

"""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


SCREAMING_SNAKE_CASE_					:	Dict							  =						logging.get_logger(__name__)


# General docstring
SCREAMING_SNAKE_CASE_					:	Tuple							  =						'MobileNetV1Config'

# Base docstring
SCREAMING_SNAKE_CASE_					:	str							  =						'google/mobilenet_v1_1.0_224'
SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						[1, 1_0_2_4, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_					:	str							  =						'google/mobilenet_v1_1.0_224'
SCREAMING_SNAKE_CASE_					:	Dict							  =						'tabby, tabby cat'


SCREAMING_SNAKE_CASE_					:	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  _snake_case     (    UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					List[str]=None			):
     A__										=  {}

     if isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  model.mobilenet_va
     else:
          A__										=  model

     A__										=  """MobilenetV1/Conv2d_0/"""
     A__										=  backbone.conv_stem.convolution.weight
     A__										=  backbone.conv_stem.normalization.bias
     A__										=  backbone.conv_stem.normalization.weight
     A__										=  backbone.conv_stem.normalization.running_mean
     A__										=  backbone.conv_stem.normalization.running_var

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

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

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

     if isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__										=  """MobilenetV1/Logits/Conv2d_1c_1x1/"""
          A__										=  model.classifier.weight
          A__										=  model.classifier.bias

     return tf_to_pt_map





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					List[str]			):
     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
     A__										=  tf.train.list_variables(UpperCAmelCase_			)
     A__										=  {}
     for name, shape in init_vars:
          logger.info(F"""Loading TF weight {name} with shape {shape}"""			)
          A__										=  tf.train.load_variable(UpperCAmelCase_					,   UpperCAmelCase_			)
          A__										=  array

     # Build TF to PyTorch weights loading map
     A__										=  _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

          A__										=  tf_weights[name]

          if "depthwise_weights" in name:
               logger.info("""Transposing depthwise"""			)
               A__										=  np.transpose(UpperCAmelCase_					,   (2, 3, 0, 1)			)
          elif "weights" in name:
               logger.info("""Transposing"""			)
               if len(pointer.shape			) == 2:  # copying into linear layer
                    A__										=  array.squeeze().transpose()
               else:
                    A__										=  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}"""			)
          A__										=  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  _snake_case     (    UpperCAmelCase_  :					torch.Tensor					,   UpperCAmelCase_  :					nn.Convad			):
     A__			,				A__										=  features.shape[-2:]
     A__			,				A__										=  conv_layer.stride
     A__			,				A__										=  conv_layer.kernel_size

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

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

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

     A__										=  (pad_left, pad_right, pad_top, pad_bottom)
     return nn.functional.pad(UpperCAmelCase_					,   UpperCAmelCase_					,   """constant"""					,   0.0			)



class 					a						(       nn.Module				):



    """simple docstring"""



    def __init__(     self: List[Any]    ,						UpperCamelCase: MobileNetVaConfig    ,						UpperCamelCase: int    ,						UpperCamelCase: int    ,						UpperCamelCase: int    ,						UpperCamelCase: Optional[int] = 1    ,						UpperCamelCase: Optional[int] = 1    ,						UpperCamelCase: bool = False    ,						UpperCamelCase: Optional[bool] = True    ,						UpperCamelCase: Optional[bool or str] = True    ,						):






         """simple docstring"""


         super().__init__()
         A__										=  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."""       )

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

         A__										=  nn.Convad(
             in_channels=UpperCamelCase    ,						out_channels=UpperCamelCase    ,						kernel_size=UpperCamelCase    ,						stride=UpperCamelCase    ,						padding=UpperCamelCase    ,						groups=UpperCamelCase    ,						bias=UpperCamelCase    ,						padding_mode="""zeros"""    ,						)

         if use_normalization:
              A__										=  nn.BatchNormad(
                  num_features=UpperCamelCase    ,						eps=config.layer_norm_eps    ,						momentum=0.9_997    ,						affine=UpperCamelCase    ,						track_running_stats=UpperCamelCase    ,						)
         else:
              A__										=  None

