





'''simple docstring'''

from typing import Optional

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

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowerCAmelCase							:     int		     =logging.get_logger(__name__)

# General docstring
__lowerCAmelCase							:     List[str]		     ="RegNetConfig"

# Base docstring
__lowerCAmelCase							:     str		     ="facebook/regnet-y-040"
__lowerCAmelCase							:     Tuple		     =[1, 1088, 7, 7]

# Image classification docstring
__lowerCAmelCase							:     Union[str, Any]		     ="facebook/regnet-y-040"
__lowerCAmelCase							:     Optional[Any]		     ="tabby, tabby cat"

__lowerCAmelCase							:     Optional[int]		     =[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:int						,	lowercase_				:int = 3						,	lowercase_				:int = 1						,	lowercase_				:int = 1						,	lowercase_				:Optional[str] = "relu"						,	)->							Optional[Any]:
     super().__init__()
     A__    =      nn.Convad(
         lowercase_						,	lowercase_						,	kernel_size=lowercase_						,	stride=lowercase_						,	padding=kernel_size // 2						,	groups=lowercase_						,	bias=lowercase_						,	)
     A__    =      nn.BatchNormad(lowercase_							)
     A__    =      ACTaFN[activation] if activation is not None else nn.Identity()



 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:int							)->							Optional[int]:
     A__    =      self.convolution(lowercase_							)
     A__    =      self.normalization(lowercase_							)
     A__    =      self.activation(lowercase_							)
     return hidden_state






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:Dict						,	lowercase_				:RegNetConfig							)->							Dict:
     super().__init__()
     A__    =      RegNetConvLayer(
         config.num_channels						,	config.embedding_size						,	kernel_size=3						,	stride=2						,	activation=config.hidden_act							)
     A__    =      config.num_channels



 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Dict							)->							Optional[Any]:
     A__    =      pixel_values.shape[1]
     if num_channels != self.num_channels:
         raise ValueError(
             "Make sure that the channel dimension of the pixel values match with the one set in the configuration."							)
     A__    =      self.embedder(lowercase_							)
     return hidden_state






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:List[str]						,	lowercase_				:int						,	lowercase_				:int						,	lowercase_				:int = 2							)->							Optional[int]:
     super().__init__()
     A__    =      nn.Convad(lowercase_						,	lowercase_						,	kernel_size=1						,	stride=lowercase_						,	bias=lowercase_							)
     A__    =      nn.BatchNormad(lowercase_							)



 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Tensor							)->							Tensor:
     A__    =      self.convolution(lowercase_							)
     A__    =      self.normalization(lowercase_							)
     return hidden_state






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:Dict						,	lowercase_				:int						,	lowercase_				:int							)->							int:
     super().__init__()

     A__    =      nn.AdaptiveAvgPoolad((1, 1)							)
     A__    =      nn.Sequential(
         nn.Convad(lowercase_						,	lowercase_						,	kernel_size=1							)						,	nn.ReLU()						,	nn.Convad(lowercase_						,	lowercase_						,	kernel_size=1							)						,	nn.Sigmoid()						,	)



 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:str							)->							Any:
     # b c h w -> b c 1 1
     A__    =      self.pooler(lowercase_							)
     A__    =      self.attention(lowercase_							)
     A__    =      hidden_state * attention
     return hidden_state






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:Optional[Any]						,	lowercase_				:RegNetConfig						,	lowercase_				:int						,	lowercase_				:int						,	lowercase_				:int = 1							)->							Union[str, Any]:
     super().__init__()
     A__    =      in_channels != out_channels or stride != 1
     A__    =      max(1						,	out_channels // config.groups_width							)
     A__    =      (
         RegNetShortCut(lowercase_						,	lowercase_						,	stride=lowercase_							) if should_apply_shortcut else nn.Identity()
     )
     A__    =      nn.Sequential(
         RegNetConvLayer(lowercase_						,	lowercase_						,	kernel_size=1						,	activation=config.hidden_act							)						,	RegNetConvLayer(lowercase_						,	lowercase_						,	stride=lowercase_						,	groups=lowercase_						,	activation=config.hidden_act							)						,	RegNetConvLayer(lowercase_						,	lowercase_						,	kernel_size=1						,	activation=lowercase_							)						,	)
     A__    =      ACTaFN[config.hidden_act]



