






'''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


SCREAMING_SNAKE_CASE								=	logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE								=	"RegNetConfig"

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

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

SCREAMING_SNAKE_CASE								=	[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase_					(    tf.keras.layers.Layer				):




      """simple docstring"""




      def __init__( self			:		Union[str, Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int = 3     ,      UpperCAmelCase			:		int = 1     ,      UpperCAmelCase			:		int = 1     ,      UpperCAmelCase			:		Optional[str] = "relu"     ,      **UpperCAmelCase			:		Dict     ,      )  ->  Dict:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		int		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.convolution(self.padding(lowerCAmelCase_		)		)
       lowercase					:   Tuple				  =self.normalization(lowerCAmelCase_		)
       lowercase					:   List[Any]				  =self.activation(lowerCAmelCase_		)
       return hidden_state





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




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		RegNetConfig     ,      **UpperCAmelCase			:		Dict		)  ->  str:


       '''simple docstring'''

       super().__init__(**lowerCAmelCase_		)
       lowercase					:   Optional[Any]				  =config.num_channels
       lowercase					:   Any				  =TFRegNetConvLayer(
           out_channels=config.embedding_size     ,      kernel_size=3     ,      stride=2     ,      activation=config.hidden_act     ,      name='''embedder'''     ,      )






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Tuple		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =shape_list(lowerCAmelCase_		)[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)
       lowercase					:   Dict				  =tf.transpose(lowerCAmelCase_     ,      perm=(0, 2, 3, 1)		)
       lowercase					:   Union[str, Any]				  =self.embedder(lowerCAmelCase_		)
       return hidden_state





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




      """simple docstring"""




      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int = 2     ,      **UpperCAmelCase			:		List[str]		)  ->  List[Any]:


       '''simple docstring'''

       super().__init__(**lowerCAmelCase_		)
       lowercase					:   int				  =tf.keras.layers.ConvaD(
           filters=lowerCAmelCase_     ,      kernel_size=1     ,      strides=lowerCAmelCase_     ,      use_bias=lowerCAmelCase_     ,      name='''convolution'''		)
       lowercase					:   Any				  =tf.keras.layers.BatchNormalization(epsilon=1e-5     ,      momentum=0.9     ,      name='''normalization'''		)






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		tf.Tensor     ,      UpperCAmelCase			:		bool = False		)  ->  int:


       '''simple docstring'''

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





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




      """simple docstring"""




      def __init__( self			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      **UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       super().__init__(**lowerCAmelCase_		)
       lowercase					:   int				  =tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCAmelCase_     ,      name='''pooler'''		)
       lowercase					:   Dict				  =[
           tf.keras.layers.ConvaD(filters=lowerCAmelCase_     ,      kernel_size=1     ,      activation='''relu'''     ,      name='''attention.0'''		),
           tf.keras.layers.ConvaD(filters=lowerCAmelCase_     ,      kernel_size=1     ,      activation='''sigmoid'''     ,      name='''attention.2'''		),
       ]






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Any		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[str]				  =self.pooler(lowerCAmelCase_		)
       for layer_module in self.attention:
        lowercase					:   List[Any]				  =layer_module(lowerCAmelCase_		)
       lowercase					:   Dict				  =hidden_state * pooled
       return hidden_state





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




      """simple docstring"""




      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		RegNetConfig     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int = 1     ,      **UpperCAmelCase			:		Dict		)  ->  int:


       '''simple docstring'''

       super().__init__(**lowerCAmelCase_		)
       lowercase					:   Optional[int]				  =in_channels != out_channels or stride != 1
       lowercase					:   int				  =max(1     ,      out_channels // config.groups_width		)
       lowercase					:   Optional[int]				  =(
           TFRegNetShortCut(lowerCAmelCase_     ,      stride=lowerCAmelCase_     ,      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.
       lowercase					:   Tuple				  =[
           TFRegNetConvLayer(lowerCAmelCase_     ,      kernel_size=1     ,      activation=config.hidden_act     ,      name='''layer.0'''		),
           TFRegNetConvLayer(
               lowerCAmelCase_     ,      stride=lowerCAmelCase_     ,      groups=lowerCAmelCase_     ,      activation=config.hidden_act     ,      name='''layer.1'''		),
           TFRegNetConvLayer(lowerCAmelCase_     ,      kernel_size=1     ,      activation=lowerCAmelCase_     ,      name='''layer.2'''		),
       ]
       lowercase					:   Any				  =ACTaFN[config.hidden_act]






