



'''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		(					_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						int							)			->						list:







	"""simple docstring"""
	seed(1							)
	return [gauss(lowercase_							,		lowercase_							) for _ in range(lowercase_							)]






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							)			->						list:







	"""simple docstring"""
	return [k for k in range(lowercase_							) for _ in range(instance_count[k]							)]






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						List[str]							)			->						float:







	"""simple docstring"""
	return sum(lowercase_							) / instance_count






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						int							)			->						float:







	"""simple docstring"""
	return instance_count / total_count






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						List[Any]							)			->						float:







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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Any							,		_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						List[Any]							)			->						list:







	"""simple docstring"""
	lowerCAmelCase    =	[]
	# for loop iterates over number of elements in list
	for i in range(len(lowercase_							)							):
		# for loop iterates over number of inner items of each element
		for j in range(len(x_items[i]							)							):
			lowerCAmelCase    =	[]  # 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(lowercase_							)							):
				# 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(lowercase_							)

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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						List[Any]							)			->						float:







	"""simple docstring"""
	lowerCAmelCase    =	sum(1 for i, j in zip(lowercase_							,		lowercase_							) 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(lowercase_							)) * 100


UpperCAmelCase			=    TypeVar('num')






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						int = lambda _SCREAMING_SNAKE_CASE							: True							,		_SCREAMING_SNAKE_CASE     :						List[Any] = None							,		)			->						num:







	"""simple docstring"""

	while True:
		try:
			lowerCAmelCase    =	input_type(input(lowercase_							).strip() or default							)
			if condition(lowercase_							):
				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		(					)			->						Any:







	"""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
		lowerCAmelCase    =	valid_input(
		    input_type=lowercase_							,		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
		lowerCAmelCase    =	valid_input(
		    input_type=lowercase_							,		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
		lowerCAmelCase    =	[]  # An empty list to store instance counts of classes in dataset
		for i in range(lowercase_							):
			lowerCAmelCase    =	valid_input(
			    input_type=lowercase_							,		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(lowercase_							)
		print("""-""" * 100							)

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

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

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

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

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

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

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

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

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

		# Calculating Accuracy of the model
		print(f'Accuracy: {accuracy(lowercase_							,		lowercase_							)}'							)
		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()






"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
  lowerCamelCase__      =      []

  lowerCamelCase__      =      os.path.join("tests", "models", "**", "test_modeling_*.py")
  lowerCamelCase__      =      glob.glob(pattern)
  # TODO: deal with TF/Flax too
  lowerCamelCase__      =      [
      x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
  ]

  for test_file in test_files:
    lowerCamelCase__      =      get_tester_classes(test_file)
    for tester_class in tester_classes:
      # A few tester classes don't have `parent` parameter in `__init__`.
      # TODO: deal this better
      try:
        lowerCamelCase__      =      tester_class(parent=None)
      except Exception:
        continue
      if hasattr(tester, "get_config"):
        lowerCamelCase__      =      tester.get_config()
        for k, v in config.to_dict().items():
          if isinstance(v, int):
            lowerCamelCase__      =      None
            if k in ["vocab_size"]:
              lowerCamelCase__      =      100
            elif k in ["max_position_embeddings"]:
              lowerCamelCase__      =      128
            elif k in ["hidden_size", "d_model"]:
              lowerCamelCase__      =      40
            elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
              lowerCamelCase__      =      5
            if target is not None and v > target:
              failures.append(
                  f"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
                  f""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
                  f""" than {target}."""
              )

  if len(failures) > 0:
    raise Exception(f"""There were {len(failures)} failures:\n""" + "\n".join(failures))
