



"""simple docstring"""
import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import FlavaImageProcessor, FlavaProcessor
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )
@require_vision
class    a		(					unittest.TestCase    ):



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =tempfile.mkdtemp()

     # fmt: off
     SCREAMING_SNAKE_CASE_:	Dict            =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     SCREAMING_SNAKE_CASE_:	str            =os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]						)

     with open(self.vocab_file    ,						"""w"""    ,						encoding="""utf-8"""						) as fp:
        fp.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

     SCREAMING_SNAKE_CASE_:	int            ={
         'image_mean': FLAVA_IMAGE_MEAN,
         'image_std': FLAVA_IMAGE_STD,
         'do_normalize': True,
         'do_resize': True,
         'size': 224,
         'do_center_crop': True,
         'crop_size': 224,
         'input_size_patches': 14,
         'total_mask_patches': 75,
         'mask_group_max_patches': None,
         'mask_group_min_patches': 16,
         'mask_group_min_aspect_ratio': 0.3,
         'mask_group_max_aspect_ratio': None,
         'codebook_do_resize': True,
         'codebook_size': 112,
         'codebook_do_center_crop': True,
         'codebook_crop_size': 112,
         'codebook_do_map_pixels': True,
         'codebook_do_normalize': True,
         'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
         'codebook_image_std': FLAVA_CODEBOOK_STD,
     }

     SCREAMING_SNAKE_CASE_:	Dict            =os.path.join(self.tmpdirname    ,						_snake_case						)
     with open(self.image_processor_file    ,						"""w"""    ,						encoding="""utf-8"""						) as fp:
        json.dump(_snake_case    ,						_snake_case						)



  def 		lowerCamelCase__    (  self	:   int    ,						**lowerCAmelCase	:   Dict						)       ->   Dict:


     '''simple docstring'''




     return BertTokenizer.from_pretrained(self.tmpdirname    ,						**_snake_case						)



  def 		lowerCamelCase__    (  self	:   List[str]    ,						**lowerCAmelCase	:   Union[str, Any]						)       ->   List[str]:


     '''simple docstring'''




     return BertTokenizerFast.from_pretrained(self.tmpdirname    ,						**_snake_case						)



  def 		lowerCamelCase__    (  self	:   int    ,						**lowerCAmelCase	:   Tuple						)       ->   List[str]:


     '''simple docstring'''




     return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,						**_snake_case						)



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




     shutil.rmtree(self.tmpdirname						)



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =[np.random.randint(255    ,						size=(3, 30, 400)    ,						dtype=np.uinta						)]

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

     return image_inputs



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.get_tokenizer()
     SCREAMING_SNAKE_CASE_:	List[str]            =self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE_:	Dict            =self.get_image_processor()

     SCREAMING_SNAKE_CASE_:	int            =FlavaProcessor(tokenizer=_snake_case    ,						image_processor=_snake_case						)
     processor_slow.save_pretrained(self.tmpdirname						)
     SCREAMING_SNAKE_CASE_:	Any            =FlavaProcessor.from_pretrained(self.tmpdirname    ,						use_fast=_snake_case						)

     SCREAMING_SNAKE_CASE_:	Any            =FlavaProcessor(tokenizer=_snake_case    ,						image_processor=_snake_case						)
     processor_fast.save_pretrained(self.tmpdirname						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =FlavaProcessor.from_pretrained(self.tmpdirname						)

     self.assertEqual(processor_slow.tokenizer.get_vocab()    ,						tokenizer_slow.get_vocab()						)
     self.assertEqual(processor_fast.tokenizer.get_vocab()    ,						tokenizer_fast.get_vocab()						)
     self.assertEqual(tokenizer_slow.get_vocab()    ,						tokenizer_fast.get_vocab()						)
     self.assertIsInstance(processor_slow.tokenizer    ,						_snake_case						)
     self.assertIsInstance(processor_fast.tokenizer    ,						_snake_case						)

     self.assertEqual(processor_slow.image_processor.to_json_string()    ,						image_processor.to_json_string()						)
     self.assertEqual(processor_fast.image_processor.to_json_string()    ,						image_processor.to_json_string()						)
     self.assertIsInstance(processor_slow.image_processor    ,						_snake_case						)
     self.assertIsInstance(processor_fast.image_processor    ,						_snake_case						)



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =FlavaProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()						)
     processor.save_pretrained(self.tmpdirname						)

