

'''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   lowerCamelCase__       (      a__			,      a__			,      a__)		->    list:



    """simple docstring"""







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

def   lowerCamelCase__       (      a__			,      a__)		->    list:



    """simple docstring"""







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

def   lowerCamelCase__       (      a__			,      a__)		->    float:



    """simple docstring"""







    return sum(a__) / instance_count

def   lowerCamelCase__       (      a__			,      a__)		->    float:



    """simple docstring"""







    return instance_count / total_count

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    float:



    """simple docstring"""







    _snake_case : Optional[Any]												=		[]  # 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
    _snake_case : Union[str, Any]												=		len(a__)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(a__)

def   lowerCamelCase__       (      a__			,      a__			,      a__			,      a__)		->    list:



    """simple docstring"""







    _snake_case : int												=		[]
    # 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])):
            _snake_case : 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(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   lowerCamelCase__       (      a__			,      a__)		->    float:



    """simple docstring"""







    _snake_case : str												=		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_0_0


SCREAMING_SNAKE_CASE_    =   TypeVar("num")

def   lowerCamelCase__       (      a__			,      a__			,      a__			,      a__ = lambda a__: True			,      a__ = None			,      )		->    num:



    """simple docstring"""







    while True:
        try:
            _snake_case : List[str]												=		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   lowerCamelCase__       (      )		->    int:



    """simple docstring"""







    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
        _snake_case : Optional[Any]												=		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_0_0)

        # Trying to get the value of standard deviation
        _snake_case : Union[str, Any]												=		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_0_0)

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        _snake_case : Dict												=		[]  # An empty list to store instance counts of classes in dataset
        for i in range(a__):
            _snake_case : Union[str, Any]												=		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_0_0)

        # An empty list to store values of user-entered means of classes
        _snake_case : Union[str, Any]												=		[]
        for a in range(a__):
            _snake_case : Any												=		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_0_0)

        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_0_0)

        # 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_0_0)

        # Generating training dataset drawn from gaussian distribution
        _snake_case : Optional[Any]												=		[
            gaussian_distribution(user_means[j]			,      a__			,      counts[j])
            for j in range(a__)
        ]
        print('Generated Normal Distribution: \n'			,      a__)
        print('-' * 1_0_0)

        # Generating Ys to detecting corresponding classes
        _snake_case : Optional[Any]												=		y_generator(a__			,      a__)
        print('Generated Corresponding Ys: \n'			,      a__)
        print('-' * 1_0_0)

        # Calculating the value of actual mean for each class
        _snake_case : Union[str, Any]												=		[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_0_0)

        # Calculating the value of probabilities for each class
        _snake_case : Dict												=		[
            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_0_0)

        # Calculating the values of variance for each class
        _snake_case : Any												=		calculate_variance(a__			,      a__			,      sum(a__))
        print('Variance: '			,      a__)
        print('-' * 1_0_0)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        _snake_case : Dict												=		predict_y_values(a__			,      a__			,      a__			,      a__)
        print('-' * 1_0_0)

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



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   SCREAMING_SNAKE_CASE_    =   False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''      ,)
@require_vision
@require_torch
class 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






      '''simple docstring'''






      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : int												=		tempfile.mkdtemp()

          _snake_case : Optional[int]												=		PixaStructImageProcessor()
          _snake_case : Dict												=		TaTokenizer.from_pretrained('t5-small'   )

          _snake_case : Optional[Any]												=		PixaStructProcessor(snake_case     ,					snake_case   )

          processor.save_pretrained(self.tmpdirname   )




      def 	__UpperCAmelCase			(					self :		Optional[Any]     ,					**snake_case :		Any   ):



          """simple docstring"""





          return AutoProcessor.from_pretrained(self.tmpdirname     ,					**snake_case   ).tokenizer




      def 	__UpperCAmelCase			(					self :		int     ,					**snake_case :		Optional[int]   ):



          """simple docstring"""





          return AutoProcessor.from_pretrained(self.tmpdirname     ,					**snake_case   ).image_processor




      def 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          shutil.rmtree(self.tmpdirname   )




      def 	__UpperCAmelCase			(					self :		Tuple   ):



          """simple docstring"""





