






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case							      =						logging.get_logger(__name__)

__snake_case							      =						{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}


def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Dict  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			Dict  ,						SCREAMING_SNAKE_CASE__						:			Optional[int] ):
    for attribute in key.split('''.''' ):
        UpperCamelCase :List[Any]										=     getattr(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )

    if weight_type is not None:
        UpperCamelCase :Dict										=     getattr(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ).shape
    else:
        UpperCamelCase :int										=     hf_pointer.shape

    assert hf_shape == value.shape, (
        F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
        F''' {value.shape} for {full_name}'''
    )

    if weight_type == "weight":
        UpperCamelCase :Any										=     value
    elif weight_type == "weight_g":
        UpperCamelCase :List[Any]										=     value
    elif weight_type == "weight_v":
        UpperCamelCase :str										=     value
    elif weight_type == "bias":
        UpperCamelCase :List[str]										=     value
    else:
        UpperCamelCase :Tuple										=     value

    logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )


def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[Any]  ,						SCREAMING_SNAKE_CASE__						:			List[str]  ,						SCREAMING_SNAKE_CASE__						:			Tuple ):
    UpperCamelCase :List[Any]										=     []
    UpperCamelCase :Tuple										=     fairseq_model.state_dict()

    UpperCamelCase :Tuple										=     hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCamelCase :List[Any]										=     False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						hf_model.config.feat_extract_norm == '''group'''  ,						)
            UpperCamelCase :List[str]										=     True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCamelCase :Optional[int]										=     '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
                    UpperCamelCase :List[str]										=     True
                    if "*" in mapped_key:
                        UpperCamelCase :Optional[Any]										=     name.split(SCREAMING_SNAKE_CASE__ )[0].split('''.''' )[-2]
                        if not layer_index.isnumeric():
                            continue
                        UpperCamelCase :Optional[Any]										=     mapped_key.replace('''*'''  ,						SCREAMING_SNAKE_CASE__ )
                    if "weight_g" in name:
                        UpperCamelCase :int										=     '''weight_g'''
                    elif "weight_v" in name:
                        UpperCamelCase :Union[str, Any]										=     '''weight_v'''
                    elif "weight" in name:
                        UpperCamelCase :Dict										=     '''weight'''
                    elif "bias" in name:
                        UpperCamelCase :Any										=     '''bias'''
                    else:
                        UpperCamelCase :str										=     None
                    set_recursively(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE__ )

    logger.warning(F'''Unused weights: {unused_weights}''' )


def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Tuple  ,						SCREAMING_SNAKE_CASE__						:			Any ):
    UpperCamelCase :Tuple										=     full_name.split('''conv_layers.''' )[-1]
    UpperCamelCase :Union[str, Any]										=     name.split('''.''' )
    UpperCamelCase :Dict										=     int(items[0] )
    UpperCamelCase :Any										=     int(items[1] )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
            )
            UpperCamelCase :Optional[int]										=     value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
            )
            UpperCamelCase :Any										=     value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                " found."
            )
            UpperCamelCase :int										=     value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
            )
            UpperCamelCase :Optional[Any]										=     value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE__ )


def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Any ):
    UpperCamelCase :Any										=     SEWDConfig()
    if is_finetuned:
        UpperCamelCase :List[Any]										=     model.wav_encoder.wav_model.cfg
    else:
        UpperCamelCase :Dict										=     model.cfg

