"""simple docstring"""




from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_a      :		Union[str, Any]           =   logging.get_logger(__name__)

# General docstring
_a      :		Any           =   'RegNetConfig'

# Base docstring
_a      :		Tuple           =   'facebook/regnet-y-040'
_a      :		List[Any]           =   [1, 1_088, 7, 7]

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

_a      :		Union[str, Any]           =   [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     a__ = 3				,     a__ = 1				,     a__ = 1				,     a__ = "relu"				,     **a__				,     ):
       super().__init__(**a__  )
       # The padding and conv has been verified in
       # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
       _lowerCAmelCase						:       List[Any]  			=     tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2  )
       _lowerCAmelCase						:       int  			=     tf.keras.layers.ConvaD(
           filters=a__				,     kernel_size=a__				,     strides=a__				,     padding="""VALID"""				,     groups=a__				,     use_bias=a__				,     name="""convolution"""				,     )
       _lowerCAmelCase						:       Optional[Any]  			=     tf.keras.layers.BatchNormalization(epsilon=1e-5				,     momentum=0.9				,     name="""normalization"""  )
       _lowerCAmelCase						:       List[str]  			=     ACTaFN[activation] if activation is not None else tf.identity






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     self.convolution(self.padding(a__  )  )
       _lowerCAmelCase						:       Optional[Any]  			=     self.normalization(a__  )
       _lowerCAmelCase						:       Optional[int]  			=     self.activation(a__  )
       return hidden_state







class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     **a__  ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       str  			=     config.num_channels
       _lowerCAmelCase						:       Optional[Any]  			=     TFRegNetConvLayer(
           out_channels=config.embedding_size				,     kernel_size=3				,     stride=2				,     activation=config.hidden_act				,     name="""embedder"""				,     )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       int  			=     shape_list(a__  )[1]
       if tf.executing_eagerly() and 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."""  )

       # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
       # So change the input format from `NCHW` to `NHWC`.
       # shape = (batch_size, in_height, in_width, in_channels=num_channels)
       _lowerCAmelCase						:       Optional[int]  			=     tf.transpose(a__				,     perm=(0, 2, 3, 1)  )
       _lowerCAmelCase						:       List[Any]  			=     self.embedder(a__  )
       return hidden_state







class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     a__ = 2				,     **a__  ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       str  			=     tf.keras.layers.ConvaD(
           filters=a__				,     kernel_size=1				,     strides=a__				,     use_bias=a__				,     name="""convolution"""  )
       _lowerCAmelCase						:       Optional[int]  			=     tf.keras.layers.BatchNormalization(epsilon=1e-5				,     momentum=0.9				,     name="""normalization"""  )






     def     __A  (		self				,     a__				,     a__ = False  ):
       return self.normalization(self.convolution(a__  )				,     training=a__  )







class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     a__				,     **a__  ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       List[Any]  			=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=a__				,     name="""pooler"""  )
       _lowerCAmelCase						:       int  			=     [
           tf.keras.layers.ConvaD(filters=a__				,     kernel_size=1				,     activation="""relu"""				,     name="""attention.0"""  ),
           tf.keras.layers.ConvaD(filters=a__				,     kernel_size=1				,     activation="""sigmoid"""				,     name="""attention.2"""  ),
       ]






     def     __A  (		self				,     a__  ):
       # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
       _lowerCAmelCase						:       int  			=     self.pooler(a__  )
       for layer_module in self.attention:
         _lowerCAmelCase						:       Dict  			=     layer_module(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_state * pooled
       return hidden_state







class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 1				,     **a__  ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       Any  			=     in_channels != out_channels or stride != 1
       _lowerCAmelCase						:       Tuple  			=     max(1				,     out_channels // config.groups_width  )
       _lowerCAmelCase						:       str  			=     (
           TFRegNetShortCut(a__				,     stride=a__				,     name="""shortcut"""  )
           if should_apply_shortcut
           else tf.keras.layers.Activation("""linear"""				,     name="""shortcut"""  )
       )
       # `self.layers` instead of `self.layer` because that is a reserved argument.
       _lowerCAmelCase						:       str  			=     [
           TFRegNetConvLayer(a__				,     kernel_size=1				,     activation=config.hidden_act				,     name="""layer.0"""  ),
           TFRegNetConvLayer(
               a__				,     stride=a__				,     groups=a__				,     activation=config.hidden_act				,     name="""layer.1"""  ),
           TFRegNetConvLayer(a__				,     kernel_size=1				,     activation=a__				,     name="""layer.2"""  ),
       ]
       _lowerCAmelCase						:       Any  			=     ACTaFN[config.hidden_act]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       int  			=     hidden_state
       for layer_module in self.layers:
         _lowerCAmelCase						:       List[str]  			=     layer_module(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     self.shortcut(a__  )
       hidden_state += residual
       _lowerCAmelCase						:       Union[str, Any]  			=     self.activation(a__  )
       return hidden_state







