




'''simple docstring'''
from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar
def 				snake_case_  (   a__		:	float    ,a__		:	float    ,a__		:	int			):



    """simple docstring"""




    seed(1			)
    return [gauss(a__    ,a__			) for _ in range(a__			)]
def 				snake_case_  (   a__		:	int    ,a__		:	list			):



    """simple docstring"""




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



    """simple docstring"""




    return sum(a__			) / instance_count
def 				snake_case_  (   a__		:	int    ,a__		:	int			):



    """simple docstring"""




    return instance_count / total_count
def 				snake_case_  (   a__		:	list    ,a__		:	list    ,a__		:	int			):



    """simple docstring"""




    __lowercase							=						[]  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(a__			)			):
        # 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
    __lowercase							=						len(a__			)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(a__			)
def 				snake_case_  (   a__		:	list    ,a__		:	list    ,a__		:	float    ,a__		:	list			):



    """simple docstring"""




    __lowercase							=						[]
    # for loop iterates over number of elements in list
    for i in range(len(a__			)			):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i]			)			):
            __lowercase							=						[]  # 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(a__			)			):
                # 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(a__			)

    return [result.index(max(a__			)			) for result in results]
def 				snake_case_  (   a__		:	list    ,a__		:	list			):



    """simple docstring"""




    __lowercase							=						sum(1 for i, j in zip(a__    ,a__			) 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(a__			)) * 1_00


A							:      List[str] 	=       TypeVar("""num""")
def 				snake_case_  (   a__		:	Callable[[object], num]    ,a__		:	str    ,a__		:	str    ,a__		:	Callable[[num], bool] = lambda a__			: True    ,a__		:	str | None = None    ,):



    """simple docstring"""




    while True:
        try:
            __lowercase							=						input_type(input(a__			).strip() or default			)
            if condition(a__			):
                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 				snake_case_  (   ):



    """simple docstring"""




    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
        __lowercase							=						valid_input(
            input_type=a__    ,condition=lambda a__			: x > 0    ,input_msg="""Enter the number of classes (Data Groupings): """    ,err_msg="""Number of classes should be positive!"""    ,)

        print("""-""" * 1_00			)

        # Trying to get the value of standard deviation
        __lowercase							=						valid_input(
            input_type=a__    ,condition=lambda a__			: 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("""-""" * 1_00			)

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        __lowercase							=						[]  # An empty list to store instance counts of classes in dataset
        for i in range(a__			):
            __lowercase							=						valid_input(
                input_type=a__    ,condition=lambda a__			: x > 0    ,input_msg=(f'Enter The number of instances for class_{i+1}: ')    ,err_msg="""Number of instances should be positive!"""    ,)
            counts.append(a__			)
        print("""-""" * 1_00			)

        # An empty list to store values of user-entered means of classes
        __lowercase							=						[]
        for a in range(a__			):
            __lowercase							=						valid_input(
                input_type=a__    ,input_msg=(f'Enter the value of mean for class_{a+1}: ')    ,err_msg="""This is an invalid value."""    ,)
            user_means.append(a__			)
        print("""-""" * 1_00			)

        print("""Standard deviation: """    ,a__			)
        # print out the number of instances in classes in separated line
        for i, count in enumerate(a__    ,1			):
            print(f'Number of instances in class_{i} is: {count}'			)
        print("""-""" * 1_00			)

        # print out mean values of classes separated line
        for i, user_mean in enumerate(a__    ,1			):
            print(f'Mean of class_{i} is: {user_mean}'			)
        print("""-""" * 1_00			)

        # Generating training dataset drawn from gaussian distribution
        __lowercase							=						[
            gaussian_distribution(user_means[j]    ,a__    ,counts[j]			)
            for j in range(a__			)
        ]
        print("""Generated Normal Distribution: \n"""    ,a__			)
        print("""-""" * 1_00			)

        # Generating Ys to detecting corresponding classes
        __lowercase							=						y_generator(a__    ,a__			)
        print("""Generated Corresponding Ys: \n"""    ,a__			)
        print("""-""" * 1_00			)

