

from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


UpperCamelCase__					: Dict								=      logging.get_logger(__name__)

# General docstring
UpperCamelCase__					: Optional[Any]								=      "ResNetConfig"

# Base docstring
UpperCamelCase__					: Union[str, Any]								=      "microsoft/resnet-50"
UpperCamelCase__					: Tuple								=      [1, 2_048, 7, 7]

# Image classification docstring
UpperCamelCase__					: List[Any]								=      "microsoft/resnet-50"
UpperCamelCase__					: str								=      "tiger cat"

UpperCamelCase__					: str								=      [
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



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

  def __init__(   self  ,						_A  ,						_A = 3  ,						_A = 1  ,						_A = "relu"  ,						**_A):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_													=  kernel_size // 2
      SCREAMING_SNAKE_CASE_													=  tf.keras.layers.ConvaD(
          _A  ,						kernel_size=_A  ,						strides=_A  ,						padding='valid'  ,						use_bias=_A  ,						name='convolution')
      # Use same default momentum and epsilon as PyTorch equivalent
      SCREAMING_SNAKE_CASE_													=  tf.keras.layers.BatchNormalization(epsilon=1E-5  ,						momentum=0.9  ,						name='normalization')
      SCREAMING_SNAKE_CASE_													=  ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear')

  def   lowerCAmelCase__       (   self  ,						_A):
      # Pad to match that done in the PyTorch Conv2D model
      SCREAMING_SNAKE_CASE_													=  SCREAMING_SNAKE_CASE_													=  (self.pad_value, self.pad_value)
      SCREAMING_SNAKE_CASE_													=  tf.pad(_A  ,						[(0, 0), height_pad, width_pad, (0, 0)])
      SCREAMING_SNAKE_CASE_													=  self.conv(_A)
      return hidden_state


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False):
      SCREAMING_SNAKE_CASE_													=  self.convolution(_A)
      SCREAMING_SNAKE_CASE_													=  self.normalization(_A  ,						training=_A)
      SCREAMING_SNAKE_CASE_													=  self.activation(_A)
      return hidden_state






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

  def __init__(   self  ,						_A  ,						**_A):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_													=  TFResNetConvLayer(
          config.embedding_size  ,						kernel_size=7  ,						stride=2  ,						activation=config.hidden_act  ,						name='embedder'  ,						)
      SCREAMING_SNAKE_CASE_													=  tf.keras.layers.MaxPoolaD(pool_size=3  ,						strides=2  ,						padding='valid'  ,						name='pooler')
      SCREAMING_SNAKE_CASE_													=  config.num_channels


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  shape_list(_A)
      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.')
      SCREAMING_SNAKE_CASE_													=  pixel_values
      SCREAMING_SNAKE_CASE_													=  self.embedder(_A)
      SCREAMING_SNAKE_CASE_													=  tf.pad(_A  ,						[[0, 0], [1, 1], [1, 1], [0, 0]])
      SCREAMING_SNAKE_CASE_													=  self.pooler(_A)
      return hidden_state






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

  def __init__(   self  ,						_A  ,						_A = 2  ,						**_A):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_													=  tf.keras.layers.ConvaD(
          _A  ,						kernel_size=1  ,						strides=_A  ,						use_bias=_A  ,						name='convolution')
      # Use same default momentum and epsilon as PyTorch equivalent
      SCREAMING_SNAKE_CASE_													=  tf.keras.layers.BatchNormalization(epsilon=1E-5  ,						momentum=0.9  ,						name='normalization')


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False):
      SCREAMING_SNAKE_CASE_													=  x
      SCREAMING_SNAKE_CASE_													=  self.convolution(_A)
      SCREAMING_SNAKE_CASE_													=  self.normalization(_A  ,						training=_A)
      return hidden_state






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

  def __init__(   self  ,						_A  ,						_A  ,						_A = 1  ,						_A = "relu"  ,						**_A):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_													=  in_channels != out_channels or stride != 1
      SCREAMING_SNAKE_CASE_													=  TFResNetConvLayer(_A  ,						stride=_A  ,						name='layer.0')
      SCREAMING_SNAKE_CASE_													=  TFResNetConvLayer(_A  ,						activation=_A  ,						name='layer.1')
      SCREAMING_SNAKE_CASE_													=  (
          TFResNetShortCut(_A  ,						stride=_A  ,						name='shortcut')
          if should_apply_shortcut
          else tf.keras.layers.Activation('linear'  ,						name='shortcut')
      )
      SCREAMING_SNAKE_CASE_													=  ACTaFN[activation]


