
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


__lowerCAmelCase    :	List[Any]						=						logging.get_logger(__name__)


# General docstring
__lowerCAmelCase    :	Union[str, Any]						=						"MobileNetV1Config"

# Base docstring
__lowerCAmelCase    :	str						=						"google/mobilenet_v1_1.0_224"
__lowerCAmelCase    :	str						=						[1, 1_024, 7, 7]

# Image classification docstring
__lowerCAmelCase    :	List[Any]						=						"google/mobilenet_v1_1.0_224"
__lowerCAmelCase    :	Dict						=						"tabby, tabby cat"


__lowerCAmelCase    :	List[Any]						=						[
    "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 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=None		)       ->  Optional[Any]:
    __lowercase						:	Optional[int]					=				{}

    if isinstance(_snake_case		,    _snake_case		):
        __lowercase						:	Union[str, Any]					=				model.mobilenet_va
    else:
        __lowercase						:	List[Any]					=				model

    __lowercase						:	Optional[int]					=				'''MobilenetV1/Conv2d_0/'''
    __lowercase						:	Optional[Any]					=				backbone.conv_stem.convolution.weight
    __lowercase						:	Optional[Any]					=				backbone.conv_stem.normalization.bias
    __lowercase						:	Union[str, Any]					=				backbone.conv_stem.normalization.weight
    __lowercase						:	Tuple					=				backbone.conv_stem.normalization.running_mean
    __lowercase						:	int					=				backbone.conv_stem.normalization.running_var

    for i in range(13		):
        __lowercase						:	List[str]					=				i + 1
        __lowercase						:	Optional[int]					=				i * 2

        __lowercase						:	Optional[int]					=				backbone.layer[pt_index]
        __lowercase						:	Optional[Any]					=				F'MobilenetV1/Conv2d_{tf_index}_depthwise/'
        __lowercase						:	List[str]					=				pointer.convolution.weight
        __lowercase						:	List[str]					=				pointer.normalization.bias
        __lowercase						:	List[str]					=				pointer.normalization.weight
        __lowercase						:	Optional[Any]					=				pointer.normalization.running_mean
        __lowercase						:	Optional[int]					=				pointer.normalization.running_var

        __lowercase						:	Optional[int]					=				backbone.layer[pt_index + 1]
        __lowercase						:	List[Any]					=				F'MobilenetV1/Conv2d_{tf_index}_pointwise/'
        __lowercase						:	str					=				pointer.convolution.weight
        __lowercase						:	Optional[Any]					=				pointer.normalization.bias
        __lowercase						:	List[Any]					=				pointer.normalization.weight
        __lowercase						:	int					=				pointer.normalization.running_mean
        __lowercase						:	List[Any]					=				pointer.normalization.running_var

    if isinstance(_snake_case		,    _snake_case		):
        __lowercase						:	str					=				'''MobilenetV1/Logits/Conv2d_1c_1x1/'''
        __lowercase						:	Any					=				model.classifier.weight
        __lowercase						:	Union[str, Any]					=				model.classifier.bias

    return tf_to_pt_map


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Optional[Any]:






    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
    __lowercase						:	int					=				tf.train.list_variables(_snake_case		)
    __lowercase						:	Dict					=				{}
    for name, shape in init_vars:
        logger.info(F'Loading TF weight {name} with shape {shape}'		)
        __lowercase						:	Union[str, Any]					=				tf.train.load_variable(_snake_case		,    _snake_case		)
        __lowercase						:	Optional[int]					=				array

    # Build TF to PyTorch weights loading map
    __lowercase						:	Dict					=				_build_tf_to_pytorch_map(_snake_case		,    _snake_case		,    _snake_case		)

    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

        __lowercase						:	Union[str, Any]					=				tf_weights[name]

        if "depthwise_weights" in name:
            logger.info('''Transposing depthwise'''		)
            __lowercase						:	str					=				np.transpose(_snake_case		,    (2, 3, 0, 1)		)
        elif "weights" in name:
            logger.info('''Transposing'''		)
            if len(pointer.shape		) == 2:  # copying into linear layer
                __lowercase						:	List[str]					=				array.squeeze().transpose()
            else:
                __lowercase						:	Any					=				np.transpose(_snake_case		,    (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}'		)
        __lowercase						:	List[Any]					=				torch.from_numpy(_snake_case		)

