




'''simple docstring'''

from typing import Optional, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, 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_mobilenet_va import MobileNetVaConfig


a    :							int										=    logging.get_logger(__name__)


# General docstring
a    :							List[str]										=    'MobileNetV1Config'

# Base docstring
a    :							Optional[Any]										=    'google/mobilenet_v1_1.0_224'
a    :							Optional[int]										=    [1, 1024, 7, 7]

# Image classification docstring
a    :							Tuple										=    'google/mobilenet_v1_1.0_224'
a    :							Optional[Any]										=    'tabby, tabby cat'


a    :							str										=    [
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=None      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							{}

					if isinstance(__UpperCAmelCase, __UpperCAmelCase      ):
										snake_case_		     =							model.mobilenet_va
					else:
										snake_case_		     =							model

					snake_case_		     =							'''MobilenetV1/Conv2d_0/'''
					snake_case_		     =							backbone.conv_stem.convolution.weight
					snake_case_		     =							backbone.conv_stem.normalization.bias
					snake_case_		     =							backbone.conv_stem.normalization.weight
					snake_case_		     =							backbone.conv_stem.normalization.running_mean
					snake_case_		     =							backbone.conv_stem.normalization.running_var

					for i in range(13      ):
										snake_case_		     =							i + 1
										snake_case_		     =							i * 2

										snake_case_		     =							backbone.layer[pt_index]
										snake_case_		     =							F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
										snake_case_		     =							pointer.convolution.weight
										snake_case_		     =							pointer.normalization.bias
										snake_case_		     =							pointer.normalization.weight
										snake_case_		     =							pointer.normalization.running_mean
										snake_case_		     =							pointer.normalization.running_var

										snake_case_		     =							backbone.layer[pt_index + 1]
										snake_case_		     =							F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
										snake_case_		     =							pointer.convolution.weight
										snake_case_		     =							pointer.normalization.bias
										snake_case_		     =							pointer.normalization.weight
										snake_case_		     =							pointer.normalization.running_mean
										snake_case_		     =							pointer.normalization.running_var

					if isinstance(__UpperCAmelCase, __UpperCAmelCase      ):
										snake_case_		     =							'''MobilenetV1/Logits/Conv2d_1c_1x1/'''
										snake_case_		     =							model.classifier.weight
										snake_case_		     =							model.classifier.bias

					return tf_to_pt_map


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					try:
										import numpy as np
										import tensorflow as tf
					except ImportError:
										logger.error(
										    '''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
										    '''https://www.tensorflow.org/install/ for installation instructions.'''      )
										raise

					# Load weights from TF model
					snake_case_		     =							tf.train.list_variables(__UpperCAmelCase      )
					snake_case_		     =							{}
					for name, shape in init_vars:
										logger.info(F"Loading TF weight {name} with shape {shape}"      )
										snake_case_		     =							tf.train.load_variable(__UpperCAmelCase, __UpperCAmelCase      )
										snake_case_		     =							array

					# Build TF to PyTorch weights loading map
					snake_case_		     =							_build_tf_to_pytorch_map(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					for name, pointer in tf_to_pt_map.items():
										logger.info(F"Importing {name}"      )
										if name not in tf_weights:
															logger.info(F"{name} not in tf pre-trained weights, skipping"      )
															continue

										snake_case_		     =							tf_weights[name]

										if "depthwise_weights" in name:
															logger.info('''Transposing depthwise'''      )
															snake_case_		     =							np.transpose(__UpperCAmelCase, (2, 3, 0, 1)      )
										elif "weights" in name:
															logger.info('''Transposing'''      )
															if len(pointer.shape      ) == 2:  # copying into linear layer
																				snake_case_		     =							array.squeeze().transpose()
															else:
																				snake_case_		     =							np.transpose(__UpperCAmelCase, (3, 2, 0, 1)      )

										if pointer.shape != array.shape:
															raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"      )

										logger.info(F"Initialize PyTorch weight {name} {array.shape}"      )
										snake_case_		     =							torch.from_numpy(__UpperCAmelCase      )

										tf_weights.pop(__UpperCAmelCase, __UpperCAmelCase      )
										tf_weights.pop(name + '''/RMSProp''', __UpperCAmelCase      )
										tf_weights.pop(name + '''/RMSProp_1''', __UpperCAmelCase      )
										tf_weights.pop(name + '''/ExponentialMovingAverage''', __UpperCAmelCase      )

					logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()      )}"      )
					return model