 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:List[str]							)->							Optional[int]:
     A__    =      hidden_state
     A__    =      self.layer(lowercase_							)
     A__    =      self.shortcut(lowercase_							)
     hidden_state += residual
     A__    =      self.activation(lowercase_							)
     return hidden_state






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:Any						,	lowercase_				:RegNetConfig						,	lowercase_				:int						,	lowercase_				:int						,	lowercase_				:int = 1							)->							Optional[Any]:
     super().__init__()
     A__    =      in_channels != out_channels or stride != 1
     A__    =      max(1						,	out_channels // config.groups_width							)
     A__    =      (
         RegNetShortCut(lowercase_						,	lowercase_						,	stride=lowercase_							) if should_apply_shortcut else nn.Identity()
     )
     A__    =      nn.Sequential(
         RegNetConvLayer(lowercase_						,	lowercase_						,	kernel_size=1						,	activation=config.hidden_act							)						,	RegNetConvLayer(lowercase_						,	lowercase_						,	stride=lowercase_						,	groups=lowercase_						,	activation=config.hidden_act							)						,	RegNetSELayer(lowercase_						,	reduced_channels=int(round(in_channels / 4							)							)							)						,	RegNetConvLayer(lowercase_						,	lowercase_						,	kernel_size=1						,	activation=lowercase_							)						,	)
     A__    =      ACTaFN[config.hidden_act]



 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Optional[int]							)->							List[Any]:
     A__    =      hidden_state
     A__    =      self.layer(lowercase_							)
     A__    =      self.shortcut(lowercase_							)
     hidden_state += residual
     A__    =      self.activation(lowercase_							)
     return hidden_state






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:Tuple						,	lowercase_				:RegNetConfig						,	lowercase_				:int						,	lowercase_				:int						,	lowercase_				:int = 2						,	lowercase_				:int = 2						,	)->							Optional[Any]:
     super().__init__()

     A__    =      RegNetXLayer if config.layer_type == "x" else RegNetYLayer

     A__    =      nn.Sequential(
         # downsampling is done in the first layer with stride of 2
         layer(
             lowercase_						,	lowercase_						,	lowercase_						,	stride=lowercase_						,	)						,	*[layer(lowercase_						,	lowercase_						,	lowercase_							) for _ in range(depth - 1							)]						,	)



 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:List[Any]							)->							Any:
     A__    =      self.layers(lowercase_							)
     return hidden_state






class        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:List[Any]						,	lowercase_				:RegNetConfig							)->							List[str]:
     super().__init__()
     A__    =      nn.ModuleList([]							)
     # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
     self.stages.append(
         RegNetStage(
             lowercase_						,	config.embedding_size						,	config.hidden_sizes[0]						,	stride=2 if config.downsample_in_first_stage else 1						,	depth=config.depths[0]						,	)							)
     A__    =      zip(config.hidden_sizes						,	config.hidden_sizes[1:]							)
     for (in_channels, out_channels), depth in zip(lowercase_						,	config.depths[1:]							):
         self.stages.append(RegNetStage(lowercase_						,	lowercase_						,	lowercase_						,	depth=lowercase_							)							)



 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Tensor						,	lowercase_				:bool = False						,	lowercase_				:bool = True							)->							BaseModelOutputWithNoAttention:
     A__    =      () if output_hidden_states else None

     for stage_module in self.stages:
         if output_hidden_states:
             A__    =      hidden_states + (hidden_state,)

         A__    =      stage_module(lowercase_							)

     if output_hidden_states:
         A__    =      hidden_states + (hidden_state,)

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

     return BaseModelOutputWithNoAttention(last_hidden_state=lowercase_						,	hidden_states=lowercase_							)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    RegNetConfig
 __lowercase       		=    """regnet"""
 __lowercase       		=    """pixel_values"""
 __lowercase       		=    True
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Optional[int]							)->							Any:
     if isinstance(lowercase_						,	nn.Convad							):
         nn.init.kaiming_normal_(module.weight						,	mode="fan_out"						,	nonlinearity="relu"							)
     elif isinstance(lowercase_						,	(nn.BatchNormad, nn.GroupNorm)							):
         nn.init.constant_(module.weight						,	1							)
         nn.init.constant_(module.bias						,	0							)



