
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


__magic_name__           =					logging.get_logger(__name__)

# General docstring
__magic_name__           =					'''ResNetConfig'''

# Base docstring
__magic_name__           =					'''microsoft/resnet-50'''
__magic_name__           =					[1, 2_048, 7, 7]

# Image classification docstring
__magic_name__           =					'''microsoft/resnet-50'''
__magic_name__           =					'''tiger cat'''

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




class    _SCREAMING_SNAKE_CASE					( tf.keras.layers.Layer	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase = 3						,		lowerCamelCase = 1						,		lowerCamelCase = "relu"						,		**lowerCamelCase	):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			kernel_size // 2
          snake_case__  =			tf.keras.layers.ConvaD(
              lowerCamelCase						,		kernel_size=lowerCamelCase						,		strides=lowerCamelCase						,		padding="valid"						,		use_bias=lowerCamelCase						,		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 A_     (     self						,		lowerCamelCase	):
          # Pad to match that done in the PyTorch Conv2D model
          snake_case__  =			snake_case__  =			(self.pad_value, self.pad_value)
          snake_case__  =			tf.pad(lowerCamelCase						,		[(0, 0), height_pad, width_pad, (0, 0)]	)
          snake_case__  =			self.conv(lowerCamelCase	)
          return hidden_state



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = False	):
          snake_case__  =			self.convolution(lowerCamelCase	)
          snake_case__  =			self.normalization(lowerCamelCase						,		training=lowerCamelCase	)
          snake_case__  =			self.activation(lowerCamelCase	)
          return hidden_state





class    _SCREAMING_SNAKE_CASE					( tf.keras.layers.Layer	):
      def __init__(     self						,		lowerCamelCase						,		**lowerCamelCase	):
          super().__init__(**lowerCamelCase	)
          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 A_     (     self						,		lowerCamelCase						,		lowerCamelCase = False	):
          snake_case__  ,   snake_case__  ,   snake_case__  ,   snake_case__  =			shape_list(lowerCamelCase	)
          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(lowerCamelCase	)
          snake_case__  =			tf.pad(lowerCamelCase						,		[[0, 0], [1, 1], [1, 1], [0, 0]]	)
          snake_case__  =			self.pooler(lowerCamelCase	)
          return hidden_state





class    _SCREAMING_SNAKE_CASE					( tf.keras.layers.Layer	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase = 2						,		**lowerCamelCase	):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			tf.keras.layers.ConvaD(
              lowerCamelCase						,		kernel_size=1						,		strides=lowerCamelCase						,		use_bias=lowerCamelCase						,		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 A_     (     self						,		lowerCamelCase						,		lowerCamelCase = False	):
          snake_case__  =			x
          snake_case__  =			self.convolution(lowerCamelCase	)
          snake_case__  =			self.normalization(lowerCamelCase						,		training=lowerCamelCase	)
          return hidden_state





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



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = False	):
          snake_case__  =			hidden_state
          snake_case__  =			self.conva(lowerCamelCase						,		training=lowerCamelCase	)
          snake_case__  =			self.conva(lowerCamelCase						,		training=lowerCamelCase	)
          snake_case__  =			self.shortcut(lowerCamelCase						,		training=lowerCamelCase	)
          hidden_state += residual
          snake_case__  =			self.activation(lowerCamelCase	)
          return hidden_state





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



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = False	):
          snake_case__  =			hidden_state
          snake_case__  =			self.conva(lowerCamelCase						,		training=lowerCamelCase	)
          snake_case__  =			self.conva(lowerCamelCase						,		training=lowerCamelCase	)
          snake_case__  =			self.conva(lowerCamelCase						,		training=lowerCamelCase	)
          snake_case__  =			self.shortcut(lowerCamelCase						,		training=lowerCamelCase	)
          hidden_state += residual
          snake_case__  =			self.activation(lowerCamelCase	)
          return hidden_state





class    _SCREAMING_SNAKE_CASE					( tf.keras.layers.Layer	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase = 2						,		lowerCamelCase = 2						,		**lowerCamelCase	):
          super().__init__(**lowerCamelCase	)

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

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



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = False	):
          for layer in self.stage_layers:
              snake_case__  =			layer(lowerCamelCase						,		training=lowerCamelCase	)
          return hidden_state





class    _SCREAMING_SNAKE_CASE					( tf.keras.layers.Layer	):
      def __init__(     self						,		lowerCamelCase						,		**lowerCamelCase	):
          super().__init__(**lowerCamelCase	)
          # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
          snake_case__  =			[
              TFResNetStage(
                  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"						,		)
          ]
          for i, (in_channels, out_channels, depth) in enumerate(
              zip(config.hidden_sizes						,		config.hidden_sizes[1:]						,		config.depths[1:]	)	):
              self.stages.append(TFResNetStage(lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		depth=lowerCamelCase						,		name=F"""stages.{i + 1}"""	)	)



