



'''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


lowercase_									=    logging.get_logger(__name__)

# General docstring
lowercase_									=    "RegNetConfig"

# Base docstring
lowercase_									=    "facebook/regnet-y-040"
lowercase_									=    [1, 1_088, 7, 7]

# Image classification docstring
lowercase_									=    "facebook/regnet-y-040"
lowercase_									=    "tabby, tabby cat"

lowercase_									=    [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






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



 '''simple docstring'''







 def __init__(self					,  A					,  A = 3					,  A = 1					,  A = 1					,  A = "relu"					,  **A					,  )       ->	List[Any]:


      """simple docstring"""



      super().__init__(**A			)
      # The padding and conv has been verified in
      # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
      _a           =    tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2			)
      _a           =    tf.keras.layers.ConvaD(
          filters=A					,  kernel_size=A					,  strides=A					,  padding='''VALID'''					,  groups=A					,  use_bias=A					,  name='''convolution'''					,  )
      _a           =    tf.keras.layers.BatchNormalization(epsilon=1E-5					,  momentum=0.9					,  name='''normalization'''			)
      _a           =    ACTaFN[activation] if activation is not None else tf.identity



 def    a__    (self					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.convolution(self.padding(A			)			)
      _a           =    self.normalization(A			)
      _a           =    self.activation(A			)
      return hidden_state






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



 '''simple docstring'''







 def __init__(self					,  A					,  **A			)       ->	Tuple:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    config.num_channels
      _a           =    TFRegNetConvLayer(
          out_channels=config.embedding_size					,  kernel_size=3					,  stride=2					,  activation=config.hidden_act					,  name='''embedder'''					,  )



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    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)
      _a           =    tf.transpose(A					,  perm=(0, 2, 3, 1)			)
      _a           =    self.embedder(A			)
      return hidden_state






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



 '''simple docstring'''







 def __init__(self					,  A					,  A = 2					,  **A			)       ->	Optional[Any]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    tf.keras.layers.ConvaD(
          filters=A					,  kernel_size=1					,  strides=A					,  use_bias=A					,  name='''convolution'''			)
      _a           =    tf.keras.layers.BatchNormalization(epsilon=1E-5					,  momentum=0.9					,  name='''normalization'''			)



 def    a__    (self					,  A					,  A = False			)       ->	tf.Tensor:


      """simple docstring"""



      return self.normalization(self.convolution(A			)					,  training=A			)






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



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  **A			)       ->	List[str]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    tf.keras.layers.GlobalAveragePoolingaD(keepdims=A					,  name='''pooler'''			)
      _a           =    [
          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			)       ->	str:


      """simple docstring"""



      _a           =    self.pooler(A			)
      for layer_module in self.attention:
           _a           =    layer_module(A			)
      _a           =    hidden_state * pooled
      return hidden_state






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



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A = 1					,  **A			)       ->	Any:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    in_channels != out_channels or stride != 1
      _a           =    max(1					,  out_channels // config.groups_width			)
      _a           =    (
          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.
      _a           =    [
          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'''			),
      ]
      _a           =    ACTaFN[config.hidden_act]



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    hidden_state
      for layer_module in self.layers:
           _a           =    layer_module(A			)
      _a           =    self.shortcut(A			)
      hidden_state += residual
      _a           =    self.activation(A			)
      return hidden_state






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



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A = 1					,  **A			)       ->	Optional[int]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    in_channels != out_channels or stride != 1
      _a           =    max(1					,  out_channels // config.groups_width			)
      _a           =    (
          TFRegNetShortCut(A					,  stride=A					,  name='''shortcut'''			)
          if should_apply_shortcut
          else tf.keras.layers.Activation('''linear'''					,  name='''shortcut'''			)
      )
      _a           =    [
          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'''			),
      ]
      _a           =    ACTaFN[config.hidden_act]



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    hidden_state
      for layer_module in self.layers:
           _a           =    layer_module(A			)
      _a           =    self.shortcut(A			)
      hidden_state += residual
      _a           =    self.activation(A			)
      return hidden_state