class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 1				,     **a__  ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       List[str]  			=     in_channels != out_channels or stride != 1
       _lowerCAmelCase						:       List[str]  			=     max(1				,     out_channels // config.groups_width  )
       _lowerCAmelCase						:       Tuple  			=     (
           TFRegNetShortCut(a__				,     stride=a__				,     name="""shortcut"""  )
           if should_apply_shortcut
           else tf.keras.layers.Activation("""linear"""				,     name="""shortcut"""  )
       )
       _lowerCAmelCase						:       int  			=     [
           TFRegNetConvLayer(a__				,     kernel_size=1				,     activation=config.hidden_act				,     name="""layer.0"""  ),
           TFRegNetConvLayer(
               a__				,     stride=a__				,     groups=a__				,     activation=config.hidden_act				,     name="""layer.1"""  ),
           TFRegNetSELayer(a__				,     reduced_channels=int(round(in_channels / 4  )  )				,     name="""layer.2"""  ),
           TFRegNetConvLayer(a__				,     kernel_size=1				,     activation=a__				,     name="""layer.3"""  ),
       ]
       _lowerCAmelCase						:       Dict  			=     ACTaFN[config.hidden_act]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       str  			=     hidden_state
       for layer_module in self.layers:
         _lowerCAmelCase						:       Any  			=     layer_module(a__  )
       _lowerCAmelCase						:       List[str]  			=     self.shortcut(a__  )
       hidden_state += residual
       _lowerCAmelCase						:       int  			=     self.activation(a__  )
       return hidden_state







class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 2				,     a__ = 2				,     **a__  ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       Tuple  			=     TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
       _lowerCAmelCase						:       Any  			=     [
           # downsampling is done in the first layer with stride of 2
           layer(a__				,     a__				,     a__				,     stride=a__				,     name="""layers.0"""  ),
           *[layer(a__				,     a__				,     a__				,     name=F"layers.{i+1}"  ) for i in range(depth - 1  )],
       ]






     def     __A  (		self				,     a__  ):
       for layer_module in self.layers:
         _lowerCAmelCase						:       str  			=     layer_module(a__  )
       return hidden_state







class 						__A							( tf.keras.layers.Layer		):






     def __init__(		self				,     a__				,     **a__  ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       str  			=     []
       # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
       self.stages.append(
           TFRegNetStage(
               a__				,     config.embedding_size				,     config.hidden_sizes[0]				,     stride=2 if config.downsample_in_first_stage else 1				,     depth=config.depths[0]				,     name="""stages.0"""				,     )  )
       _lowerCAmelCase						:       Optional[Any]  			=     zip(config.hidden_sizes				,     config.hidden_sizes[1:]  )
       for i, ((in_channels, out_channels), depth) in enumerate(zip(a__				,     config.depths[1:]  )  ):
         self.stages.append(TFRegNetStage(a__				,     a__				,     a__				,     depth=a__				,     name=F"stages.{i+1}"  )  )






     def     __A  (		self				,     a__				,     a__ = False				,     a__ = True  ):
       _lowerCAmelCase						:       Any  			=     () if output_hidden_states else None

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

         _lowerCAmelCase						:       Optional[Any]  			=     stage_module(a__  )

       if output_hidden_states:
         _lowerCAmelCase						:       int  			=     hidden_states + (hidden_state,)

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

       return TFBaseModelOutputWithNoAttention(last_hidden_state=a__				,     hidden_states=a__  )







@keras_serializable
class 						__A							( tf.keras.layers.Layer		):
     _UpperCamelCase							:			Tuple							=		RegNetConfig






     def __init__(		self				,     a__				,     **a__  ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       Dict  			=     config
       _lowerCAmelCase						:       List[Any]  			=     TFRegNetEmbeddings(a__				,     name="""embedder"""  )
       _lowerCAmelCase						:       str  			=     TFRegNetEncoder(a__				,     name="""encoder"""  )
       _lowerCAmelCase						:       Dict  			=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=a__				,     name="""pooler"""  )






