





"""simple docstring"""




import random

class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_ = 0.01   , snake_case_ = 1_0_0_0   , snake_case_ = -1   , ):

        """simple docstring"""

        A_    :			Optional[Any]									=    sample
        if len(self.sample    ) == 0:
          raise ValueError('Sample data can not be empty'    )
        A_    :			Optional[int]									=    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'    )
        A_    :			Tuple									=    learning_rate
        A_    :			Tuple									=    epoch_number
        A_    :			List[Any]									=    bias
        A_    :			Dict									=    len(snake_case_    )
        A_    :			Tuple									=    len(sample[0]    )  # number of columns in dataset
        A_    :			list									=    []

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        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    )

        A_    :			Dict									=    0

        while True:
          A_    :			str									=    False
          for i in range(self.number_sample    ):
            A_    :			Any									=    0
            for j in range(self.col_sample + 1    ):
              A_    :			Dict									=    u + self.weight[j] * self.sample[i][j]
            A_    :			Dict									=    self.sign(snake_case_    )
            if y != self.target[i]:
              for j in range(self.col_sample + 1    ):
                A_    :			int									=    (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              A_    :			List[str]									=    True
            # print('Epoch: \n',epoch_count)
          A_    :			str									=    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    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if len(self.sample    ) == 0:
          raise ValueError('Sample data can not be empty'    )
        sample.insert(0   , self.bias    )
        A_    :			Union[str, Any]									=    0
        for i in range(self.col_sample + 1    ):
          A_    :			Optional[int]									=    u + self.weight[i] * sample[i]

        A_    :			Union[str, Any]									=    self.sign(snake_case_    )

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

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return 1 if u >= 0 else -1


lowerCamelCase_	:		List[Any]         =   [
    [-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],
]

lowerCamelCase_	:		Optional[Any]         =   [
    -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()

   lowerCamelCase_	:		Any         =   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:
      lowerCamelCase_	:		list         =   []
      for i in range(len(samples[0])):
         lowerCamelCase_	:		Optional[Any]         =   input('value: ').strip()
         if user_input == "q":
            break
         lowerCamelCase_	:		List[Any]         =   float(user_input)
         sample.insert(i, observation)
      network.sort(sample)


"""simple docstring"""




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					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

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


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							pass


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().test_determinism()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained_variant()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_model_from_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							super().test_output()


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   {
							    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "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",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(loading_info["missing_keys"])	,	0)

							model.to(__SCREAMING_SNAKE_CASE)
							A     =   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    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   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)

							A     =   model.config.in_channels
							A     =   1_6
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample.permute(0	,	2	,	1)

							A     =   output[0, -3:, -3:].flatten()
							# fmt: off
							A     =   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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


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


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "harmonai/maestro-150k"
							A     =   UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							model.to(__SCREAMING_SNAKE_CASE)

							A     =   6_5_5_3_6
							A     =   torch.sin(torch.arange(__SCREAMING_SNAKE_CASE)[None, None, :].repeat(1	,	2	,	1)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1]).to(__SCREAMING_SNAKE_CASE)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							A     =   output.abs().sum()
							A     =   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					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

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


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							return (4, 1_4, 1)


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


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained_variant()


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


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							# UNetRL is a value-function is different output shape
							A     ,       A     =   self.prepare_init_args_and_inputs_for_common()
							A     =   self.model_class(**__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
												A     =   model(**__SCREAMING_SNAKE_CASE)

												if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE):
																	A     =   output.sample

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   {
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
							    "up_block_types": [],
							    "out_block_type": "ValueFunction",
							    "mid_block_type": "ValueFunctionMidBlock1D",
							    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
							    "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",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(vf_loading_info["missing_keys"])	,	0)

							value_function.to(__SCREAMING_SNAKE_CASE)
							A     =   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    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							torch.manual_seed(0)
							if torch.cuda.is_available():
												torch.cuda.manual_seed_all(0)

							A     =   value_function.config.in_channels
							A     =   1_4
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   value_function(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							# Not implemented yet for this UNet
							pass