    UpperCamelCase :Any										=     fs_config.conv_bias
    UpperCamelCase :List[Any]										=     eval(fs_config.conv_feature_layers )
    UpperCamelCase :Optional[int]										=     [x[0] for x in conv_layers]
    UpperCamelCase :List[str]										=     [x[1] for x in conv_layers]
    UpperCamelCase :Optional[Any]										=     [x[2] for x in conv_layers]
    UpperCamelCase :Any										=     '''gelu'''
    UpperCamelCase :Tuple										=     '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    UpperCamelCase :Optional[int]										=     0.0
    UpperCamelCase :Tuple										=     fs_config.activation_fn.name
    UpperCamelCase :Optional[Any]										=     fs_config.encoder_embed_dim
    UpperCamelCase :Optional[Any]										=     0.02
    UpperCamelCase :Optional[Any]										=     fs_config.encoder_ffn_embed_dim
    UpperCamelCase :Optional[Any]										=     1e-5
    UpperCamelCase :Optional[Any]										=     fs_config.encoder_layerdrop
    UpperCamelCase :Dict										=     fs_config.encoder_attention_heads
    UpperCamelCase :List[str]										=     fs_config.conv_pos_groups
    UpperCamelCase :Union[str, Any]										=     fs_config.conv_pos
    UpperCamelCase :Tuple										=     len(SCREAMING_SNAKE_CASE__ )
    UpperCamelCase :List[Any]										=     fs_config.encoder_layers
    UpperCamelCase :Optional[Any]										=     fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    UpperCamelCase :List[str]										=     fs_config.max_position_embeddings
    UpperCamelCase :str										=     fs_config.position_buckets
    UpperCamelCase :int										=     fs_config.share_att_key
    UpperCamelCase :Dict										=     fs_config.relative_attention
    UpperCamelCase :List[Any]										=     fs_config.position_biased_input
    UpperCamelCase :Optional[Any]										=     tuple(fs_config.pos_att_type.split('''|''' ) )
    UpperCamelCase :str										=     fs_config.norm_rel_ebd

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        UpperCamelCase :Tuple										=     model.cfg
        UpperCamelCase :Tuple										=     fs_config.final_dropout
        UpperCamelCase :Any										=     fs_config.layerdrop
    UpperCamelCase :List[Any]										=     fs_config.activation_dropout
    UpperCamelCase :Any										=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    UpperCamelCase :Optional[Any]										=     fs_config.attention_dropout
    UpperCamelCase :Dict										=     fs_config.dropout_input
    UpperCamelCase :int										=     fs_config.dropout
    UpperCamelCase :int										=     fs_config.mask_channel_length
    UpperCamelCase :Dict										=     fs_config.mask_channel_prob
    UpperCamelCase :Union[str, Any]										=     fs_config.mask_length
    UpperCamelCase :List[str]										=     fs_config.mask_prob

    UpperCamelCase :int										=     '''Wav2Vec2FeatureExtractor'''
    UpperCamelCase :int										=     '''Wav2Vec2CTCTokenizer'''

    return config


@torch.no_grad()
def 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			Any=None  ,						SCREAMING_SNAKE_CASE__						:			List[str]=None  ,						SCREAMING_SNAKE_CASE__						:			Any=True ):

    if is_finetuned:
        UpperCamelCase :str										=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]  ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
    else:
        UpperCamelCase :Union[str, Any]										=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

    if config_path is not None:
        UpperCamelCase :str										=     SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
    else:
        UpperCamelCase :Tuple										=     convert_config(model[0]  ,						SCREAMING_SNAKE_CASE__ )
    UpperCamelCase :str										=     model[0].eval()

    UpperCamelCase :Tuple										=     True if config.feat_extract_norm == '''layer''' else False
    UpperCamelCase :Dict										=     WavaVecaFeatureExtractor(
        feature_size=1  ,						sampling_rate=16000  ,						padding_value=0  ,						do_normalize=SCREAMING_SNAKE_CASE__  ,						return_attention_mask=SCREAMING_SNAKE_CASE__  ,						)

    if is_finetuned:
        if dict_path:
            UpperCamelCase :Tuple										=     Dictionary.load(SCREAMING_SNAKE_CASE__ )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCamelCase :int										=     target_dict.pad_index
            UpperCamelCase :Union[str, Any]										=     target_dict.bos_index
            UpperCamelCase :Dict										=     target_dict.pad_index
            UpperCamelCase :Optional[int]										=     target_dict.bos_index
            UpperCamelCase :Optional[Any]										=     target_dict.eos_index
            UpperCamelCase :List[str]										=     len(target_dict.symbols )
            UpperCamelCase :Dict										=     os.path.join(SCREAMING_SNAKE_CASE__  ,						'''vocab.json''' )
            if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE__ ) )
                return
            os.makedirs(SCREAMING_SNAKE_CASE__  ,						exist_ok=SCREAMING_SNAKE_CASE__ )
            with open(SCREAMING_SNAKE_CASE__  ,						'''w'''  ,						encoding='''utf-8''' ) as vocab_handle:
                json.dump(target_dict.indices  ,						SCREAMING_SNAKE_CASE__ )
            UpperCamelCase :Dict										=     WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE__  ,						unk_token=target_dict.unk_word  ,						pad_token=target_dict.pad_word  ,						bos_token=target_dict.bos_word  ,						eos_token=target_dict.eos_word  ,						word_delimiter_token='''|'''  ,						do_lower_case=SCREAMING_SNAKE_CASE__  ,						)
            UpperCamelCase :Optional[int]										=     WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__  ,						tokenizer=SCREAMING_SNAKE_CASE__ )
            processor.save_pretrained(SCREAMING_SNAKE_CASE__ )