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





    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: torch.Tensor       ):






         """simple docstring"""


         if self.config.tf_padding:
              A__										=  apply_tf_padding(UpperCamelCase    ,						self.convolution       )
         A__										=  self.convolution(UpperCamelCase       )
         if self.normalization is not None:
              A__										=  self.normalization(UpperCamelCase       )
         if self.activation is not None:
              A__										=  self.activation(UpperCamelCase       )
         return features



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MobileNetVaConfig
    UpperCAmelCase						 =					load_tf_weights_in_mobilenet_va
    UpperCAmelCase						 =					"mobilenet_v1"
    UpperCAmelCase						 =					"pixel_values"
    UpperCAmelCase						 =					False

    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: Union[nn.Linear, nn.Convad]       ):






         """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.BatchNormad       ):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0       )

SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						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'

SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						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				):



    """simple docstring"""



    def __init__(     self: Tuple    ,						UpperCamelCase: MobileNetVaConfig    ,						UpperCamelCase: bool = True       ):






         """simple docstring"""


         super().__init__(UpperCamelCase       )
         A__										=  config

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

         A__										=  MobileNetVaConvLayer(
             UpperCamelCase    ,						in_channels=config.num_channels    ,						out_channels=UpperCamelCase    ,						kernel_size=3    ,						stride=2    ,						)

         A__										=  [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

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

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

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

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

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

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

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         raise NotImplementedError





    @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 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Optional[torch.Tensor] = None    ,						UpperCamelCase: Optional[bool] = None    ,						UpperCamelCase: Optional[bool] = None    ,						):






         """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.conv_stem(UpperCamelCase       )

         A__										=  () if output_hidden_states else None

         for i, layer_module in enumerate(self.layer       ):
              A__										=  layer_module(UpperCamelCase       )

              if output_hidden_states:
                   A__										=  all_hidden_states + (hidden_states,)

         A__										=  hidden_states

         if self.pooler is not None:
              A__										=  torch.flatten(self.pooler(UpperCamelCase       )    ,						start_dim=1       )
         else:
              A__										=  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=UpperCamelCase    ,						pooler_output=UpperCamelCase    ,						hidden_states=UpperCamelCase    ,						)



@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				):



    """simple docstring"""



    def __init__(     self: List[str]    ,						UpperCamelCase: MobileNetVaConfig       ):






         """simple docstring"""


         super().__init__(UpperCamelCase       )

         A__										=  config.num_labels
         A__										=  MobileNetVaModel(UpperCamelCase       )

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

         # Classifier head
         A__										=  nn.Dropout(config.classifier_dropout_prob    ,						inplace=UpperCamelCase       )
         A__										=  nn.Linear(UpperCamelCase    ,						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 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: Optional[torch.Tensor] = None    ,						UpperCamelCase: Optional[bool] = None    ,						UpperCamelCase: Optional[torch.Tensor] = None    ,						UpperCamelCase: Optional[bool] = None    ,						):






         """simple docstring"""


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

         A__										=  self.mobilenet_va(UpperCamelCase    ,						output_hidden_states=UpperCamelCase    ,						return_dict=UpperCamelCase       )

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

         A__										=  self.classifier(self.dropout(UpperCamelCase       )       )

         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"""



from collections import defaultdict





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  1
     A__										=  True
     for v in tree[start]:
          if v not in visited:
               ret += dfs(UpperCAmelCase_			)
     if ret % 2 == 0:
          cuts.append(UpperCAmelCase_			)
     return ret





def  _snake_case     (    ):
     dfs(1			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_					:	Any							  =						1_0, 9
 SCREAMING_SNAKE_CASE_					:	Any							  =						defaultdict(list)
 SCREAMING_SNAKE_CASE_					:	dict[int, bool]							  =						{}
 SCREAMING_SNAKE_CASE_					:	list[int]							  =						[]
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						0
 SCREAMING_SNAKE_CASE_					:	Any							  =						[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
 for u, v in edges:
  tree[u].append(v)
  tree[v].append(u)
 even_tree()
 print(len(cuts) - 1)






