

from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar
def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						Any						,					snake_case__:						Tuple     ) ->				list:
 seed(1     )
 return [gauss(snake_case__						,					snake_case__     ) for _ in range(snake_case__     )]
def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						Any     ) ->				list:
 return [k for k in range(snake_case__     ) for _ in range(instance_count[k]     )]
def 		UpperCamelCase_(    snake_case__:						Tuple						,					snake_case__:						int     ) ->				float:
 return sum(snake_case__     ) / instance_count
def 		UpperCamelCase_(    snake_case__:						Optional[int]						,					snake_case__:						int     ) ->				float:
 return instance_count / total_count
def 		UpperCamelCase_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Any     ) ->				float:
 UpperCAmelCase__           =       []  # An empty list to store all squared differences
 # iterate over number of elements in items
 for i in range(len(snake_case__     )     ):
  # 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
 UpperCAmelCase__           =       len(snake_case__     )  # Number of classes in dataset
 return 1 / (total_count - n_classes) * sum(snake_case__     )
def 		UpperCamelCase_(    snake_case__:						Optional[int]						,					snake_case__:						Dict						,					snake_case__:						Tuple						,					snake_case__:						List[str]     ) ->				list:
 UpperCAmelCase__           =       []
 # for loop iterates over number of elements in list
 for i in range(len(snake_case__     )     ):
  # for loop iterates over number of inner items of each element
  for j in range(len(x_items[i]     )     ):
   UpperCAmelCase__           =       []  # 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(snake_case__     )     ):
    # 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(snake_case__     )

 return [result.index(max(snake_case__     )     ) for result in results]
def 		UpperCamelCase_(    snake_case__:						Optional[Any]						,					snake_case__:						Any     ) ->				float:
 UpperCAmelCase__           =       sum(1 for i, j in zip(snake_case__						,					snake_case__     ) 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(snake_case__     )) * 1_00


_UpperCamelCase     =    TypeVar('''num''')
def 		UpperCamelCase_(    snake_case__:						List[Any]						,					snake_case__:						str						,					snake_case__:						Any						,					snake_case__:						Optional[Any] = lambda snake_case__     : True						,					snake_case__:						List[str] = None						,					) ->				num:

 while True:
  try:
   UpperCAmelCase__           =       input_type(input(snake_case__     ).strip() or default     )
   if condition(snake_case__     ):
    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 		UpperCamelCase_(    ) ->				Tuple:

 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
  UpperCAmelCase__           =       valid_input(
      input_type=snake_case__						,					condition=lambda snake_case__     : x > 0						,					input_msg='Enter the number of classes (Data Groupings): '						,					err_msg='Number of classes should be positive!'						,					)

  print('-' * 1_00     )

  # Trying to get the value of standard deviation
  UpperCAmelCase__           =       valid_input(
      input_type=snake_case__						,					condition=lambda 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('-' * 1_00     )

  # Trying to get number of instances in classes and theirs means to generate
  # dataset
  UpperCAmelCase__           =       []  # An empty list to store instance counts of classes in dataset
  for i in range(snake_case__     ):
   UpperCAmelCase__           =       valid_input(
       input_type=snake_case__						,					condition=lambda 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(snake_case__     )
  print('-' * 1_00     )

  # An empty list to store values of user-entered means of classes
  UpperCAmelCase__           =       []
  for a in range(snake_case__     ):
   UpperCAmelCase__           =       valid_input(
       input_type=snake_case__						,					input_msg=(f"Enter the value of mean for class_{a+1}: ")						,					err_msg='This is an invalid value.'						,					)
   user_means.append(snake_case__     )
  print('-' * 1_00     )

  print('Standard deviation: '						,					snake_case__     )
  # print out the number of instances in classes in separated line
  for i, count in enumerate(snake_case__						,					1     ):
   print(f"Number of instances in class_{i} is: {count}"     )
  print('-' * 1_00     )

  # print out mean values of classes separated line
  for i, user_mean in enumerate(snake_case__						,					1     ):
   print(f"Mean of class_{i} is: {user_mean}"     )
  print('-' * 1_00     )

  # Generating training dataset drawn from gaussian distribution
  UpperCAmelCase__           =       [
      gaussian_distribution(user_means[j]						,					snake_case__						,					counts[j]     )
      for j in range(snake_case__     )
  ]
  print('Generated Normal Distribution: \n'						,					snake_case__     )
  print('-' * 1_00     )

  # Generating Ys to detecting corresponding classes
  UpperCAmelCase__           =       y_generator(snake_case__						,					snake_case__     )
  print('Generated Corresponding Ys: \n'						,					snake_case__     )
  print('-' * 1_00     )