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False):
      SCREAMING_SNAKE_CASE_													=  hidden_state
      SCREAMING_SNAKE_CASE_													=  self.conva(_A  ,						training=_A)
      SCREAMING_SNAKE_CASE_													=  self.conva(_A  ,						training=_A)
      SCREAMING_SNAKE_CASE_													=  self.shortcut(_A  ,						training=_A)
      hidden_state += residual
      SCREAMING_SNAKE_CASE_													=  self.activation(_A)
      return hidden_state






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

  def __init__(   self  ,						_A  ,						_A  ,						_A = 1  ,						_A = "relu"  ,						_A = 4  ,						**_A  ,						):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_													=  in_channels != out_channels or stride != 1
      SCREAMING_SNAKE_CASE_													=  out_channels // reduction
      SCREAMING_SNAKE_CASE_													=  TFResNetConvLayer(_A  ,						kernel_size=1  ,						name='layer.0')
      SCREAMING_SNAKE_CASE_													=  TFResNetConvLayer(_A  ,						stride=_A  ,						name='layer.1')
      SCREAMING_SNAKE_CASE_													=  TFResNetConvLayer(_A  ,						kernel_size=1  ,						activation=_A  ,						name='layer.2')
      SCREAMING_SNAKE_CASE_													=  (
          TFResNetShortCut(_A  ,						stride=_A  ,						name='shortcut')
          if should_apply_shortcut
          else tf.keras.layers.Activation('linear'  ,						name='shortcut')
      )
      SCREAMING_SNAKE_CASE_													=  ACTaFN[activation]


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False):
      SCREAMING_SNAKE_CASE_													=  hidden_state
      SCREAMING_SNAKE_CASE_													=  self.conva(_A  ,						training=_A)
      SCREAMING_SNAKE_CASE_													=  self.conva(_A  ,						training=_A)
      SCREAMING_SNAKE_CASE_													=  self.conva(_A  ,						training=_A)
      SCREAMING_SNAKE_CASE_													=  self.shortcut(_A  ,						training=_A)
      hidden_state += residual
      SCREAMING_SNAKE_CASE_													=  self.activation(_A)
      return hidden_state






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

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

      SCREAMING_SNAKE_CASE_													=  TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

      SCREAMING_SNAKE_CASE_													=  [layer(_A  ,						_A  ,						stride=_A  ,						activation=config.hidden_act  ,						name='layers.0')]
      layers += [
          layer(_A  ,						_A  ,						activation=config.hidden_act  ,						name=f"""layers.{i + 1}""")
          for i in range(depth - 1)
      ]
      SCREAMING_SNAKE_CASE_													=  layers


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False):
      for layer in self.stage_layers:
          SCREAMING_SNAKE_CASE_													=  layer(_A  ,						training=_A)
      return hidden_state






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

  def __init__(   self  ,						_A  ,						**_A):
      super().__init__(**_A)
      # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
      SCREAMING_SNAKE_CASE_													=  [
          TFResNetStage(
              _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'  ,						)
      ]
      for i, (in_channels, out_channels, depth) in enumerate(
          zip(config.hidden_sizes  ,						config.hidden_sizes[1:]  ,						config.depths[1:])):
          self.stages.append(TFResNetStage(_A  ,						_A  ,						_A  ,						depth=_A  ,						name=f"""stages.{i + 1}"""))


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False  ,						_A = True  ,						_A = False  ,						):
      SCREAMING_SNAKE_CASE_													=  () if output_hidden_states else None

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

          SCREAMING_SNAKE_CASE_													=  stage_module(_A  ,						training=_A)

      if output_hidden_states:
          SCREAMING_SNAKE_CASE_													=  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)






class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					ResNetConfig
  __lowerCAmelCase       :		Optional[Any]  	=					'resnet'
  __lowerCAmelCase       :		Tuple  	=					'pixel_values'

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






UpperCamelCase__					: Dict								=      r"\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\n    Parameters:\n        config ([`ResNetConfig`]): 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"


UpperCamelCase__					: Optional[int]								=      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            [`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 [`~utils.ModelOutput`] instead of a plain tuple.\n"



@keras_serializable
class  __snake_case					(      tf.keras.layers.Layer    ):
  __lowerCAmelCase       :		str  	=					ResNetConfig

  def __init__(   self  ,						_A  ,						**_A):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_													=  config
      SCREAMING_SNAKE_CASE_													=  TFResNetEmbeddings(_A  ,						name='embedder')
      SCREAMING_SNAKE_CASE_													=  TFResNetEncoder(_A  ,						name='encoder')
      SCREAMING_SNAKE_CASE_													=  tf.keras.layers.GlobalAveragePoolingaD(keepdims=_A)


  @unpack_inputs
  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = None  ,						_A = False  ,						):
      SCREAMING_SNAKE_CASE_													=  (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      SCREAMING_SNAKE_CASE_													=  return_dict if return_dict is not None else self.config.use_return_dict