     SCREAMING_SNAKE_CASE_:	str            =self.get_tokenizer(bos_token="""(BOS)"""    ,						eos_token="""(EOS)"""						)
     SCREAMING_SNAKE_CASE_:	str            =self.get_image_processor(do_normalize=_snake_case    ,						padding_value=1.0						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =FlavaProcessor.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 		lowerCamelCase__    (  self	:   str						)       ->   Optional[int]:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =FlavaProcessor(tokenizer=_snake_case    ,						image_processor=_snake_case						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE_:	Optional[int]            =image_processor(_snake_case    ,						return_tensors="""np"""						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =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						)

     # With rest of the args
     random.seed(1234						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =image_processor(
         _snake_case    ,						return_image_mask=_snake_case    ,						return_codebook_pixels=_snake_case    ,						return_tensors="""np"""						)
     random.seed(1234						)
     SCREAMING_SNAKE_CASE_:	List[str]            =processor(
         images=_snake_case    ,						return_image_mask=_snake_case    ,						return_codebook_pixels=_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 		lowerCamelCase__    (  self	:   List[Any]						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.get_image_processor()
     SCREAMING_SNAKE_CASE_:	int            =self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:	Dict            =FlavaProcessor(tokenizer=_snake_case    ,						image_processor=_snake_case						)

     SCREAMING_SNAKE_CASE_:	str            ='lower newer'

     SCREAMING_SNAKE_CASE_:	List[str]            =processor(text=_snake_case						)

     SCREAMING_SNAKE_CASE_:	Any            =tokenizer(_snake_case						)

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



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


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	Dict            =FlavaProcessor(tokenizer=_snake_case    ,						image_processor=_snake_case						)

     SCREAMING_SNAKE_CASE_:	str            ='lower newer'
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.prepare_image_inputs()

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

     self.assertListEqual(list(inputs.keys()						)    ,						["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]						)

     # add extra args
     SCREAMING_SNAKE_CASE_:	Any            =processor(text=_snake_case    ,						images=_snake_case    ,						return_codebook_pixels=_snake_case    ,						return_image_mask=_snake_case						)

     self.assertListEqual(
         list(inputs.keys()						)    ,						[
             """input_ids""",
             """token_type_ids""",
             """attention_mask""",
             """pixel_values""",
             """codebook_pixel_values""",
             """bool_masked_pos""",
         ]    ,						)

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



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	List[str]            =FlavaProcessor(tokenizer=_snake_case    ,						image_processor=_snake_case						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     SCREAMING_SNAKE_CASE_:	List[str]            =processor.batch_decode(_snake_case						)
     SCREAMING_SNAKE_CASE_:	int            =tokenizer.batch_decode(_snake_case						)

     self.assertListEqual(_snake_case    ,						_snake_case						)





  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =self.get_image_processor()
     SCREAMING_SNAKE_CASE_:	Any            =self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:	Optional[int]            =FlavaProcessor(tokenizer=_snake_case    ,						image_processor=_snake_case						)

     SCREAMING_SNAKE_CASE_:	str            ='lower newer'
     SCREAMING_SNAKE_CASE_:	str            =self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE_:	Optional[int]            =processor(text=_snake_case    ,						images=_snake_case						)

     self.assertListEqual(list(inputs.keys()						)    ,						processor.model_input_names						)









"""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        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   seed(1	)
   return [gauss(lowercase							,     lowercase	) for _ in range(lowercase	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return sum(lowercase	) / instance_count





def        __magic_name__			(							lowercase							,     lowercase	):
   return instance_count / total_count





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





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
   # 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]	)	):
         SCREAMING_SNAKE_CASE_:	Optional[int]            =[]  # 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        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =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        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase = lambda lowercase	: True							,     lowercase = None							,     ):
   while True:
      try:
         SCREAMING_SNAKE_CASE_:	List[Any]            =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        __magic_name__			(							):
   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
      SCREAMING_SNAKE_CASE_:	List[Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: 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
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: 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
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store instance counts of classes in dataset
      for i in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	int            =valid_input(
             input_type=lowercase							,     condition=lambda lowercase	: 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
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
      for a in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	Tuple            =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
      SCREAMING_SNAKE_CASE_:	Tuple            =[
          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
      SCREAMING_SNAKE_CASE_:	int            =y_generator(lowercase							,     lowercase	)
      print("""Generated Corresponding Ys: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Calculating the value of actual mean for each class
      SCREAMING_SNAKE_CASE_:	str            =[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
      SCREAMING_SNAKE_CASE_:	List[Any]            =[
          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
      SCREAMING_SNAKE_CASE_:	str            =calculate_variance(lowercase							,     lowercase							,     sum(lowercase	)	)
      print("""Variance: """							,     lowercase	)
      print("""-""" * 100	)

      # Predicting Y values
      # storing predicted Y values in 'pre_indexes' variable
      SCREAMING_SNAKE_CASE_:	Dict            =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()



