






import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


UpperCAmelCase__				   =		logging.get_logger(__name__)

# General docstring
UpperCAmelCase__				   =		"PoolFormerConfig"

# Base docstring
UpperCAmelCase__				   =		"sail/poolformer_s12"
UpperCAmelCase__				   =		[1, 512, 7, 7]

# Image classification docstring
UpperCAmelCase__				   =		"sail/poolformer_s12"
UpperCAmelCase__				   =		"tabby, tabby cat"

UpperCAmelCase__				   =		[
    "sail/poolformer_s12",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def 						_a    (      a :Dict	,							a :float = 0.0	,							a :bool = False )							->			Optional[Any]:
 if drop_prob == 0.0 or not training:
  return input
 a		  =      1 - drop_prob
 a		  =      (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
 a		  =      keep_prob + torch.rand(a	,							dtype=input.dtype	,							device=input.device )
 random_tensor.floor_()  # binarize
 a		  =      input.div(a ) * random_tensor
 return output






class 		lowercase_					(	nn.Module   ):






      '''simple docstring'''




      def __init__(      self      :						int ,					__UpperCAmelCase      :						Optional[float] = None  )    ->None:




       """simple docstring"""


       super().__init__()
       a		  =      drop_prob







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						torch.Tensor  )    ->torch.Tensor:




       """simple docstring"""


       return drop_path(__UpperCAmelCase ,					self.drop_prob ,					self.training  )







      def       __lowerCAmelCase					(      self      :						int  )    ->str:




       """simple docstring"""


       return "p={}".format(self.drop_prob  )






class 		lowercase_					(	nn.Module   ):






      '''simple docstring'''




      def __init__(      self      :						List[Any] ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Any=None  )    ->int:




       """simple docstring"""


       super().__init__()
       a		  =      patch_size if isinstance(__UpperCAmelCase ,					collections.abc.Iterable  ) else (patch_size, patch_size)
       a		  =      stride if isinstance(__UpperCAmelCase ,					collections.abc.Iterable  ) else (stride, stride)
       a		  =      padding if isinstance(__UpperCAmelCase ,					collections.abc.Iterable  ) else (padding, padding)

       a		  =      nn.Convad(__UpperCAmelCase ,					__UpperCAmelCase ,					kernel_size=__UpperCAmelCase ,					stride=__UpperCAmelCase ,					padding=__UpperCAmelCase  )
       a		  =      norm_layer(__UpperCAmelCase  ) if norm_layer else nn.Identity()







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.projection(__UpperCAmelCase  )
       a		  =      self.norm(__UpperCAmelCase  )
       return embeddings






class 		lowercase_					(	nn.GroupNorm   ):






      '''simple docstring'''




      def __init__(      self      :						List[Any] ,					__UpperCAmelCase      :						Union[str, Any] ,					**__UpperCAmelCase      :						Union[str, Any]  )    ->Dict:




       """simple docstring"""


       super().__init__(1 ,					__UpperCAmelCase ,					**__UpperCAmelCase  )






class 		lowercase_					(	nn.Module   ):






      '''simple docstring'''




      def __init__(      self      :						int ,					__UpperCAmelCase      :						Optional[Any]  )    ->List[Any]:




       """simple docstring"""


       super().__init__()
       a		  =      nn.AvgPoolad(__UpperCAmelCase ,					stride=1 ,					padding=pool_size // 2 ,					count_include_pad=__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						List[str]  )    ->Union[str, Any]:




       """simple docstring"""


       return self.pool(__UpperCAmelCase  ) - hidden_states






class 		lowercase_					(	nn.Module   ):






      '''simple docstring'''




      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Dict  )    ->List[Any]:




       """simple docstring"""


       super().__init__()
       a		  =      nn.Convad(__UpperCAmelCase ,					__UpperCAmelCase ,					1  )
       a		  =      nn.Convad(__UpperCAmelCase ,					__UpperCAmelCase ,					1  )
       a		  =      PoolFormerDropPath(__UpperCAmelCase  )
       if isinstance(config.hidden_act ,					__UpperCAmelCase  ):
        a		  =      ACTaFN[config.hidden_act]
       else:
        a		  =      config.hidden_act







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       a		  =      self.conva(__UpperCAmelCase  )
       a		  =      self.act_fn(__UpperCAmelCase  )
       a		  =      self.drop(__UpperCAmelCase  )
       a		  =      self.conva(__UpperCAmelCase  )
       a		  =      self.drop(__UpperCAmelCase  )

       return hidden_states






class 		lowercase_					(	nn.Module   ):






      '''simple docstring'''




      def __init__(      self      :						int ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Tuple  )    ->int:




       """simple docstring"""


       super().__init__()
       a		  =      PoolFormerPooling(__UpperCAmelCase  )
       a		  =      PoolFormerOutput(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      PoolFormerGroupNorm(__UpperCAmelCase  )
       a		  =      PoolFormerGroupNorm(__UpperCAmelCase  )