      # TF 2.0 image layers can't use NCHW format when running on CPU.
      # We transpose to NHWC format and then transpose back after the full forward pass.
      # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
      SCREAMING_SNAKE_CASE_													=  tf.transpose(_A  ,						perm=[0, 2, 3, 1])
      SCREAMING_SNAKE_CASE_													=  self.embedder(_A  ,						training=_A)

      SCREAMING_SNAKE_CASE_													=  self.encoder(
          _A  ,						output_hidden_states=_A  ,						return_dict=_A  ,						training=_A)

      SCREAMING_SNAKE_CASE_													=  encoder_outputs[0]

      SCREAMING_SNAKE_CASE_													=  self.pooler(_A)

      # Transpose all the outputs to the NCHW format
      # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
      SCREAMING_SNAKE_CASE_													=  tf.transpose(_A  ,						(0, 3, 1, 2))
      SCREAMING_SNAKE_CASE_													=  tf.transpose(_A  ,						(0, 3, 1, 2))
      SCREAMING_SNAKE_CASE_													=  ()
      for hidden_state in encoder_outputs[1:]:
          SCREAMING_SNAKE_CASE_													=  hidden_states + tuple(tf.transpose(_A  ,						(0, 3, 1, 2)) for h in hidden_state)

      if not return_dict:
          return (last_hidden_state, pooled_output) + hidden_states

      SCREAMING_SNAKE_CASE_													=  hidden_states if output_hidden_states else None

      return TFBaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=_A  ,						pooler_output=_A  ,						hidden_states=_A  ,						)






@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'       ,	lowerCAmelCase__       ,	)
class  __snake_case					(      lowerCAmelCase__    ):

  def __init__(   self  ,						_A  ,						**_A):
      super().__init__(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  TFResNetMainLayer(config=_A  ,						name='resnet')


  @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  ,						)
  @unpack_inputs
  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = None  ,						_A = False  ,						):
      SCREAMING_SNAKE_CASE_													=  (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      SCREAMING_SNAKE_CASE_													=  return_dict if return_dict is not None else self.config.use_return_dict

      SCREAMING_SNAKE_CASE_													=  self.resnet(
          pixel_values=_A  ,						output_hidden_states=_A  ,						return_dict=_A  ,						training=_A  ,						)
      return resnet_outputs






@add_start_docstrings(
    '\n    ResNet 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  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__    ):

  def __init__(   self  ,						_A  ,						**_A):
      super().__init__(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  config.num_labels
      SCREAMING_SNAKE_CASE_													=  TFResNetMainLayer(_A  ,						name='resnet')
      # classification head
      SCREAMING_SNAKE_CASE_													=  (
          tf.keras.layers.Dense(config.num_labels  ,						name='classifier.1')
          if config.num_labels > 0
          else tf.keras.layers.Activation('linear'  ,						name='classifier.1')
      )

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  tf.keras.layers.Flatten()(_A)
      SCREAMING_SNAKE_CASE_													=  self.classifier_layer(_A)
      return logits


  @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  ,						)
  @unpack_inputs
  def   lowerCAmelCase__       (   self  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = False  ,						):
      SCREAMING_SNAKE_CASE_													=  return_dict if return_dict is not None else self.config.use_return_dict

      SCREAMING_SNAKE_CASE_													=  self.resnet(
          _A  ,						output_hidden_states=_A  ,						return_dict=_A  ,						training=_A)

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

      SCREAMING_SNAKE_CASE_													=  self.classifier(_A)

      SCREAMING_SNAKE_CASE_													=  None if labels is None else self.hf_compute_loss(_A  ,						_A)

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

      return TFImageClassifierOutputWithNoAttention(loss=_A  ,						logits=_A  ,						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.
from queue import Queue


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Queue()
    SCREAMING_SNAKE_CASE_													=  [False] * len(_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  [-1] * len(_SCREAMING_SNAKE_CASE    )

    def bfs():
        while not queue.empty():
            SCREAMING_SNAKE_CASE_													=  queue.get()
            SCREAMING_SNAKE_CASE_													=  True

            for neighbour in graph[u]:
                if neighbour == u:
                    return False

                if color[neighbour] == -1:
                    SCREAMING_SNAKE_CASE_													=  1 - color[u]
                    queue.put(_SCREAMING_SNAKE_CASE    )

                elif color[neighbour] == color[u]:
                    return False

        return True

    for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
        if not visited[i]:
            queue.put(_SCREAMING_SNAKE_CASE    )
            SCREAMING_SNAKE_CASE_													=  0
            if bfs() is False:
                return False

    return True


if __name__ == "__main__":
       # Adjacency List of graph
       print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))