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



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A = 2					,  A = 2					,  **A			)       ->	str:


      """simple docstring"""



      super().__init__(**A			)

      _a           =    TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
      _a           =    [
          # 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			)       ->	str:


      """simple docstring"""



      for layer_module in self.layers:
           _a           =    layer_module(A			)
      return hidden_state






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



 '''simple docstring'''







 def __init__(self					,  A					,  **A			)       ->	Optional[Any]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    []
      # 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'''					,  )			)
      _a           =    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			)       ->	TFBaseModelOutputWithNoAttention:


      """simple docstring"""



      _a           =    () if output_hidden_states else None

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

           _a           =    stage_module(A			)

      if output_hidden_states:
           _a           =    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		):



 '''simple docstring'''







 __lowerCamelCase       :			Any		=			RegNetConfig



 def __init__(self					,  A					,  **A			)       ->	str:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    config
      _a           =    TFRegNetEmbeddings(A					,  name='''embedder'''			)
      _a           =    TFRegNetEncoder(A					,  name='''encoder'''			)
      _a           =    tf.keras.layers.GlobalAveragePoolingaD(keepdims=A					,  name='''pooler'''			)



 @unpack_inputs
 def    a__    (self					,  A					,  A = None					,  A = None					,  A = False					,  )       ->	TFBaseModelOutputWithPoolingAndNoAttention:


      """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

      _a           =    self.embedder(A					,  training=A			)

      _a           =    self.encoder(
          A					,  output_hidden_states=A					,  return_dict=A					,  training=A			)

      _a           =    encoder_outputs[0]
      _a           =    self.pooler(A			)

      # Change to NCHW output format have uniformity in the modules
      _a           =    tf.transpose(A					,  perm=(0, 3, 1, 2)			)
      _a           =    tf.transpose(A					,  perm=(0, 3, 1, 2)			)

      # Change the other hidden state outputs to NCHW as well
      if output_hidden_states:
           _a           =    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     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			RegNetConfig
 __lowerCamelCase       :			List[str]		=			'regnet'
 __lowerCamelCase       :			Optional[int]		=			'pixel_values'



 @property
 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



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





lowercase_									=    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"

lowercase_									=    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.'  ,  A  ,  )
class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A					,  *A					,  **A			)       ->	List[str]:


      """simple docstring"""



      super().__init__(A					,  *A					,  **A			)
      _a           =    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					,  )       ->	Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:


      """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

      _a           =    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    '  ,  A  ,  )
class __A     (    A  ,  A		):



 '''simple docstring'''







 def __init__(self					,  A					,  *A					,  **A			)       ->	int:


      """simple docstring"""



      super().__init__(A					,  *A					,  **A			)
      _a           =    config.num_labels
      _a           =    TFRegNetMainLayer(A					,  name='''regnet'''			)
      # classification head
      _a           =    [
          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					,  )       ->	Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:


      """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

      _a           =    self.regnet(
          A					,  output_hidden_states=A					,  return_dict=A					,  training=A			)

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

      _a           =    self.classifier[0](A			)
      _a           =    self.classifier[1](A			)

      _a           =    None if labels is None else self.hf_compute_loss(labels=A					,  logits=A			)

      if not return_dict:
           _a           =    (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'''





from __future__ import annotations




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if len(__A) == 0:
          return False
     _a           =    len(__A) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint]    ,      __A)
     else:
          return binary_search(a_list[midpoint + 1 :]    ,      __A)


if __name__ == "__main__":
  lowercase_									=    input("Enter numbers separated by comma:\n").strip()
  lowercase_									=    [int(item.strip()) for item in user_input.split(",")]
  lowercase_									=    int(input("Enter the number to be found in the list:\n").strip())
  lowercase_									=    "" if binary_search(sequence, target) else "not "
  print(F"""{target} was {not_str}found in {sequence}""")