       # Useful for training neural nets
       a		  =      PoolFormerDropPath(__UpperCAmelCase  ) if drop_path > 0.0 else nn.Identity()
       a		  =      config.use_layer_scale
       if config.use_layer_scale:
        a		  =      nn.Parameter(
            config.layer_scale_init_value * torch.ones((__UpperCAmelCase)  ) ,					requires_grad=__UpperCAmelCase  )
        a		  =      nn.Parameter(
            config.layer_scale_init_value * torch.ones((__UpperCAmelCase)  ) ,					requires_grad=__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						Dict  )    ->Any:




       """simple docstring"""


       if self.use_layer_scale:
        a		  =      self.pooling(self.before_norm(__UpperCAmelCase  )  )
        a		  =      self.layer_scale_a.unsqueeze(-1  ).unsqueeze(-1  ) * pooling_output
        # First residual connection
        a		  =      hidden_states + self.drop_path(__UpperCAmelCase  )
        a		  =      ()

        a		  =      self.output(self.after_norm(__UpperCAmelCase  )  )
        a		  =      self.layer_scale_a.unsqueeze(-1  ).unsqueeze(-1  ) * layer_output
        # Second residual connection
        a		  =      hidden_states + self.drop_path(__UpperCAmelCase  )

        a		  =      (output,) + outputs
        return outputs

       else:
        a		  =      self.drop_path(self.pooling(self.before_norm(__UpperCAmelCase  )  )  )
        # First residual connection
        a		  =      pooling_output + hidden_states
        a		  =      ()

        # Second residual connection inside the PoolFormerOutput block
        a		  =      self.drop_path(self.output(self.after_norm(__UpperCAmelCase  )  )  )
        a		  =      hidden_states + layer_output

        a		  =      (output,) + outputs
        return outputs






class 		lowercase_					(	nn.Module   ):






      '''simple docstring'''




      def __init__(      self      :						List[Any] ,					__UpperCAmelCase      :						Optional[int]  )    ->Dict:




       """simple docstring"""


       super().__init__()
       a		  =      config
       # stochastic depth decay rule
       a		  =      [x.item() for x in torch.linspace(0 ,					config.drop_path_rate ,					sum(config.depths  )  )]

       # patch embeddings
       a		  =      []
       for i in range(config.num_encoder_blocks  ):
        embeddings.append(
            PoolFormerEmbeddings(
                patch_size=config.patch_sizes[i] ,					stride=config.strides[i] ,					padding=config.padding[i] ,					num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] ,					hidden_size=config.hidden_sizes[i] ,					)  )
       a		  =      nn.ModuleList(__UpperCAmelCase  )

       # Transformer blocks
       a		  =      []
       a		  =      0
       for i in range(config.num_encoder_blocks  ):
        # each block consists of layers
        a		  =      []
        if i != 0:
         cur += config.depths[i - 1]
        for j in range(config.depths[i]  ):
         layers.append(
             PoolFormerLayer(
                 __UpperCAmelCase ,					num_channels=config.hidden_sizes[i] ,					pool_size=config.pool_size ,					hidden_size=config.hidden_sizes[i] ,					intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio  ) ,					drop_path=dpr[cur + j] ,					)  )
        blocks.append(nn.ModuleList(__UpperCAmelCase  )  )

       a		  =      nn.ModuleList(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[Any]=False ,					__UpperCAmelCase      :						Tuple=True  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      () if output_hidden_states else None

       a		  =      pixel_values
       for idx, layers in enumerate(zip(self.patch_embeddings ,					self.block  )  ):
        a  ,     a		  =      layers
        # Get patch embeddings from hidden_states
        a		  =      embedding_layer(__UpperCAmelCase  )
        # Send the embeddings through the blocks
        for _, blk in enumerate(__UpperCAmelCase  ):
         a		  =      blk(__UpperCAmelCase  )
         a		  =      layer_outputs[0]

        if output_hidden_states:
         a		  =      all_hidden_states + (hidden_states,)

       if not return_dict:
        return tuple(v for v in [hidden_states, all_hidden_states] if v is not None  )

       return BaseModelOutputWithNoAttention(last_hidden_state=__UpperCAmelCase ,					hidden_states=__UpperCAmelCase  )






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  PoolFormerConfig
      __snake_case       =  '''poolformer'''
      __snake_case       =  '''pixel_values'''
      __snake_case       =  True







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						List[str]  )    ->Optional[Any]:




       """simple docstring"""


       if isinstance(__UpperCAmelCase ,					(nn.Linear, nn.Convad)  ):
        module.weight.data.normal_(mean=0.0 ,					std=self.config.initializer_range  )
        if module.bias is not None:
         module.bias.data.zero_()
       elif isinstance(__UpperCAmelCase ,					nn.LayerNorm  ):
        module.bias.data.zero_()
        module.weight.data.fill_(1.0  )