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

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


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  int:
    __lowercase			,					__lowercase						:	List[Any]					=				features.shape[-2:]
    __lowercase			,					__lowercase						:	List[str]					=				conv_layer.stride
    __lowercase			,					__lowercase						:	int					=				conv_layer.kernel_size

    if in_height % stride_height == 0:
        __lowercase						:	int					=				max(kernel_height - stride_height		,    0		)
    else:
        __lowercase						:	Union[str, Any]					=				max(kernel_height - (in_height % stride_height)		,    0		)

    if in_width % stride_width == 0:
        __lowercase						:	List[Any]					=				max(kernel_width - stride_width		,    0		)
    else:
        __lowercase						:	int					=				max(kernel_width - (in_width % stride_width)		,    0		)

    __lowercase						:	Optional[int]					=				pad_along_width // 2
    __lowercase						:	List[Any]					=				pad_along_width - pad_left
    __lowercase						:	Optional[Any]					=				pad_along_height // 2
    __lowercase						:	List[Any]					=				pad_along_height - pad_top

    __lowercase						:	Any					=				(pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(_snake_case		,    _snake_case		,    '''constant'''		,    0.0		)


class   __lowerCAmelCase		(		nn.Module					):



      """simple docstring"""







      def __init__(					self						:				Dict ,				_snake_case						:				Optional[int] ,				_snake_case						:				int ,				_snake_case						:				Any ,				_snake_case						:				str ,				_snake_case						:				Optional[Any] = 1 ,				_snake_case						:				int = 1 ,				_snake_case						:				List[Any] = False ,				_snake_case						:				Optional[Any] = True ,				_snake_case						:				List[Any] = True ,				):
          super().__init__()
          __lowercase						:	int					=				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.' )

          __lowercase						:	Dict					=				0 if config.tf_padding else int((kernel_size - 1) / 2 )

          __lowercase						:	Any					=				nn.Convad(
              in_channels=__snake_case ,				out_channels=__snake_case ,				kernel_size=__snake_case ,				stride=__snake_case ,				padding=__snake_case ,				groups=__snake_case ,				bias=__snake_case ,				padding_mode='''zeros''' ,				)

          if use_normalization:
              __lowercase						:	Any					=				nn.BatchNormad(
                  num_features=__snake_case ,				eps=config.layer_norm_eps ,				momentum=0.99_97 ,				affine=__snake_case ,				track_running_stats=__snake_case ,				)
          else:
              __lowercase						:	Tuple					=				None

          if use_activation:
              if isinstance(__snake_case ,				__snake_case ):
                  __lowercase						:	Optional[int]					=				ACTaFN[use_activation]
              elif isinstance(config.hidden_act ,				__snake_case ):
                  __lowercase						:	Dict					=				ACTaFN[config.hidden_act]
              else:
                  __lowercase						:	List[Any]					=				config.hidden_act
          else:
              __lowercase						:	List[str]					=				None




      def 						snake_case_	(					self						:				List[Any] ,				_snake_case						:				Dict ):
          if self.config.tf_padding:
              __lowercase						:	Dict					=				apply_tf_padding(__snake_case ,				self.convolution )
          __lowercase						:	str					=				self.convolution(__snake_case )
          if self.normalization is not None:
              __lowercase						:	Tuple					=				self.normalization(__snake_case )
          if self.activation is not None:
              __lowercase						:	List[str]					=				self.activation(__snake_case )
          return features






class   __lowerCAmelCase		(		lowerCAmelCase_					):



      """simple docstring"""







      A__		:	Union[str, Any]         =			MobileNetVaConfig
      A__		:	Dict         =			load_tf_weights_in_mobilenet_va
      A__		:	List[str]         =			'''mobilenet_v1'''
      A__		:	Union[str, Any]         =			'''pixel_values'''
      A__		:	Tuple         =			False




      def 						snake_case_	(					self						:				str ,				_snake_case						:				List[Any] ):
          if isinstance(__snake_case ,				(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(__snake_case ,				nn.BatchNormad ):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0 )






__lowerCAmelCase    :	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"

__lowerCAmelCase    :	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   __lowerCAmelCase		(		lowerCAmelCase_					):



      """simple docstring"""







      def __init__(					self						:				Optional[int] ,				_snake_case						:				Optional[Any] ,				_snake_case						:				List[Any] = True ):
          super().__init__(__snake_case )
          __lowercase						:	Optional[Any]					=				config

          __lowercase						:	Union[str, Any]					=				32
          __lowercase						:	Optional[int]					=				max(int(depth * config.depth_multiplier ) ,				config.min_depth )

          __lowercase						:	Any					=				MobileNetVaConvLayer(
              __snake_case ,				in_channels=config.num_channels ,				out_channels=__snake_case ,				kernel_size=3 ,				stride=2 ,				)

