


'''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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							seed(1   )
							return [gauss(UpperCamelCase_	,     UpperCamelCase_   ) for _ in range(UpperCamelCase_   )]







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							return [k for k in range(UpperCamelCase_   ) for _ in range(instance_count[k]   )]







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							return sum(UpperCamelCase_   ) / instance_count







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							return instance_count / total_count







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							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 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							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_   )) * 1_0_0


__SCREAMING_SNAKE_CASE				:							Dict		       =		TypeVar('''num''')







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_ = lambda UpperCamelCase_   : True	,     UpperCamelCase_ = None	,     ):
							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 			a_							(  ):
							while True:
														print(" Linear Discriminant Analysis ".center(5_0	,     "*"   )   )
														print("*" * 5_0	,     "\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("-" * 1_0_0   )

														# 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("-" * 1_0_0   )

														# 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("-" * 1_0_0   )

														# 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("-" * 1_0_0   )

														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("-" * 1_0_0   )

														# 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("-" * 1_0_0   )

														# 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("-" * 1_0_0   )

														# Generating Ys to detecting corresponding classes
														A_ 						=		y_generator(UpperCamelCase_	,     UpperCamelCase_   )
														print("Generated Corresponding Ys: \n"	,     UpperCamelCase_   )
														print("-" * 1_0_0   )

														# 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("-" * 1_0_0   )

														# 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("-" * 1_0_0   )

														# Calculating the values of variance for each class
														A_ 						=		calculate_variance(UpperCamelCase_	,     UpperCamelCase_	,     sum(UpperCamelCase_   )   )
														print("Variance: "	,     UpperCamelCase_   )
														print("-" * 1_0_0   )

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														A_ 						=		predict_y_values(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
														print("-" * 1_0_0   )

														# Calculating Accuracy of the model
														print(f"Accuracy: {accuracy(UpperCamelCase_	,     UpperCamelCase_   )}"   )
														print("-" * 1_0_0   )
														print(" DONE ".center(1_0_0	,     "+"   )   )

														if input("Press any key to restart or 'q' for quit: "   ).strip().lower() == "q":
																					print("\n" + "GoodBye!".center(1_0_0	,     "-"   ) + "\n"   )
																					break
														system("cls" if name == "nt" else "clear"   )  # noqa: S605


if __name__ == "__main__":
							main()









'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					int      	=["image_processor", "tokenizer"]
						_UpperCAmelCase		:					Dict      	="EfficientNetImageProcessor"
						_UpperCAmelCase		:					Union[str, Any]      	=("BertTokenizer", "BertTokenizerFast")






						def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			int  ):
													super().__init__(lowerCAmelCase   ,     lowerCAmelCase  )






						def __call__(     self       :			str   ,     lowerCAmelCase       :			Tuple=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Optional[int]="max_length"   ,     lowerCAmelCase       :			Tuple=64   ,     lowerCAmelCase       :			Dict=None   ,     **lowerCAmelCase       :			Any  ):
													if text is None and images is None:
																				raise ValueError("You have to specify either text or images. Both cannot be none."  )

													if text is not None:
																				A_ 						=		self.tokenizer(
																				    lowerCAmelCase   ,     padding=lowerCAmelCase   ,     max_length=lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if images is not None:
																				A_ 						=		self.image_processor(lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if text is not None and images is not None:
																				A_ 						=		image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCAmelCase  )   ,     tensor_type=lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     *lowerCAmelCase       :			Optional[int]   ,     **lowerCAmelCase       :			str  ):
													return self.tokenizer.batch_decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str   ,     *lowerCAmelCase       :			Any   ,     **lowerCAmelCase       :			Dict  ):
													return self.tokenizer.decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.tokenizer.model_input_names
													A_ 						=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