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Optional[int]		)  ->  str:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =hidden_state
       for layer_module in self.layers:
        lowercase					:   Tuple				  =layer_module(lowerCAmelCase_		)
       lowercase					:   Optional[Any]				  =self.shortcut(lowerCAmelCase_		)
       hidden_state += residual
       lowercase					:   Optional[Any]				  =self.activation(lowerCAmelCase_		)
       return hidden_state





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




      """simple docstring"""




      def __init__( self			:		Dict     ,      UpperCAmelCase			:		RegNetConfig     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int = 1     ,      **UpperCAmelCase			:		Optional[Any]		)  ->  int:


       '''simple docstring'''

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






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		List[str]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =hidden_state
       for layer_module in self.layers:
        lowercase					:   List[str]				  =layer_module(lowerCAmelCase_		)
       lowercase					:   List[str]				  =self.shortcut(lowerCAmelCase_		)
       hidden_state += residual
       lowercase					:   Any				  =self.activation(lowerCAmelCase_		)
       return hidden_state





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




      """simple docstring"""




      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		RegNetConfig     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int = 2     ,      UpperCAmelCase			:		int = 2     ,      **UpperCAmelCase			:		int		)  ->  Optional[int]:


       '''simple docstring'''

       super().__init__(**lowerCAmelCase_		)

       lowercase					:   int				  =TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
       lowercase					:   Optional[Any]				  =[
           # downsampling is done in the first layer with stride of 2
           layer(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      stride=lowerCAmelCase_     ,      name='''layers.0'''		),
           *[layer(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      name=f'layers.{i+1}'		) for i in range(depth - 1		)],
       ]






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		int		)  ->  int:


       '''simple docstring'''

       for layer_module in self.layers:
        lowercase					:   str				  =layer_module(lowerCAmelCase_		)
       return hidden_state





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




      """simple docstring"""




      def __init__( self			:		List[str]     ,      UpperCAmelCase			:		RegNetConfig     ,      **UpperCAmelCase			:		Optional[int]		)  ->  Optional[int]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		tf.Tensor     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = True		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =() if output_hidden_states else None

       for stage_module in self.stages:
        if output_hidden_states:
         lowercase					:   Optional[Any]				  =hidden_states + (hidden_state,)

        lowercase					:   str				  =stage_module(lowerCAmelCase_		)

       if output_hidden_states:
        lowercase					:   Optional[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=lowerCAmelCase_     ,      hidden_states=lowerCAmelCase_		)





@keras_serializable
class UpperCAmelCase_					(    tf.keras.layers.Layer				):




      """simple docstring"""




      UpperCamelCase_								=			RegNetConfig






      def __init__( self			:		Any     ,      UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		List[Any]		)  ->  Optional[int]:


       '''simple docstring'''

       super().__init__(**lowerCAmelCase_		)
       lowercase					:   Union[str, Any]				  =config
       lowercase					:   List[str]				  =TFRegNetEmbeddings(lowerCAmelCase_     ,      name='''embedder'''		)
       lowercase					:   Tuple				  =TFRegNetEncoder(lowerCAmelCase_     ,      name='''encoder'''		)
       lowercase					:   Optional[Any]				  =tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCAmelCase_     ,      name='''pooler'''		)






      @unpack_inputs
      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		tf.Tensor     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		bool = False     ,      )  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase					:   Tuple				  =return_dict if return_dict is not None else self.config.use_return_dict

       lowercase					:   List[Any]				  =self.embedder(lowerCAmelCase_     ,      training=lowerCAmelCase_		)

       lowercase					:   Dict				  =self.encoder(
           lowerCAmelCase_     ,      output_hidden_states=lowerCAmelCase_     ,      return_dict=lowerCAmelCase_     ,      training=lowerCAmelCase_		)

       lowercase					:   List[str]				  =encoder_outputs[0]
       lowercase					:   Optional[int]				  =self.pooler(lowerCAmelCase_		)

       # Change to NCHW output format have uniformity in the modules
       lowercase					:   List[Any]				  =tf.transpose(lowerCAmelCase_     ,      perm=(0, 3, 1, 2)		)
       lowercase					:   List[Any]				  =tf.transpose(lowerCAmelCase_     ,      perm=(0, 3, 1, 2)		)

       # Change the other hidden state outputs to NCHW as well
       if output_hidden_states:
        lowercase					:   Tuple				  =tuple([tf.transpose(lowerCAmelCase_     ,      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=lowerCAmelCase_     ,      pooler_output=lowerCAmelCase_     ,      hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states     ,      )





class UpperCAmelCase_					(    __UpperCAmelCase				):