        # Calculating the value of actual mean for each class
        __lowercase							=						[calculate_mean(counts[k]    ,x[k]			) for k in range(a__			)]
        # for loop iterates over number of elements in 'actual_means' list and print
        # out them in separated line
        for i, actual_mean in enumerate(a__    ,1			):
            print(f'Actual(Real) mean of class_{i} is: {actual_mean}'			)
        print("""-""" * 1_00			)

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

        # for loop iterates over number of elements in 'probabilities' list and print
        # out them in separated line
        for i, probability in enumerate(a__    ,1			):
            print(f'Probability of class_{i} is: {probability}'			)
        print("""-""" * 1_00			)

        # Calculating the values of variance for each class
        __lowercase							=						calculate_variance(a__    ,a__    ,sum(a__			)			)
        print("""Variance: """    ,a__			)
        print("""-""" * 1_00			)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        __lowercase							=						predict_y_values(a__    ,a__    ,a__    ,a__			)
        print("""-""" * 1_00			)

        # Calculating Accuracy of the model
        print(f'Accuracy: {accuracy(a__    ,a__			)}'			)
        print("""-""" * 1_00			)
        print(""" DONE """.center(1_00    ,"""+"""			)			)

        if input("""Press any key to restart or 'q' for quit: """			).strip().lower() == "q":
            print("""\n""" + """GoodBye!""".center(1_00    ,"""-"""			) + """\n"""			)
            break
        system("""cls""" if name == """nt""" else """clear"""			)  # noqa: S605


if __name__ == "__main__":
  main()










'''simple docstring'''
import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


A							:      Union[str, Any] 	=       logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[Any]    =					"""semantic"""

       def __init__(    self					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=True					,		lowerCamelCase__=768					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12_9595					,		lowerCamelCase__=12_9599					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=256					,		lowerCamelCase__=49.9					,		**lowerCamelCase__					,		)    ->					List[Any]:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		eos_token_id=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		max_new_tokens=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						text_encoding_offset
           __lowercase							=						text_pad_token
           __lowercase							=						eos_token_id
           __lowercase							=						semantic_infer_token
           __lowercase							=						semantic_vocab_size
           __lowercase							=						max_input_semantic_length
           __lowercase							=						semantic_rate_hz






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""coarse_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_2048					,		lowerCamelCase__=75					,		lowerCamelCase__=2					,		lowerCamelCase__=1_2050					,		lowerCamelCase__=256					,		lowerCamelCase__ = 630					,		lowerCamelCase__ = 60					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						coarse_semantic_pad_token
           __lowercase							=						coarse_rate_hz
           __lowercase							=						n_coarse_codebooks
           __lowercase							=						coarse_infer_token
           __lowercase							=						max_coarse_input_length
           __lowercase							=						max_coarse_history
           __lowercase							=						sliding_window_len






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=0.5					,		lowerCamelCase__=512					,		lowerCamelCase__=1024					,		lowerCamelCase__=8					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           super().__init__(temperature=lowerCamelCase__     )

           __lowercase							=						max_fine_history_length
           __lowercase							=						max_fine_input_length
           __lowercase							=						n_fine_codebooks






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""bark"""
       snake_case_ :     Dict    =					True

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=1024					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """simple docstring"""



           if semantic_config is None:
               __lowercase							=						{}
               logger.info("""semantic_config is None. initializing the semantic model with default values."""     )

           if coarse_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""     )

           if fine_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""     )

           __lowercase							=						BarkSemanticGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkCoarseGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkFineGenerationConfig(**lowerCamelCase__     )

           __lowercase							=						sample_rate
           __lowercase							=						codebook_size

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Union[str, Any]:

           """simple docstring"""



           return cls(
               semantic_config=semantic_config.to_dict()					,		coarse_acoustics_config=coarse_acoustics_config.to_dict()					,		fine_acoustics_config=fine_acoustics_config.to_dict()					,		**lowerCamelCase__					,		)




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						copy.deepcopy(self.__dict__     )

           __lowercase							=						self.semantic_config.to_dict()
           __lowercase							=						self.coarse_acoustics_config.to_dict()
           __lowercase							=						self.fine_acoustics_config.to_dict()

           __lowercase							=						self.__class__.model_type
           return output