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   torch.Tensor:

					'''simple docstring'''

					snake_case_     ,snake_case_		     =							features.shape[-2:]
					snake_case_     ,snake_case_		     =							conv_layer.stride
					snake_case_     ,snake_case_		     =							conv_layer.kernel_size

					if in_height % stride_height == 0:
										snake_case_		     =							max(kernel_height - stride_height, 0      )
					else:
										snake_case_		     =							max(kernel_height - (in_height % stride_height), 0      )

					if in_width % stride_width == 0:
										snake_case_		     =							max(kernel_width - stride_width, 0      )
					else:
										snake_case_		     =							max(kernel_width - (in_width % stride_width), 0      )

					snake_case_		     =							pad_along_width // 2
					snake_case_		     =							pad_along_width - pad_left
					snake_case_		     =							pad_along_height // 2
					snake_case_		     =							pad_along_height - pad_top

					snake_case_		     =							(pad_left, pad_right, pad_top, pad_bottom)
					return nn.functional.pad(__UpperCAmelCase, __UpperCAmelCase, '''constant''', 0.0      )






class 		a							(  nn.Module	):


				def __init__(						self       :   Dict			,							lowercase_       :   MobileNetVaConfig			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Optional[int] = 1			,							lowercase_       :   Optional[int] = 1			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[bool] = True			,							lowercase_       :   Optional[bool or str] = True			,							):
									super().__init__()
									snake_case_		     =							config

									if in_channels % groups != 0:
														raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups."   )
									if out_channels % groups != 0:
														raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups."   )

									snake_case_		     =							0 if config.tf_padding else int((kernel_size - 1) / 2   )

									snake_case_		     =							nn.Convad(
									    in_channels=lowercase_			,							out_channels=lowercase_			,							kernel_size=lowercase_			,							stride=lowercase_			,							padding=lowercase_			,							groups=lowercase_			,							bias=lowercase_			,							padding_mode='''zeros'''			,							)

									if use_normalization:
														snake_case_		     =							nn.BatchNormad(
														    num_features=lowercase_			,							eps=config.layer_norm_eps			,							momentum=0.9997			,							affine=lowercase_			,							track_running_stats=lowercase_			,							)
									else:
														snake_case_		     =							None

									if use_activation:
														if isinstance(lowercase_			,							lowercase_   ):
																			snake_case_		     =							ACTaFN[use_activation]
														elif isinstance(config.hidden_act			,							lowercase_   ):
																			snake_case_		     =							ACTaFN[config.hidden_act]
														else:
																			snake_case_		     =							config.hidden_act
									else:
														snake_case_		     =							None


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   torch.Tensor   ):
									if self.config.tf_padding:
														snake_case_		     =							apply_tf_padding(lowercase_			,							self.convolution   )
									snake_case_		     =							self.convolution(lowercase_   )
									if self.normalization is not None:
														snake_case_		     =							self.normalization(lowercase_   )
									if self.activation is not None:
														snake_case_		     =							self.activation(lowercase_   )
									return features







class 		a							(  _lowerCamelCase	):
				snake_case_           =						MobileNetVaConfig
				snake_case_           =						load_tf_weights_in_mobilenet_va
				snake_case_           =						"mobilenet_v1"
				snake_case_           =						"pixel_values"
				snake_case_           =						False