      """simple docstring"""




      UpperCamelCase_								=			RegNetConfig
      UpperCamelCase_								=			'''regnet'''
      UpperCamelCase_								=			'''pixel_values'''






      @property
      def 				A__      ( self			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

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





SCREAMING_SNAKE_CASE								=	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"

SCREAMING_SNAKE_CASE								=	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.'''				,       __UpperCAmelCase				,       )
class UpperCAmelCase_					(    __UpperCAmelCase				):




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		RegNetConfig     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		List[Any]		)  ->  Any:


       '''simple docstring'''

       super().__init__(lowerCAmelCase_     ,      *lowerCAmelCase_     ,      **lowerCAmelCase_		)
       lowercase					:   Any				  =TFRegNetMainLayer(lowerCAmelCase_     ,      name='''regnet'''		)






      @unpack_inputs
      @add_start_docstrings_to_model_forward(lowerCAmelCase_		)
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC     ,      output_type=lowerCAmelCase_     ,      config_class=_CONFIG_FOR_DOC     ,      modality='''vision'''     ,      expected_output=_EXPECTED_OUTPUT_SHAPE     ,      )
      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		tf.Tensor     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		int=False     ,      )  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   str				  =(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase					:   Dict				  =return_dict if return_dict is not None else self.config.use_return_dict

       lowercase					:   List[str]				  =self.regnet(
           pixel_values=lowerCAmelCase_     ,      output_hidden_states=lowerCAmelCase_     ,      return_dict=lowerCAmelCase_     ,      training=lowerCAmelCase_     ,      )
       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    '''				,       __UpperCAmelCase				,       )
class UpperCAmelCase_					(    __UpperCAmelCase				,       __UpperCAmelCase				):




      """simple docstring"""




      def __init__( self			:		Dict     ,      UpperCAmelCase			:		RegNetConfig     ,      *UpperCAmelCase			:		Tuple     ,      **UpperCAmelCase			:		str		)  ->  List[Any]:


       '''simple docstring'''

       super().__init__(lowerCAmelCase_     ,      *lowerCAmelCase_     ,      **lowerCAmelCase_		)
       lowercase					:   List[str]				  =config.num_labels
       lowercase					:   int				  =TFRegNetMainLayer(lowerCAmelCase_     ,      name='''regnet'''		)
       # classification head
       lowercase					:   str				  =[
           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(lowerCAmelCase_		)
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT     ,      output_type=lowerCAmelCase_     ,      config_class=_CONFIG_FOR_DOC     ,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,      )
      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		tf.Tensor = None     ,      UpperCAmelCase			:		tf.Tensor = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Tuple=False     ,      )  ->  List[str]:


       '''simple docstring'''

       lowercase					:   int				  =(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase					:   Optional[int]				  =return_dict if return_dict is not None else self.config.use_return_dict

       lowercase					:   List[Any]				  =self.regnet(
           lowerCAmelCase_     ,      output_hidden_states=lowerCAmelCase_     ,      return_dict=lowerCAmelCase_     ,      training=lowerCAmelCase_		)

       lowercase					:   Optional[int]				  =outputs.pooler_output if return_dict else outputs[1]

       lowercase					:   Optional[int]				  =self.classifier[0](lowerCAmelCase_		)
       lowercase					:   Optional[Any]				  =self.classifier[1](lowerCAmelCase_		)

       lowercase					:   Optional[int]				  =None if labels is None else self.hf_compute_loss(labels=lowerCAmelCase_     ,      logits=lowerCAmelCase_		)

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

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











'''simple docstring'''



from __future__ import annotations

SCREAMING_SNAKE_CASE						=	8.988E9  # units = N * m^s * C^-2


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      dict[str, float]:



 """simple docstring"""




 lowercase					:   Dict				  =abs(chargea * chargea			)

 if (force, chargea, chargea, distance).count(0			) != 1:
  raise ValueError('''One and only one argument must be 0'''			)
 if distance < 0:
  raise ValueError('''Distance cannot be negative'''			)
 if force == 0:
  lowercase					:   Union[str, Any]				  =COULOMBS_CONSTANT * charge_product / (distance**2)
  return {"force": force}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge1": chargea}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge2": chargea}
 elif distance == 0:
  lowercase					:   Tuple				  =(COULOMBS_CONSTANT * charge_product / abs(__A			)) ** 0.5
  return {"distance": distance}
 raise ValueError('''Exactly one argument must be 0'''			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