 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:str						,	lowercase_				:Any=False							)->							Any:
     if isinstance(lowercase_						,	lowercase_							):
         A__    =      value

__lowerCAmelCase							:     int		     =R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    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 ([`RegNetConfig`]): 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"

__lowerCAmelCase							:     Union[str, Any]		     =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            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"






@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""      ,	UpperCamelCase__      ,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Union[str, Any]						,	lowercase_				:Union[str, Any]							)->							List[str]:
     super().__init__(lowercase_							)
     A__    =      config
     A__    =      RegNetEmbeddings(lowercase_							)
     A__    =      RegNetEncoder(lowercase_							)
     A__    =      nn.AdaptiveAvgPoolad((1, 1)							)
     # Initialize weights and apply final processing
     self.post_init()



 @add_start_docstrings_to_model_forward(lowercase_							)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC						,	output_type=lowercase_						,	config_class=_CONFIG_FOR_DOC						,	modality="vision"						,	expected_output=_EXPECTED_OUTPUT_SHAPE						,	)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Tensor						,	lowercase_				:Optional[bool] = None						,	lowercase_				:Optional[bool] = None							)->							BaseModelOutputWithPoolingAndNoAttention:
     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

     A__    =      self.embedder(lowercase_							)

     A__    =      self.encoder(
         lowercase_						,	output_hidden_states=lowercase_						,	return_dict=lowercase_							)

     A__    =      encoder_outputs[0]

     A__    =      self.pooler(lowercase_							)

     if not return_dict:
         return (last_hidden_state, pooled_output) + encoder_outputs[1:]

     return BaseModelOutputWithPoolingAndNoAttention(
         last_hidden_state=lowercase_						,	pooler_output=lowercase_						,	hidden_states=encoder_outputs.hidden_states						,	)






@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      ,	UpperCamelCase__      ,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:str						,	lowercase_				:Union[str, Any]							)->							str:
     super().__init__(lowercase_							)
     A__    =      config.num_labels
     A__    =      RegNetModel(lowercase_							)
     # classification head
     A__    =      nn.Sequential(
         nn.Flatten()						,	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(lowercase_							)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT						,	output_type=lowercase_						,	config_class=_CONFIG_FOR_DOC						,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,	)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Optional[torch.FloatTensor] = None						,	lowercase_				:Optional[torch.LongTensor] = None						,	lowercase_				:Optional[bool] = None						,	lowercase_				:Optional[bool] = None						,	)->							ImageClassifierOutputWithNoAttention:
     A__    =      return_dict if return_dict is not None else self.config.use_return_dict

     A__    =      self.regnet(lowercase_						,	output_hidden_states=lowercase_						,	return_dict=lowercase_							)

     A__    =      outputs.pooler_output if return_dict else outputs[1]

     A__    =      self.classifier(lowercase_							)

     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(lowercase_						,	lowercase_							)
         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(lowercase_						,	lowercase_							)

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

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










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple	):
    # Return True if there is node that has not iterated.
    A__    =      [False] * len(_lowerCamelCase	)
    A__    =      []
    queue.append(_lowerCamelCase	)
    A__    =      True

    while queue:
        A__    =      queue.pop(0	)
        for ind in range(len(graph[u]	)	):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(_lowerCamelCase	)
                A__    =      True
                A__    =      u

    return visited[t]





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]	):
    # This array is filled by BFS and to store path
    A__    =      [-1] * (len(_lowerCamelCase	))
    A__    =      0
    while bfs(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      float("Inf"	)
        A__    =      sink

        while s != source:
            # Find the minimum value in select path
            A__    =      min(_lowerCamelCase						,	graph[parent[s]][s]	)
            A__    =      parent[s]

        max_flow += path_flow
        A__    =      sink

        while v != source:
            A__    =      parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            A__    =      parent[v]
    return max_flow


__lowerCAmelCase							:     Any		     =[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__lowerCAmelCase     ,					__lowerCAmelCase							:     int		     =0, 5



print(ford_fulkerson(graph, source, sink))



