import random




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[str]							,      lowerCamelCase							:		list[list[float]]							,      lowerCamelCase							:		list[int]							,      lowerCamelCase							:		float = 0.0_1							,      lowerCamelCase							:		int = 1_0_0_0							,      lowerCamelCase							:		float = -1							,      )			->				None:
        _UpperCamelCase  :							str	    =	sample
        if len(self.sample	) == 0:
               raise ValueError('Sample data can not be empty'	)
        _UpperCamelCase  :							Any	    =	target
        if len(self.target	) == 0:
               raise ValueError('Target data can not be empty'	)
        if len(self.sample	) != len(self.target	):
               raise ValueError('Sample data and Target data do not have matching lengths'	)
        _UpperCamelCase  :							Dict	    =	learning_rate
        _UpperCamelCase  :							Dict	    =	epoch_number
        _UpperCamelCase  :							Optional[Any]	    =	bias
        _UpperCamelCase  :							Any	    =	len(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	len(sample[0]	)  # number of columns in dataset
        _UpperCamelCase  :							list	    =	[]

 def        __snake_case       (      self							:		List[str]	)			->				None:
        for sample in self.sample:
               sample.insert(0							,      self.bias	)

        for _ in range(self.col_sample	):
               self.weight.append(random.random()	)

        self.weight.insert(0							,      self.bias	)

        _UpperCamelCase  :							List[str]	    =	0

        while True:
               _UpperCamelCase  :							int	    =	False
               for i in range(self.number_sample	):
                      _UpperCamelCase  :							Union[str, Any]	    =	0
                      for j in range(self.col_sample + 1	):
                             _UpperCamelCase  :							List[Any]	    =	u + self.weight[j] * self.sample[i][j]
                      _UpperCamelCase  :							str	    =	self.sign(lowerCamelCase	)
                      if y != self.target[i]:
                             for j in range(self.col_sample + 1	):
                                    _UpperCamelCase  :							Optional[int]	    =	(
                                        self.weight[j]
                                        + self.learning_rate
                                        * (self.target[i] - y)
                                        * self.sample[i][j]
                                    )
                             _UpperCamelCase  :							Dict	    =	True
            # print('Epoch: \n',epoch_count)
               _UpperCamelCase  :							Optional[int]	    =	epoch_count + 1
               # if you want control the epoch or just by error
               if not has_misclassified:
                      print(('\nEpoch:\n', epoch_count)	)
                      print('------------------------\n'	)
                      # if epoch_count > self.epoch_number or not error:
                      break

 def        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		list[float]	)			->				None:
        if len(self.sample	) == 0:
               raise ValueError('Sample data can not be empty'	)
        sample.insert(0							,      self.bias	)
        _UpperCamelCase  :							List[Any]	    =	0
        for i in range(self.col_sample + 1	):
               _UpperCamelCase  :							List[Any]	    =	u + self.weight[i] * sample[i]

        _UpperCamelCase  :							Union[str, Any]	    =	self.sign(lowerCamelCase	)

        if y == -1:
               print(('Sample: ', sample)	)
               print('classification: P1'	)
        else:
               print(('Sample: ', sample)	)
               print('classification: P2'	)

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		float	)			->				int:
        return 1 if u >= 0 else -1







UpperCAmelCase      		=					[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

UpperCAmelCase      		=					[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCAmelCase      		=					Perceptron(
     sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
 )
 network.training()
 print('''Finished training perceptron''')
 print('''Enter values to predict or q to exit''')
 while True:
  UpperCAmelCase      		=					[]
  for i in range(len(samples[0])):
   UpperCAmelCase      		=					input('''value: ''').strip()
   if user_input == "q":
    break
   UpperCAmelCase      		=					float(user_input)
   sample.insert(i, observation)
  network.sort(sample)










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 	_UpperCAmelCase						( _lowercase			,							_lowercase			,							unittest.TestCase       ):
					__A        = UNetaDModel
					__A        = "sample"




					@property
					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						4
							UpperCamelCase__								=						14
							UpperCamelCase__								=						16

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

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




					@property
					def 						snake_case_		(    self							)    ->	Optional[int]:
							return (4, 14, 16)




					@property
					def 						snake_case_		(    self							)    ->	Any:
							return (4, 14, 16)




					def 						snake_case_		(    self							)    ->	Any:
							pass




					def 						snake_case_		(    self							)    ->	Tuple:
							pass




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Any:
							super().test_determinism()




