





"""simple docstring"""


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


a__    :   str    						= logging.get_logger(__name__)

# General docstring
a__    :   str    						= """ResNetConfig"""

# Base docstring
a__    :   str    						= """microsoft/resnet-50"""
a__    :   Union[str, Any]    						= [1, 2_0_4_8, 7, 7]

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

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



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

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 3      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = "relu"      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= snake_case__										= (self.pad_value, self.pad_value)
        snake_case__										= tf.pad(__SCREAMING_SNAKE_CASE      ,    [(0, 0), height_pad, width_pad, (0, 0)]   )
        snake_case__										= self.conv(__SCREAMING_SNAKE_CASE   )
        return hidden_state

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = False   ):






        '''simple docstring'''

        snake_case__										= self.convolution(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.normalization(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.activation(__SCREAMING_SNAKE_CASE   )
        return hidden_state



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

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= TFResNetConvLayer(
            config.embedding_size      ,    kernel_size=7      ,    stride=2      ,    activation=config.hidden_act      ,    name="embedder"      ,    )
        snake_case__										= tf.keras.layers.MaxPoolaD(pool_size=3      ,    strides=2      ,    padding="valid"      ,    name="pooler"   )
        snake_case__										= config.num_channels

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = False   ):






        '''simple docstring'''

        snake_case__							,      snake_case__							,      snake_case__							,      snake_case__										= shape_list(__SCREAMING_SNAKE_CASE   )
        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."   )
        snake_case__										= pixel_values
        snake_case__										= self.embedder(__SCREAMING_SNAKE_CASE   )
        snake_case__										= tf.pad(__SCREAMING_SNAKE_CASE      ,    [[0, 0], [1, 1], [1, 1], [0, 0]]   )
        snake_case__										= self.pooler(__SCREAMING_SNAKE_CASE   )
        return hidden_state



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

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 2      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= tf.keras.layers.ConvaD(
            __SCREAMING_SNAKE_CASE      ,    kernel_size=1      ,    strides=__SCREAMING_SNAKE_CASE      ,    use_bias=__SCREAMING_SNAKE_CASE      ,    name="convolution"   )
        # Use same default momentum and epsilon as PyTorch equivalent
        snake_case__										= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,    momentum=0.9      ,    name="normalization"   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = False   ):






        '''simple docstring'''

        snake_case__										= x
        snake_case__										= self.convolution(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.normalization(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        return hidden_state



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

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = "relu"      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= in_channels != out_channels or stride != 1
        snake_case__										= TFResNetConvLayer(__SCREAMING_SNAKE_CASE      ,    stride=__SCREAMING_SNAKE_CASE      ,    name="layer.0"   )
        snake_case__										= TFResNetConvLayer(__SCREAMING_SNAKE_CASE      ,    activation=__SCREAMING_SNAKE_CASE      ,    name="layer.1"   )
        snake_case__										= (
            TFResNetShortCut(__SCREAMING_SNAKE_CASE      ,    stride=__SCREAMING_SNAKE_CASE      ,    name="shortcut"   )
            if should_apply_shortcut
            else tf.keras.layers.Activation("linear"      ,    name="shortcut"   )
        )
        snake_case__										= ACTaFN[activation]

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = False   ):






        '''simple docstring'''

        snake_case__										= hidden_state
        snake_case__										= self.conva(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.conva(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.shortcut(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        hidden_state += residual
        snake_case__										= self.activation(__SCREAMING_SNAKE_CASE   )
        return hidden_state



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

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = "relu"      ,    __SCREAMING_SNAKE_CASE = 4      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= in_channels != out_channels or stride != 1
        snake_case__										= out_channels // reduction
        snake_case__										= TFResNetConvLayer(__SCREAMING_SNAKE_CASE      ,    kernel_size=1      ,    name="layer.0"   )
        snake_case__										= TFResNetConvLayer(__SCREAMING_SNAKE_CASE      ,    stride=__SCREAMING_SNAKE_CASE      ,    name="layer.1"   )
        snake_case__										= TFResNetConvLayer(__SCREAMING_SNAKE_CASE      ,    kernel_size=1      ,    activation=__SCREAMING_SNAKE_CASE      ,    name="layer.2"   )
        snake_case__										= (
            TFResNetShortCut(__SCREAMING_SNAKE_CASE      ,    stride=__SCREAMING_SNAKE_CASE      ,    name="shortcut"   )
            if should_apply_shortcut
            else tf.keras.layers.Activation("linear"      ,    name="shortcut"   )
        )
        snake_case__										= ACTaFN[activation]

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = False   ):






        '''simple docstring'''

        snake_case__										= hidden_state
        snake_case__										= self.conva(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.conva(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.conva(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.shortcut(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        hidden_state += residual
        snake_case__										= self.activation(__SCREAMING_SNAKE_CASE   )
        return hidden_state



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

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 2      ,    __SCREAMING_SNAKE_CASE = 2      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )

        snake_case__										= TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

        snake_case__										= [layer(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    stride=__SCREAMING_SNAKE_CASE      ,    activation=config.hidden_act      ,    name="layers.0"   )]
        layers += [
            layer(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    activation=config.hidden_act      ,    name=F"layers.{i + 1}"   )
            for i in range(depth - 1   )
        ]
        snake_case__										= layers