        UpperCamelCase :Tuple										=     SEWDForCTC(SCREAMING_SNAKE_CASE__ )
    else:
        UpperCamelCase :List[str]										=     SEWDModel(SCREAMING_SNAKE_CASE__ )
        feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )

    recursively_load_weights(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
 __snake_case							      =						argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
 parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 parser.add_argument(
     """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
 )
 __snake_case							      =						parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )











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


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


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
    return [k for k in range(SCREAMING_SNAKE_CASE__ ) for _ in range(instance_count[k] )]


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
    return sum(SCREAMING_SNAKE_CASE__ ) / instance_count


def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			int ):
    return instance_count / total_count


def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			int ):
    UpperCamelCase :Optional[Any]										=     []  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
        # 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
    UpperCamelCase :Tuple										=     len(SCREAMING_SNAKE_CASE__ )  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__ )


def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			list ):
    UpperCamelCase :Dict										=     []
    # for loop iterates over number of elements in list
    for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i] ) ):
            UpperCamelCase :List[Any]										=     []  # 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(SCREAMING_SNAKE_CASE__ ) ):
                # 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(SCREAMING_SNAKE_CASE__ )

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


def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list ):
    UpperCamelCase :List[Any]										=     sum(1 for i, j in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) 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(SCREAMING_SNAKE_CASE__ )) * 100


__snake_case							      =						TypeVar("""num""")


def 						_A     (       SCREAMING_SNAKE_CASE__						:			Callable[[object], num]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Callable[[num], bool] = lambda SCREAMING_SNAKE_CASE__ : True  ,						SCREAMING_SNAKE_CASE__						:			str | None = None  ,						):
    while True:
        try:
            UpperCamelCase :List[Any]										=     input_type(input(SCREAMING_SNAKE_CASE__ ).strip() or default )
            if condition(SCREAMING_SNAKE_CASE__ ):
                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 						_A     (       ):
    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
        UpperCamelCase :List[Any]										=     valid_input(
            input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : 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
        UpperCamelCase :Any										=     valid_input(
            input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : 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
        UpperCamelCase :Tuple										=     []  # An empty list to store instance counts of classes in dataset
        for i in range(SCREAMING_SNAKE_CASE__ ):
            UpperCamelCase :List[Any]										=     valid_input(
                input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : x > 0  ,						input_msg=(F'''Enter The number of instances for class_{i+1}: ''')  ,						err_msg='''Number of instances should be positive!'''  ,						)
            counts.append(SCREAMING_SNAKE_CASE__ )
        print('''-''' * 100 )

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

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

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

        # Generating Ys to detecting corresponding classes
        UpperCamelCase :int										=     y_generator(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
        print('''Generated Corresponding Ys: \n'''  ,						SCREAMING_SNAKE_CASE__ )
        print('''-''' * 100 )

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

        # Calculating the value of probabilities for each class
        UpperCamelCase :Dict										=     [
            calculate_probabilities(counts[i]  ,						sum(SCREAMING_SNAKE_CASE__ ) ) for i in range(SCREAMING_SNAKE_CASE__ )
        ]

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

        # Calculating the values of variance for each class
        UpperCamelCase :int										=     calculate_variance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						sum(SCREAMING_SNAKE_CASE__ ) )
        print('''Variance: '''  ,						SCREAMING_SNAKE_CASE__ )
        print('''-''' * 100 )

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        UpperCamelCase :Any										=     predict_y_values(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
        print('''-''' * 100 )

        # Calculating Accuracy of the model
        print(F'''Accuracy: {accuracy(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )}''' )
        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()