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = False						,		lowerCamelCase = True						,		lowerCamelCase = False						,		):
          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(lowerCamelCase						,		training=lowerCamelCase	)

          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=lowerCamelCase						,		hidden_states=lowerCamelCase	)





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						List[Any]      		=       ResNetConfig
      _A  :						Any      		=       'resnet'
      _A  :						str      		=       'pixel_values'
      @property
      def A_     (     self	):
          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)						,		dtype=tf.floataa	)}





__magic_name__           =					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.
'''


__magic_name__           =					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    _SCREAMING_SNAKE_CASE					( tf.keras.layers.Layer	):
      _A  :						List[str]      		=       ResNetConfig
      def __init__(     self						,		lowerCamelCase						,		**lowerCamelCase	):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			config
          snake_case__  =			TFResNetEmbeddings(lowerCamelCase						,		name="embedder"	)
          snake_case__  =			TFResNetEncoder(lowerCamelCase						,		name="encoder"	)
          snake_case__  =			tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase	)



      @unpack_inputs
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = False						,		):
          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(lowerCamelCase						,		perm=[0, 2, 3, 1]	)
          snake_case__  =			self.embedder(lowerCamelCase						,		training=lowerCamelCase	)

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

          snake_case__  =			encoder_outputs[0]

          snake_case__  =			self.pooler(lowerCamelCase	)

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





@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'    ,   __UpperCamelCase    ,   )
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      def __init__(     self						,		lowerCamelCase						,		**lowerCamelCase	):
          super().__init__(lowerCamelCase						,		**lowerCamelCase	)
          snake_case__  =			TFResNetMainLayer(config=lowerCamelCase						,		name="resnet"	)



      @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						,		)
      @unpack_inputs
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = False						,		):
          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=lowerCamelCase						,		output_hidden_states=lowerCamelCase						,		return_dict=lowerCamelCase						,		training=lowerCamelCase						,		)
          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    '    ,   __UpperCamelCase    ,   )
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase    ,   __UpperCamelCase	):
      def __init__(     self						,		lowerCamelCase						,		**lowerCamelCase	):
          super().__init__(lowerCamelCase						,		**lowerCamelCase	)
          snake_case__  =			config.num_labels
          snake_case__  =			TFResNetMainLayer(lowerCamelCase						,		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 A_     (     self						,		lowerCamelCase	):
          snake_case__  =			tf.keras.layers.Flatten()(lowerCamelCase	)
          snake_case__  =			self.classifier_layer(lowerCamelCase	)
          return logits



      @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						,		)
      @unpack_inputs
      def A_     (     self						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = False						,		):
          snake_case__  =			return_dict if return_dict is not None else self.config.use_return_dict

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

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

          snake_case__  =			self.classifier(lowerCamelCase	)

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

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

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





from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    seed(1							)
    return [gauss(__lowerCAmelCase      ,						__lowerCAmelCase							) for _ in range(__lowerCAmelCase							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return [k for k in range(__lowerCAmelCase							) for _ in range(instance_count[k]							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return sum(__lowerCAmelCase							) / instance_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return instance_count / total_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(__lowerCAmelCase							)							):
        # for loop iterates over number of elements in inner layer of items
        for j in range(len(items[i]							)							):
            # appending squared differences to 'squared_diff' list
            squared_diff.append((items[i][j] - means[i]) ** 2							)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
    snake_case__  =			len(__lowerCAmelCase							)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]
    # for loop iterates over number of elements in list
    for i in range(len(__lowerCAmelCase							)							):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i]							)							):
            snake_case__  =			[]  # to store all discriminant values of each item as a list
            # for loop iterates over number of classes we have in our dataset
            for k in range(len(__lowerCAmelCase							)							):
                # appending values of discriminants for each class to 'temp' list
                temp.append(
                    x_items[i][j] * (means[k] / variance)
                    - (means[k] ** 2 / (2 * variance))
                    + log(probabilities[k]							)							)
            # appending discriminant values of each item to 'results' list
            results.append(__lowerCAmelCase							)

    return [result.index(max(__lowerCAmelCase							)							) for result in results]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			sum(1 for i, j in zip(__lowerCAmelCase      ,						__lowerCAmelCase							) if i == j							)
    # percentage of accuracy equals to number of correct predictions divided by number
    # of all data and multiplied by 100
    return (correct / len(__lowerCAmelCase							)) * 100