          _snake_case : List[Any]												=		[np.random.randint(255     ,					size=(3, 30, 400)     ,					dtype=np.uinta   )]

          _snake_case : List[str]												=		[Image.fromarray(np.moveaxis(snake_case     ,					0     ,					-1   )   ) for x in image_inputs]

          return image_inputs




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		PixaStructProcessor(tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()   )
          processor.save_pretrained(self.tmpdirname   )

          _snake_case : Tuple												=		self.get_tokenizer(bos_token='(BOS)'     ,					eos_token='(EOS)'   )
          _snake_case : Dict												=		self.get_image_processor(do_normalize=snake_case     ,					padding_value=1.0   )

          _snake_case : int												=		PixaStructProcessor.from_pretrained(
              self.tmpdirname     ,					bos_token='(BOS)'     ,					eos_token='(EOS)'     ,					do_normalize=snake_case     ,					padding_value=1.0   )

          self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer_add_kwargs.get_vocab()   )
          self.assertIsInstance(processor.tokenizer     ,					snake_case   )

          self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor_add_kwargs.to_json_string()   )
          self.assertIsInstance(processor.image_processor     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.get_image_processor()
          _snake_case : Optional[Any]												=		self.get_tokenizer()

          _snake_case : Optional[int]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[str]												=		self.prepare_image_inputs()

          _snake_case : Optional[Any]												=		image_processor(snake_case     ,					return_tensors='np'   )
          _snake_case : Tuple												=		processor(images=snake_case     ,					return_tensors='np'   )

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,					input_processor[key].sum()     ,					delta=1e-2   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		self.get_image_processor()
          _snake_case : Union[str, Any]												=		self.get_tokenizer()

          _snake_case : str												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[str]												=		'lower newer'

          _snake_case : Dict												=		processor(text=snake_case   )

          _snake_case : Any												=		tokenizer(snake_case     ,					return_token_type_ids=snake_case     ,					add_special_tokens=snake_case   )

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,					encoded_processor[key]   )




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.get_image_processor()
          _snake_case : List[str]												=		self.get_tokenizer()

          _snake_case : int												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Optional[Any]												=		'lower newer'
          _snake_case : Any												=		self.prepare_image_inputs()

          _snake_case : Any												=		processor(text=snake_case     ,					images=snake_case   )

          self.assertListEqual(
              list(inputs.keys()   )     ,					['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

          # test if it raises when no input is passed
          with pytest.raises(snake_case   ):
              processor()




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : int												=		self.get_image_processor()
          _snake_case : List[str]												=		self.get_tokenizer()

          _snake_case : Optional[Any]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Optional[Any]												=		'lower newer'
          _snake_case : str												=		self.prepare_image_inputs()

          _snake_case : Optional[Any]												=		processor(text=snake_case     ,					images=snake_case   )

          _snake_case : Tuple												=		[512, 1024, 2048, 4096]
          _snake_case : str												=		[770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(snake_case   ):
              _snake_case : List[Any]												=		processor(text=snake_case     ,					images=snake_case     ,					max_patches=snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,					snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,					expected_hidden_size[i]   )

          # without text input
          for i, max_patch in enumerate(snake_case   ):
              _snake_case : Union[str, Any]												=		processor(images=snake_case     ,					max_patches=snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,					snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,					expected_hidden_size[i]   )




      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          _snake_case : Union[str, Any]												=		self.get_image_processor()
          _snake_case : List[Any]												=		self.get_tokenizer()

          _snake_case : List[Any]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[Any]												=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _snake_case : Dict												=		processor.batch_decode(snake_case   )
          _snake_case : List[Any]												=		tokenizer.batch_decode(snake_case   )

          self.assertListEqual(snake_case     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : str												=		self.get_image_processor()
          _snake_case : Optional[int]												=		self.get_tokenizer()

          _snake_case : List[str]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Tuple												=		'lower newer'
          _snake_case : List[Any]												=		self.prepare_image_inputs()

          _snake_case : Tuple												=		processor(text=snake_case     ,					images=snake_case   )

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(
              list(inputs.keys()   )     ,					['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

          _snake_case : str												=		processor(text=snake_case   )

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(list(inputs.keys()   )     ,					['input_ids', 'attention_mask']   )