  # Calculating the value of actual mean for each class
  UpperCAmelCase__           =       [calculate_mean(counts[k]						,					x[k]     ) for k in range(snake_case__     )]
  # for loop iterates over number of elements in 'actual_means' list and print
  # out them in separated line
  for i, actual_mean in enumerate(snake_case__						,					1     ):
   print(f"Actual(Real) mean of class_{i} is: {actual_mean}"     )
  print('-' * 1_00     )

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

  # for loop iterates over number of elements in 'probabilities' list and print
  # out them in separated line
  for i, probability in enumerate(snake_case__						,					1     ):
   print(f"Probability of class_{i} is: {probability}"     )
  print('-' * 1_00     )

  # Calculating the values of variance for each class
  UpperCAmelCase__           =       calculate_variance(snake_case__						,					snake_case__						,					sum(snake_case__     )     )
  print('Variance: '						,					snake_case__     )
  print('-' * 1_00     )

  # Predicting Y values
  # storing predicted Y values in 'pre_indexes' variable
  UpperCAmelCase__           =       predict_y_values(snake_case__						,					snake_case__						,					snake_case__						,					snake_case__     )
  print('-' * 1_00     )

  # Calculating Accuracy of the model
  print(f"Accuracy: {accuracy(snake_case__						,					snake_case__     )}"     )
  print('-' * 1_00     )
  print(' DONE '.center(1_00						,					'+'     )     )

  if input('Press any key to restart or \'q\' for quit: '     ).strip().lower() == "q":
   print('\n' + 'GoodBye!'.center(1_00						,					'-'     ) + '\n'     )
   break
  system('cls' if name == 'nt' else 'clear'     )  # noqa: S605


if __name__ == "__main__":
 main()






import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin



class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

  return {"sample": noise, "timestep": time_step}




 @property
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return (4, 14, 16)




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_output()




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'block_out_channels': (32, 64, 128, 256),
      'in_channels': 14,
      'out_channels': 14,
      'time_embedding_type': 'positional',
      'use_timestep_embedding': True,
      'flip_sin_to_cos': False,
      'freq_shift': 1.0,
      'out_block_type': 'OutConv1DBlock',
      'mid_block_type': 'MidResTemporalBlock1D',
      'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
      'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='unet'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(loading_info['missing_keys']				) , 0				)

  model.to(__a				)
  UpperCAmelCase__           =       model(**self.dummy_input				)

  assert image is not None, "Make sure output is not None"




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32' , subfolder='unet'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       model.config.in_channels
  UpperCAmelCase__           =       16
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample.permute(0 , 2 , 1				)

  UpperCAmelCase__           =       output[0, -3:, -3:].flatten()
  # fmt: off
  UpperCAmelCase__           =       torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  pass




 @slow
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       'harmonai/maestro-150k'
  UpperCAmelCase__           =       UNetaDModel.from_pretrained(__a , subfolder='unet'				)
  model.to(__a				)

  UpperCAmelCase__           =       65536
  UpperCAmelCase__           =       torch.sin(torch.arange(__a				)[None, None, :].repeat(1 , 2 , 1				)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([1]				).to(__a				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample

  UpperCAmelCase__           =       output.abs().sum()
  UpperCAmelCase__           =       output.abs().max()

  assert (output_sum - 2_24.08_96).abs() < 0.5
  assert (output_max - 0.06_07).abs() < 4E-4







class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

  return {"sample": noise, "timestep": time_step}




 @property
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (4, 14, 1)




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.prepare_init_args_and_inputs_for_common()
  UpperCAmelCase__           =       self.model_class(**__a				)
  model.to(__a				)
  model.eval()

  with torch.no_grad():
   UpperCAmelCase__           =       model(**__a				)

   if isinstance(__a , __a				):
    UpperCAmelCase__           =       output.sample

  self.assertIsNotNone(__a				)
  UpperCAmelCase__           =       torch.Size((inputs_dict['sample'].shape[0], 1)				)
  self.assertEqual(output.shape , __a , 'Input and output shapes do not match'				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'in_channels': 14,
      'out_channels': 14,
      'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
      'up_block_types': [],
      'out_block_type': 'ValueFunction',
      'mid_block_type': 'ValueFunctionMidBlock1D',
      'block_out_channels': [32, 64, 128, 256],
      'layers_per_block': 1,
      'downsample_each_block': True,
      'use_timestep_embedding': True,
      'freq_shift': 1.0,
      'flip_sin_to_cos': False,
      'time_embedding_type': 'positional',
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(vf_loading_info['missing_keys']				) , 0				)

  value_function.to(__a				)
  UpperCAmelCase__           =       value_function(**self.dummy_input				)

  assert image is not None, "Make sure output is not None"




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       value_function.config.in_channels
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       value_function(__a , __a				).sample

  # fmt: off
  UpperCAmelCase__           =       torch.tensor([1_65.25] * seq_len				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  pass