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Union[str, Any]=False  )    ->int:




       """simple docstring"""


       if isinstance(__UpperCAmelCase ,					__UpperCAmelCase  ):
        a		  =      value
UpperCAmelCase__				   =		R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

UpperCAmelCase__				   =		R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n"






@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' ,  lowercase ,  )
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						str ,					__UpperCAmelCase      :						int  )    ->Optional[Any]:




       """simple docstring"""


       super().__init__(__UpperCAmelCase  )
       a		  =      config

       a		  =      PoolFormerEncoder(__UpperCAmelCase  )

       # Initialize weights and apply final processing
       self.post_init()







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




       """simple docstring"""


       return self.embeddings.patch_embeddings







      @add_start_docstrings_to_model_forward(__UpperCAmelCase  )
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC ,					output_type=__UpperCAmelCase ,					config_class=_CONFIG_FOR_DOC ,					modality='''vision''' ,					expected_output=_EXPECTED_OUTPUT_SHAPE ,					)
      def       __lowerCAmelCase					(      self      :						str ,					__UpperCAmelCase      :						Optional[torch.FloatTensor] = None ,					__UpperCAmelCase      :						Optional[bool] = None ,					__UpperCAmelCase      :						Optional[bool] = None ,					)    ->Union[Tuple, BaseModelOutputWithNoAttention]:




       """simple docstring"""


       a		  =      (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       a		  =      return_dict if return_dict is not None else self.config.use_return_dict

       if pixel_values is None:
        raise ValueError('''You have to specify pixel_values'''  )

       a		  =      self.encoder(
           __UpperCAmelCase ,					output_hidden_states=__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					)
       a		  =      encoder_outputs[0]

       if not return_dict:
        return (sequence_output, None) + encoder_outputs[1:]

       return BaseModelOutputWithNoAttention(
           last_hidden_state=__UpperCAmelCase ,					hidden_states=encoder_outputs.hidden_states ,					)






class 		lowercase_					(	nn.Module   ):






      '''simple docstring'''




      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						Dict  )    ->List[str]:




       """simple docstring"""


       super().__init__()
       a		  =      nn.Linear(config.hidden_size ,					config.hidden_size  )







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Optional[Any]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      self.dense(__UpperCAmelCase  )
       return output






@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    ''' ,  lowercase ,  )
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




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




       """simple docstring"""


       super().__init__(__UpperCAmelCase  )
       a		  =      config.num_labels
       a		  =      PoolFormerModel(__UpperCAmelCase  )

       # Final norm
       a		  =      PoolFormerGroupNorm(config.hidden_sizes[-1]  )
       # Classifier head
       a		  =      (
           nn.Linear(config.hidden_sizes[-1] ,					config.num_labels  ) if config.num_labels > 0 else nn.Identity()
       )

       # Initialize weights and apply final processing
       self.post_init()







      @add_start_docstrings_to_model_forward(__UpperCAmelCase  )
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT ,					output_type=__UpperCAmelCase ,					config_class=_CONFIG_FOR_DOC ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,					)
      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						Optional[torch.FloatTensor] = None ,					__UpperCAmelCase      :						Optional[torch.LongTensor] = None ,					__UpperCAmelCase      :						Optional[bool] = None ,					__UpperCAmelCase      :						Optional[bool] = None ,					)    ->Union[Tuple, ImageClassifierOutputWithNoAttention]:




       """simple docstring"""


       a		  =      return_dict if return_dict is not None else self.config.use_return_dict

       a		  =      self.poolformer(
           __UpperCAmelCase ,					output_hidden_states=__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					)

       a		  =      outputs[0]

       a		  =      self.classifier(self.norm(__UpperCAmelCase  ).mean([-2, -1]  )  )

       a		  =      None
       if labels is not None:
        if self.config.problem_type is None:
         if self.num_labels == 1:
          a		  =      '''regression'''
         elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
          a		  =      '''single_label_classification'''
         else:
          a		  =      '''multi_label_classification'''

        if self.config.problem_type == "regression":
         a		  =      MSELoss()
         if self.num_labels == 1:
          a		  =      loss_fct(logits.squeeze() ,					labels.squeeze()  )
         else:
          a		  =      loss_fct(__UpperCAmelCase ,					__UpperCAmelCase  )
        elif self.config.problem_type == "single_label_classification":
         a		  =      CrossEntropyLoss()
         a		  =      loss_fct(logits.view(-1 ,					self.num_labels  ) ,					labels.view(-1  )  )
        elif self.config.problem_type == "multi_label_classification":
         a		  =      BCEWithLogitsLoss()
         a		  =      loss_fct(__UpperCAmelCase ,					__UpperCAmelCase  )

       if not return_dict:
        a		  =      (logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

       return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase ,					logits=__UpperCAmelCase ,					hidden_states=outputs.hidden_states  )




'''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}''')