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	int:
							super().test_outputs_equivalence()




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




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




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




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Dict:
							super().test_output()




					def 						snake_case_		(    self							)    ->	int:
							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 						snake_case_		(    self							)    ->	List[Any]:
							UpperCamelCase__	,      UpperCamelCase__								=						UNetaDModel.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32"""     ,   output_loading_info=__lowerCAmelCase     ,   subfolder="""unet"""							)
							self.assertIsNotNone(__lowerCAmelCase							)
							self.assertEqual(len(loading_info["""missing_keys"""]							)     ,   0							)

							model.to(__lowerCAmelCase							)
							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 						snake_case_		(    self							)    ->	Optional[Any]:
							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(__lowerCAmelCase     ,   __lowerCAmelCase							).sample.permute(0     ,   2     ,   1							)

							UpperCamelCase__								=						output[0, -3:, -3:].flatten()
							# fmt: off
							UpperCamelCase__								=						torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]							)
							# fmt: on
							self.assertTrue(torch.allclose(__lowerCAmelCase     ,   __lowerCAmelCase     ,   rtol=1E-3							)							)




					def 						snake_case_		(    self							)    ->	int:
							# Not implemented yet for this UNet
							pass




					@slow
					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						"""harmonai/maestro-150k"""
							UpperCamelCase__								=						UNetaDModel.from_pretrained(__lowerCAmelCase     ,   subfolder="""unet"""							)
							model.to(__lowerCAmelCase							)

							UpperCamelCase__								=						65_536
							UpperCamelCase__								=						torch.sin(torch.arange(__lowerCAmelCase							)[None, None, :].repeat(1     ,   2     ,   1							)							).to(__lowerCAmelCase							)
							UpperCamelCase__								=						torch.tensor([1]							).to(__lowerCAmelCase							)

							with torch.no_grad():
									UpperCamelCase__								=						model(__lowerCAmelCase     ,   __lowerCAmelCase							).sample

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

							assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
							assert (output_max - 0.0_6_0_7).abs() < 4E-4




class 	_UpperCAmelCase						( _lowercase			,							_lowercase			,							unittest.TestCase       ):
					__A        = UNetaDModel
					__A        = "sample"




					@property
					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						4
							UpperCamelCase__								=						14
							UpperCamelCase__								=						16

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

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




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return (4, 14, 16)




					@property
					def 						snake_case_		(    self							)    ->	List[Any]:
							return (4, 14, 1)




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	Dict:
							super().test_determinism()




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




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




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




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




					@unittest.skipIf(torch_device == """mps"""     ,   """mish op not supported in MPS"""							)
					def 						snake_case_		(    self							)    ->	List[str]:
							# UNetRL is a value-function is different output shape
							UpperCamelCase__	,      UpperCamelCase__								=						self.prepare_init_args_and_inputs_for_common()
							UpperCamelCase__								=						self.model_class(**__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()

							with torch.no_grad():
									UpperCamelCase__								=						model(**__lowerCAmelCase							)

									if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							):
											UpperCamelCase__								=						output.sample

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




					def 						snake_case_		(    self							)    ->	Optional[int]:
							pass




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							pass




					def 						snake_case_		(    self							)    ->	Dict:
							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 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__	,      UpperCamelCase__								=						UNetaDModel.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32"""     ,   output_loading_info=__lowerCAmelCase     ,   subfolder="""value_function"""							)
							self.assertIsNotNone(__lowerCAmelCase							)
							self.assertEqual(len(vf_loading_info["""missing_keys"""]							)     ,   0							)

							value_function.to(__lowerCAmelCase							)
							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 						snake_case_		(    self							)    ->	List[Any]:
							UpperCamelCase__	,      UpperCamelCase__								=						UNetaDModel.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32"""     ,   output_loading_info=__lowerCAmelCase     ,   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(__lowerCAmelCase     ,   __lowerCAmelCase							).sample

							# fmt: off
							UpperCamelCase__								=						torch.tensor([1_6_5.2_5] * seq_len							)
							# fmt: on
							self.assertTrue(torch.allclose(__lowerCAmelCase     ,   __lowerCAmelCase     ,   rtol=1E-3							)							)




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							# Not implemented yet for this UNet
							pass