     @unpack_inputs
     def     __A  (		self				,     a__				,     a__ = None				,     a__ = None				,     a__ = False				,     ):
       _lowerCAmelCase						:       Union[str, Any]  			=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowerCAmelCase						:       List[str]  			=     return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:       Union[str, Any]  			=     self.embedder(a__				,     training=a__  )

       _lowerCAmelCase						:       Any  			=     self.encoder(
           a__				,     output_hidden_states=a__				,     return_dict=a__				,     training=a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_outputs[0]
       _lowerCAmelCase						:       str  			=     self.pooler(a__  )

       # Change to NCHW output format have uniformity in the modules
       _lowerCAmelCase						:       Dict  			=     tf.transpose(a__				,     perm=(0, 3, 1, 2)  )
       _lowerCAmelCase						:       str  			=     tf.transpose(a__				,     perm=(0, 3, 1, 2)  )

       # Change the other hidden state outputs to NCHW as well
       if output_hidden_states:
         _lowerCAmelCase						:       List[str]  			=     tuple([tf.transpose(a__				,     perm=(0, 3, 1, 2)  ) for h in encoder_outputs[1]]  )

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

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=a__				,     pooler_output=a__				,     hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states				,     )







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[Any]							=		RegNetConfig
     _UpperCamelCase							:			List[str]							=		"regnet"
     _UpperCamelCase							:			Dict							=		"pixel_values"






     @property
     def     __A  (		self  ):
       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)				,     dtype=tf.floataa  )}







_a      :		List[str]           =   r'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

_a      :		List[str]           =   r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__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 RegNet model outputting raw features without any specific head on top."    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     *a__				,     **a__  ):
       super().__init__(a__				,     *a__				,     **a__  )
       _lowerCAmelCase						:       int  			=     TFRegNetMainLayer(a__				,     name="""regnet"""  )






     @unpack_inputs
     @add_start_docstrings_to_model_forward(a__  )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC				,     output_type=a__				,     config_class=_CONFIG_FOR_DOC				,     modality="""vision"""				,     expected_output=_EXPECTED_OUTPUT_SHAPE				,     )
     def     __A  (		self				,     a__				,     a__ = None				,     a__ = None				,     a__=False				,     ):
       _lowerCAmelCase						:       List[Any]  			=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowerCAmelCase						:       Optional[int]  			=     return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:       Optional[int]  			=     self.regnet(
           pixel_values=a__				,     output_hidden_states=a__				,     return_dict=a__				,     training=a__				,     )
       if not return_dict:
         return (outputs[0],) + outputs[1:]

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=outputs.last_hidden_state				,     pooler_output=outputs.pooler_output				,     hidden_states=outputs.hidden_states				,     )







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






     def __init__(		self				,     a__				,     *a__				,     **a__  ):
       super().__init__(a__				,     *a__				,     **a__  )
       _lowerCAmelCase						:       Tuple  			=     config.num_labels
       _lowerCAmelCase						:       List[str]  			=     TFRegNetMainLayer(a__				,     name="""regnet"""  )
       # classification head
       _lowerCAmelCase						:       Union[str, Any]  			=     [
           tf.keras.layers.Flatten(),
           tf.keras.layers.Dense(config.num_labels				,     name="""classifier.1"""  ) if config.num_labels > 0 else tf.identity,
       ]






     @unpack_inputs
     @add_start_docstrings_to_model_forward(a__  )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT				,     output_type=a__				,     config_class=_CONFIG_FOR_DOC				,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,     )
     def     __A  (		self				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__=False				,     ):
       _lowerCAmelCase						:       Any  			=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowerCAmelCase						:       Union[str, Any]  			=     return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:       str  			=     self.regnet(
           a__				,     output_hidden_states=a__				,     return_dict=a__				,     training=a__  )

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

       _lowerCAmelCase						:       Any  			=     self.classifier[0](a__  )
       _lowerCAmelCase						:       Optional[int]  			=     self.classifier[1](a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     None if labels is None else self.hf_compute_loss(labels=a__				,     logits=a__  )

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

       return TFSequenceClassifierOutput(loss=a__				,     logits=a__				,     hidden_states=outputs.hidden_states  )





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




