


import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


A_    :					Dict             =logging.get_logger(__name__)

if is_tf_available():
	import tensorflow as tf





@dataclass
class        lowercase_				( lowercase__):






							"""simple docstring"""







							snake_case_        =		"""tf"""
							snake_case_        =		field(
							    default=lowercase__			,metadata={'''help''': '''Name of TPU'''}			,)

							snake_case_        =		field(
							    default=lowercase__			,metadata={'''help''': '''Zone of TPU'''}			,)

							snake_case_        =		field(
							    default=lowercase__			,metadata={'''help''': '''Name of Cloud TPU-enabled project'''}			,)

							snake_case_        =		field(
							    default=1.0			,metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}			,)

							snake_case_        =		field(default=lowercase__			,metadata={'''help''': '''Whether to activate the XLA compilation or not'''})


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													requires_backends(self			,    ["""tf"""]		)
													logger.info("""Tensorflow: setting up strategy"""		)

													a_					=		tf.config.list_physical_devices("""GPU"""		)

													# Set to float16 at first
													if self.fpaa:
																			tf.keras.mixed_precision.set_global_policy("""mixed_float16"""		)

													if self.no_cuda:
																			a_					=		tf.distribute.OneDeviceStrategy(device="""/cpu:0"""		)
													else:
																			try:
																									if self.tpu_name:
																															a_					=		tf.distribute.cluster_resolver.TPUClusterResolver(
																															    self.tpu_name			,    zone=self.tpu_zone			,    project=self.gcp_project		)
																									else:
																															a_					=		tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									if self.tpu_name:
																															raise RuntimeError(f"Couldn\'t connect to TPU {self.tpu_name}!"		)
																									else:
																															a_					=		None

																			if tpu:
																									# Set to bfloat16 in case of TPU
																									if self.fpaa:
																															tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""		)

																									tf.config.experimental_connect_to_cluster(_A		)
																									tf.tpu.experimental.initialize_tpu_system(_A		)

																									a_					=		tf.distribute.TPUStrategy(_A		)

																			elif len(_A		) == 0:
																									a_					=		tf.distribute.OneDeviceStrategy(device="""/cpu:0"""		)
																			elif len(_A		) == 1:
																									a_					=		tf.distribute.OneDeviceStrategy(device="""/gpu:0"""		)
																			elif len(_A		) > 1:
																									# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																									a_					=		tf.distribute.MirroredStrategy()
																			else:
																									raise ValueError("""Cannot find the proper strategy, please check your environment properties."""		)

													return strategy


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													requires_backends(self			,    ["""tf"""]		)
													return self._setup_strategy


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													requires_backends(self			,    ["""tf"""]		)
													return self._setup_strategy.num_replicas_in_sync


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return False  # TF Logging is handled by Keras not the Trainer


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													if self.per_gpu_train_batch_size:
																			logger.warning(
																			    """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
																			    """version. Using `--per_device_train_batch_size` is preferred."""		)
													a_					=		self.per_gpu_train_batch_size or self.per_device_train_batch_size
													return per_device_batch_size * self.n_replicas


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													if self.per_gpu_eval_batch_size:
																			logger.warning(
																			    """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
																			    """version. Using `--per_device_eval_batch_size` is preferred."""		)
													a_					=		self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
													return per_device_batch_size * self.n_replicas






							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													requires_backends(self			,    ["""tf"""]		)
													warnings.warn(
													    """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""			,    _A			,    )
													return self._setup_strategy.num_replicas_in_sync



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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						seed(1      )
						return [gauss(UpperCAmelCase__       ,    UpperCAmelCase__      ) for _ in range(UpperCAmelCase__      )]




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						return [k for k in range(UpperCAmelCase__      ) for _ in range(instance_count[k]      )]




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						return sum(UpperCAmelCase__      ) / instance_count




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						return instance_count / total_count




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




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

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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		sum(1 for i, j in zip(UpperCAmelCase__       ,    UpperCAmelCase__      ) 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(UpperCAmelCase__      )) * 100


A_    :					Dict             =TypeVar("""num""")




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__ = lambda UpperCAmelCase__      : True       ,    UpperCAmelCase__ = None       ,    ):



						"""simple docstring"""




						while True:
												try:
																		a_					=		input_type(input(UpperCAmelCase__      ).strip() or default      )
																		if condition(UpperCAmelCase__      ):
																								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   lowerCamelCase_       (					):



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

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

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

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

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

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

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

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

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

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

												# Calculating Accuracy of the model
												print(F"Accuracy: {accuracy(UpperCAmelCase__       ,    UpperCAmelCase__      )}"      )
												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()