__magic_name__           =					TypeVar('''num''')





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = lambda __lowerCAmelCase							: True      ,						__lowerCAmelCase = None      ,						):
    while True:
        try:
            snake_case__  =			input_type(input(__lowerCAmelCase							).strip() or default							)
            if condition(__lowerCAmelCase							):
                return user_input
            else:
                print(F"""{user_input}: {err_msg}"""							)
                continue
        except ValueError:
            print(
                F"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""							)





def 		SCREAMING_SNAKE_CASE__      ( ):
    while True:
        print(" Linear Discriminant Analysis ".center(50      ,						"*"							)							)
        print("*" * 50      ,						"\n"							)
        print("First of all we should specify the number of classes that"							)
        print("we want to generate as training dataset"							)
        # Trying to get number of classes
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x > 0      ,						input_msg="Enter the number of classes (Data Groupings): "      ,						err_msg="Number of classes should be positive!"      ,						)

        print("-" * 100							)

        # Trying to get the value of standard deviation
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x >= 0      ,						input_msg=(
                "Enter the value of standard deviation"
                "(Default value is 1.0 for all classes): "
            )      ,						err_msg="Standard deviation should not be negative!"      ,						default="1.0"      ,						)

        print("-" * 100							)

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        snake_case__  =			[]  # An empty list to store instance counts of classes in dataset
        for i in range(__lowerCAmelCase							):
            snake_case__  =			valid_input(
                input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x > 0      ,						input_msg=(F"""Enter The number of instances for class_{i+1}: """)      ,						err_msg="Number of instances should be positive!"      ,						)
            counts.append(__lowerCAmelCase							)
        print("-" * 100							)

        # An empty list to store values of user-entered means of classes
        snake_case__  =			[]
        for a in range(__lowerCAmelCase							):
            snake_case__  =			valid_input(
                input_type=__lowerCAmelCase      ,						input_msg=(F"""Enter the value of mean for class_{a+1}: """)      ,						err_msg="This is an invalid value."      ,						)
            user_means.append(__lowerCAmelCase							)
        print("-" * 100							)

        print("Standard deviation: "      ,						__lowerCAmelCase							)
        # print out the number of instances in classes in separated line
        for i, count in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Number of instances in class_{i} is: {count}"""							)
        print("-" * 100							)

        # print out mean values of classes separated line
        for i, user_mean in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Mean of class_{i} is: {user_mean}"""							)
        print("-" * 100							)

        # Generating training dataset drawn from gaussian distribution
        snake_case__  =			[
            gaussian_distribution(user_means[j]      ,						__lowerCAmelCase      ,						counts[j]							)
            for j in range(__lowerCAmelCase							)
        ]
        print("Generated Normal Distribution: \n"      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Generating Ys to detecting corresponding classes
        snake_case__  =			y_generator(__lowerCAmelCase      ,						__lowerCAmelCase							)
        print("Generated Corresponding Ys: \n"      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Calculating the value of actual mean for each class
        snake_case__  =			[calculate_mean(counts[k]      ,						x[k]							) for k in range(__lowerCAmelCase							)]
        # for loop iterates over number of elements in 'actual_means' list and print
        # out them in separated line
        for i, actual_mean in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Actual(Real) mean of class_{i} is: {actual_mean}"""							)
        print("-" * 100							)

        # Calculating the value of probabilities for each class
        snake_case__  =			[
            calculate_probabilities(counts[i]      ,						sum(__lowerCAmelCase							)							) for i in range(__lowerCAmelCase							)
        ]

        # for loop iterates over number of elements in 'probabilities' list and print
        # out them in separated line
        for i, probability in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Probability of class_{i} is: {probability}"""							)
        print("-" * 100							)

        # Calculating the values of variance for each class
        snake_case__  =			calculate_variance(__lowerCAmelCase      ,						__lowerCAmelCase      ,						sum(__lowerCAmelCase							)							)
        print("Variance: "      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        snake_case__  =			predict_y_values(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Calculating Accuracy of the model
        print(F"""Accuracy: {accuracy(__lowerCAmelCase      ,						__lowerCAmelCase							)}"""							)
        print("-" * 100							)
        print(" DONE ".center(100      ,						"+"							)							)

        if input("Press any key to restart or 'q' for quit: "							).strip().lower() == "q":
            print("\n" + "GoodBye!".center(100      ,						"-"							) + "\n"							)
            break
        system("cls" if name == "nt" else "clear"							)  # noqa: S605


if __name__ == "__main__":
     main()