          __lowercase						:	Tuple					=				[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

          __lowercase						:	int					=				nn.ModuleList()
          for i in range(13 ):
              __lowercase						:	Dict					=				out_channels

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

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

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

          __lowercase						:	Any					=				nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None

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




      def 						snake_case_	(					self						:				Optional[int] ,				_snake_case						:				List[Any] ):
          raise NotImplementedError




      @add_start_docstrings_to_model_forward(__snake_case )
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC ,				output_type=__snake_case ,				config_class=_CONFIG_FOR_DOC ,				modality='''vision''' ,				expected_output=_EXPECTED_OUTPUT_SHAPE ,				)
      def 						snake_case_	(					self						:				Optional[Any] ,				_snake_case						:				Optional[int] = None ,				_snake_case						:				List[str] = None ,				_snake_case						:				Any = None ,				):
          __lowercase						:	Union[str, Any]					=				(
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          __lowercase						:	List[Any]					=				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''' )

          __lowercase						:	Optional[Any]					=				self.conv_stem(__snake_case )

          __lowercase						:	List[str]					=				() if output_hidden_states else None

          for i, layer_module in enumerate(self.layer ):
              __lowercase						:	Any					=				layer_module(__snake_case )

              if output_hidden_states:
                  __lowercase						:	Any					=				all_hidden_states + (hidden_states,)

          __lowercase						:	Optional[Any]					=				hidden_states

          if self.pooler is not None:
              __lowercase						:	Union[str, Any]					=				torch.flatten(self.pooler(__snake_case ) ,				start_dim=1 )
          else:
              __lowercase						:	Optional[int]					=				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=__snake_case ,				pooler_output=__snake_case ,				hidden_states=__snake_case ,				)






@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   __lowerCAmelCase		(		lowerCAmelCase_					):



      """simple docstring"""







      def __init__(					self						:				Optional[Any] ,				_snake_case						:				int ):
          super().__init__(__snake_case )

          __lowercase						:	Tuple					=				config.num_labels
          __lowercase						:	Any					=				MobileNetVaModel(__snake_case )

          __lowercase						:	Optional[Any]					=				self.mobilenet_va.layer[-1].convolution.out_channels

          # Classifier head
          __lowercase						:	List[str]					=				nn.Dropout(config.classifier_dropout_prob ,				inplace=__snake_case )
          __lowercase						:	Dict					=				nn.Linear(__snake_case ,				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(__snake_case )
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT ,				output_type=__snake_case ,				config_class=_CONFIG_FOR_DOC ,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,				)
      def 						snake_case_	(					self						:				Dict ,				_snake_case						:				List[Any] = None ,				_snake_case						:				Optional[int] = None ,				_snake_case						:				Union[str, Any] = None ,				_snake_case						:				Tuple = None ,				):
          __lowercase						:	List[Any]					=				return_dict if return_dict is not None else self.config.use_return_dict

          __lowercase						:	List[str]					=				self.mobilenet_va(__snake_case ,				output_hidden_states=__snake_case ,				return_dict=__snake_case )

          __lowercase						:	Any					=				outputs.pooler_output if return_dict else outputs[1]

          __lowercase						:	Optional[int]					=				self.classifier(self.dropout(__snake_case ) )

          __lowercase						:	List[str]					=				None
          if labels is not None:
              if self.config.problem_type is None:
                  if self.num_labels == 1:
                      __lowercase						:	int					=				'''regression'''
                  elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                      __lowercase						:	Any					=				'''single_label_classification'''
                  else:
                      __lowercase						:	Optional[Any]					=				'''multi_label_classification'''

              if self.config.problem_type == "regression":
                  __lowercase						:	List[Any]					=				MSELoss()
                  if self.num_labels == 1:
                      __lowercase						:	Any					=				loss_fct(logits.squeeze() ,				labels.squeeze() )
                  else:
                      __lowercase						:	Dict					=				loss_fct(__snake_case ,				__snake_case )
              elif self.config.problem_type == "single_label_classification":
                  __lowercase						:	List[str]					=				CrossEntropyLoss()
                  __lowercase						:	List[str]					=				loss_fct(logits.view(-1 ,				self.num_labels ) ,				labels.view(-1 ) )
              elif self.config.problem_type == "multi_label_classification":
                  __lowercase						:	str					=				BCEWithLogitsLoss()
                  __lowercase						:	Dict					=				loss_fct(__snake_case ,				__snake_case )

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

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





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