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   Union[nn.Linear, nn.Convad]   ):
									if isinstance(lowercase_			,							(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(lowercase_			,							nn.BatchNormad   ):
														module.bias.data.zero_()
														module.weight.data.fill_(1.0   )







a    :							Tuple										=    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 ([`MobileNetV1Config`]): 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'

a    :							Dict										=    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            [`MobileNetV1ImageProcessor.__call__`] for details.\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 [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."  ,	_lowerCamelCase  ,	)
class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[Any]			,							lowercase_       :   MobileNetVaConfig			,							lowercase_       :   bool = True   ):
									super().__init__(lowercase_   )
									snake_case_		     =							config

									snake_case_		     =							32
									snake_case_		     =							max(int(depth * config.depth_multiplier   )			,							config.min_depth   )

									snake_case_		     =							MobileNetVaConvLayer(
									    lowercase_			,							in_channels=config.num_channels			,							out_channels=lowercase_			,							kernel_size=3			,							stride=2			,							)

									snake_case_		     =							[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

									snake_case_		     =							nn.ModuleList()
									for i in range(13   ):
														snake_case_		     =							out_channels

														if strides[i] == 2 or i == 0:
																			depth *= 2
																			snake_case_		     =							max(int(depth * config.depth_multiplier   )			,							config.min_depth   )

														self.layer.append(
														    MobileNetVaConvLayer(
														        lowercase_			,							in_channels=lowercase_			,							out_channels=lowercase_			,							kernel_size=3			,							stride=strides[i]			,							groups=lowercase_			,							)   )

														self.layer.append(
														    MobileNetVaConvLayer(
														        lowercase_			,							in_channels=lowercase_			,							out_channels=lowercase_			,							kernel_size=1			,							)   )

									snake_case_		     =							nn.AdaptiveAvgPoolad((1, 1)   ) if add_pooling_layer else None

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


				def     A_					(						self       :   Any			,							lowercase_       :   Dict   ):
									raise NotImplementedError


				@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     A_					(						self       :   Union[str, Any]			,							lowercase_       :   Optional[torch.Tensor] = None			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[bool] = None			,							):
									snake_case_		     =							(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									snake_case_		     =							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'''   )

									snake_case_		     =							self.conv_stem(lowercase_   )

									snake_case_		     =							() if output_hidden_states else None

									for i, layer_module in enumerate(self.layer   ):
														snake_case_		     =							layer_module(lowercase_   )

														if output_hidden_states:
																			snake_case_		     =							all_hidden_states + (hidden_states,)

									snake_case_		     =							hidden_states

									if self.pooler is not None:
														snake_case_		     =							torch.flatten(self.pooler(lowercase_   )			,							start_dim=1   )
									else:
														snake_case_		     =							None

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

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







@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "  ,	_lowerCamelCase  ,	)
class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   List[Any]			,							lowercase_       :   MobileNetVaConfig   ):
									super().__init__(lowercase_   )

									snake_case_		     =							config.num_labels
									snake_case_		     =							MobileNetVaModel(lowercase_   )

									snake_case_		     =							self.mobilenet_va.layer[-1].convolution.out_channels

									# Classifier head
									snake_case_		     =							nn.Dropout(config.classifier_dropout_prob			,							inplace=lowercase_   )
									snake_case_		     =							nn.Linear(lowercase_			,							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     A_					(						self       :   Dict			,							lowercase_       :   Optional[torch.Tensor] = None			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[torch.Tensor] = None			,							lowercase_       :   Optional[bool] = None			,							):
									snake_case_		     =							return_dict if return_dict is not None else self.config.use_return_dict

									snake_case_		     =							self.mobilenet_va(lowercase_			,							output_hidden_states=lowercase_			,							return_dict=lowercase_   )

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

									snake_case_		     =							self.classifier(self.dropout(lowercase_   )   )

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

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

									if not return_dict:
														snake_case_		     =							(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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							[False] * len(__UpperCAmelCase      )
					snake_case_		     =							[]
					queue.append(__UpperCAmelCase      )
					snake_case_		     =							True

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

					return visited[t]


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							[-1] * (len(__UpperCAmelCase      ))
					snake_case_		     =							0
					while bfs(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ):
										snake_case_		     =							float('''Inf'''      )
										snake_case_		     =							sink

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

										max_flow += path_flow
										snake_case_		     =							sink

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


a    :							List[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],
]

a						,a    :							int										=    0, 5

print(ford_fulkerson(graph, source, sink))