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = False   ):






        '''simple docstring'''

        for layer in self.stage_layers:
         snake_case__										= layer(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )
        return hidden_state



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

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )
        # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
        snake_case__										= [
            TFResNetStage(
                __SCREAMING_SNAKE_CASE      ,    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(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    depth=__SCREAMING_SNAKE_CASE      ,    name=F"stages.{i + 1}"   )   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = False      ,    __SCREAMING_SNAKE_CASE = True      ,    __SCREAMING_SNAKE_CASE = False      ,    ):






        '''simple docstring'''

        snake_case__										= () if output_hidden_states else None

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

         snake_case__										= stage_module(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )

        if output_hidden_states:
         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=__SCREAMING_SNAKE_CASE      ,    hidden_states=__SCREAMING_SNAKE_CASE   )



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							Dict 							=   ResNetConfig
       snake_case__ :							Any 							=   """resnet"""
       snake_case__ :							str 							=   """pixel_values"""

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,    dtype=tf.floataa   )}
a__    :   Any    						= r"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


a__    :   Tuple    						= r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""



@keras_serializable
class    lowercase_ (   tf.keras.layers.Layer							):
       snake_case__ :							Tuple 							=   ResNetConfig

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= config
        snake_case__										= TFResNetEmbeddings(__SCREAMING_SNAKE_CASE      ,    name="embedder"   )
        snake_case__										= TFResNetEncoder(__SCREAMING_SNAKE_CASE      ,    name="encoder"   )
        snake_case__										= tf.keras.layers.GlobalAveragePoolingaD(keepdims=__SCREAMING_SNAKE_CASE   )

       @unpack_inputs
       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = False      ,    ):






        '''simple docstring'''

        snake_case__										= (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        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)
        snake_case__										= tf.transpose(__SCREAMING_SNAKE_CASE      ,    perm=[0, 2, 3, 1]   )
        snake_case__										= self.embedder(__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.encoder(
            __SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE      ,    return_dict=__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )

        snake_case__										= encoder_outputs[0]

        snake_case__										= self.pooler(__SCREAMING_SNAKE_CASE   )

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

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

        snake_case__										= hidden_states if output_hidden_states else None

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



@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top.""" ,__lowerCAmelCase ,)
class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= TFResNetMainLayer(config=__SCREAMING_SNAKE_CASE      ,    name="resnet"   )

       @add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE   )
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC      ,    output_type=__SCREAMING_SNAKE_CASE      ,    config_class=_CONFIG_FOR_DOC      ,    modality="vision"      ,    expected_output=_EXPECTED_OUTPUT_SHAPE      ,    )
       @unpack_inputs
       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = False      ,    ):






        '''simple docstring'''

        snake_case__										= (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        snake_case__										= return_dict if return_dict is not None else self.config.use_return_dict

        snake_case__										= self.resnet(
            pixel_values=__SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE      ,    return_dict=__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE      ,    )
        return resnet_outputs



@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """ ,__lowerCAmelCase ,)
class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__(__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= config.num_labels
        snake_case__										= TFResNetMainLayer(__SCREAMING_SNAKE_CASE      ,    name="resnet"   )
        # classification head
        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 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= tf.keras.layers.Flatten()(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.classifier_layer(__SCREAMING_SNAKE_CASE   )
        return logits

       @add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE   )
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT      ,    output_type=__SCREAMING_SNAKE_CASE      ,    config_class=_CONFIG_FOR_DOC      ,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,    )
       @unpack_inputs
       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = False      ,    ):






        '''simple docstring'''

        snake_case__										= return_dict if return_dict is not None else self.config.use_return_dict

        snake_case__										= self.resnet(
            __SCREAMING_SNAKE_CASE      ,    output_hidden_states=__SCREAMING_SNAKE_CASE      ,    return_dict=__SCREAMING_SNAKE_CASE      ,    training=__SCREAMING_SNAKE_CASE   )

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

        snake_case__										= self.classifier(__SCREAMING_SNAKE_CASE   )

        snake_case__										= None if labels is None else self.hf_compute_loss(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

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

        return TFImageClassifierOutputWithNoAttention(loss=__SCREAMING_SNAKE_CASE      ,    logits=__SCREAMING_SNAKE_CASE      ,    hidden_states=outputs.hidden_states   )








"""simple docstring"""


from __future__ import annotations





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   )			->     list[float | int]:

 '''simple docstring'''






 if not all((nth_term, start_term_a, common_ratio_r)		):
  return []
 snake_case__										= []
 snake_case__										= 1
 snake_case__										= common_ratio_r
 for _ in range(int(_UpperCAmelCase		)		):
  if not series:
   series.append(_UpperCAmelCase		)
  else:
   power += 1
   series.append(float(start_term_a * multiple		)		)
   snake_case__										= pow(float(_UpperCAmelCase		)		,   _UpperCAmelCase		)
 return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   a__    :   int    						= float(input("""Enter the last number (n term) of the Geometric Series"""))
   a__    :   str    						= float(input("""Enter the starting term (a) of the Geometric Series"""))
   a__    :   str    						= float(
       input("""Enter the common ratio between two terms (r) of the Geometric Series""")
   )
   print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
   print(geometric_series(nth_term, start_term_a, common_ratio_r))

