len(SCREAMING_SNAKE_CASE	):
             A       :   str					=						vertices_list[i]
             A       :   List[str]					=						self.heights[vertex_index]
             self.process_vertex(SCREAMING_SNAKE_CASE	)
             if self.heights[vertex_index] > previous_height:
                 # if it was relabeled, swap elements
                 # and start from 0 index
                 vertices_list.insert(0						,			vertices_list.pop(SCREAMING_SNAKE_CASE	)	)
                 A       :   int					=						0
             else:
                 i += 1

         A       :   Optional[Any]					=						sum(self.preflow[self.source_index]	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         while self.excesses[vertex_index] > 0:
             for neighbour_index in range(self.verticies_count	):
                 # if it's neighbour and current vertex is higher
                 if (
                     self.graph[vertex_index][neighbour_index]
                     - self.preflow[vertex_index][neighbour_index]
                     > 0
                     and self.heights[vertex_index] > self.heights[neighbour_index]
                 ):
                     self.push(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

             self.relabel(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Dict					=						min(
             self.excesses[from_index]						,			self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,			)
         self.preflow[from_index][to_index] += preflow_delta
         self.preflow[to_index][from_index] -= preflow_delta
         self.excesses[from_index] -= preflow_delta
         self.excesses[to_index] += preflow_delta





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   Dict					=						None
         for to_index in range(self.verticies_count	):
             if (
                 self.graph[vertex_index][to_index]
                 - self.preflow[vertex_index][to_index]
                 > 0
             ) and (min_height is None or self.heights[to_index] < min_height):
                 A       :   Dict					=						self.heights[to_index]

         if min_height is not None:
             A       :   Dict					=						min_height + 1


if __name__ == "__main__":
      lowercase			:				Optional[int]    		=			[0]
      lowercase			:				List[Any]    		=			[3]
      # graph = [
      #     [0, 0, 4, 6, 0, 0],
      #     [0, 0, 5, 2, 0, 0],
      #     [0, 0, 0, 0, 4, 4],
      #     [0, 0, 0, 0, 6, 6],
      #     [0, 0, 0, 0, 0, 0],
      #     [0, 0, 0, 0, 0, 0],
      # ]
      lowercase			:				int    		=			[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

      # prepare our network
      lowercase			:				List[str]    		=			FlowNetwork(graph, entrances, exits)
      # set algorithm
      flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
      # and calculate
      lowercase			:				List[str]    		=			flow_network.find_maximum_flow()

      print(f'''maximum flow is {maximum_flow}''')




