
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 		__magic_name__				( lowercase_						,lowercase_						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = UNetaDModel
  _UpperCamelCase						 = "sample"


  @property
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						4
    _lowerCamelCase      =						14
    _lowerCamelCase      =						16

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

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


  @property
  def     _UpperCAmelCase  (					self		):
    return (4, 14, 16)


  @property
  def     _UpperCAmelCase  (					self		):
    return (4, 14, 16)


  def     _UpperCAmelCase  (					self		):
    pass


  def     _UpperCAmelCase  (					self		):
    pass


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


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


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


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


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


  @unittest.skipIf(torch_device == '''mps'''	,		'''mish op not supported in MPS'''		)
  def     _UpperCAmelCase  (					self		):
    super().test_output()


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{
        '''block_out_channels''': (32, 64, 1_28, 2_56),
        '''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''',
    }
    _lowerCamelCase      =						self.dummy_input
    return init_dict, inputs_dict


  @unittest.skipIf(torch_device == '''mps'''	,		'''mish op not supported in MPS'''		)
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						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__		)
    _lowerCamelCase      =						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		):
    _lowerCamelCase      =						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		)

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

    with torch.no_grad():
      _lowerCamelCase      =						model(a__	,		a__		).sample.permute(0	,		2	,		1		)

    _lowerCamelCase      =						output[0, -3:, -3:].flatten()
    # fmt: off
    _lowerCamelCase      =						torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]		)
    # fmt: on
    self.assertTrue(torch.allclose(a__	,		a__	,		rtol=1E-3		)		)


  def     _UpperCAmelCase  (					self		):
    # Not implemented yet for this UNet
    pass


  @slow
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						'''harmonai/maestro-150k'''
    _lowerCamelCase      =						UNetaDModel.from_pretrained(a__	,		subfolder='''unet'''		)
    model.to(a__		)

    _lowerCamelCase      =						6_55_36
    _lowerCamelCase      =						torch.sin(torch.arange(a__		)[None, None, :].repeat(1	,		2	,		1		)		).to(a__		)
    _lowerCamelCase      =						torch.tensor([1]		).to(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						model(a__	,		a__		).sample

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

    assert (output_sum - 224.0896).abs() < 0.5
    assert (output_max - 0.0607).abs() < 4E-4







class 		__magic_name__				( lowercase_						,lowercase_						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = UNetaDModel
  _UpperCamelCase						 = "sample"


  @property
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						4
    _lowerCamelCase      =						14
    _lowerCamelCase      =						16

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

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


  @property
  def     _UpperCAmelCase  (					self		):
    return (4, 14, 16)


  @property
  def     _UpperCAmelCase  (					self		):
    return (4, 14, 1)


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


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


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


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


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


  @unittest.skipIf(torch_device == '''mps'''	,		'''mish op not supported in MPS'''		)
  def     _UpperCAmelCase  (					self		):
    # UNetRL is a value-function is different output shape
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()
    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)
    model.eval()

    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		)

      if isinstance(a__	,		a__		):
        _lowerCamelCase      =						output.sample

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


  def     _UpperCAmelCase  (					self		):
    pass


  def     _UpperCAmelCase  (					self		):
    pass


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{
        '''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, 1_28, 2_56],
        '''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''',
    }
    _lowerCamelCase      =						self.dummy_input
    return init_dict, inputs_dict


  @unittest.skipIf(torch_device == '''mps'''	,		'''mish op not supported in MPS'''		)
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						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__		)
    _lowerCamelCase      =						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		):
    _lowerCamelCase    ,    _lowerCamelCase      =						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		)

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

    with torch.no_grad():
      _lowerCamelCase      =						value_function(a__	,		a__		).sample

    # fmt: off
    _lowerCamelCase      =						torch.tensor([165.25] * seq_len		)
    # fmt: on
    self.assertTrue(torch.allclose(a__	,		a__	,		rtol=1E-3		)		)


  def     _UpperCAmelCase  (					self		):
    # Not implemented yet for this UNet
    pass






import random

class 		__magic_name__				:


  """simple docstring"""





  def __init__(					self	,		a__	,		a__	,		a__ = 0.01	,		a__ = 10_00	,		a__ = -1	,		):
    _lowerCamelCase      =						sample
    if len(self.sample		) == 0:
      raise ValueError('''Sample data can not be empty'''		)
    _lowerCamelCase      =						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'''		)
    _lowerCamelCase      =						learning_rate
    _lowerCamelCase      =						epoch_number
    _lowerCamelCase      =						bias
    _lowerCamelCase      =						len(a__		)
    _lowerCamelCase      =						len(sample[0]		)  # number of columns in dataset
    _lowerCamelCase      =						[]


  def     _UpperCAmelCase  (					self		):
    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		)

    _lowerCamelCase      =						0

    while True:
      _lowerCamelCase      =						False
      for i in range(self.number_sample		):
        _lowerCamelCase      =						0
        for j in range(self.col_sample + 1		):
          _lowerCamelCase      =						u + self.weight[j] * self.sample[i][j]
        _lowerCamelCase      =						self.sign(a__		)
        if y != self.target[i]:
          for j in range(self.col_sample + 1		):
            _lowerCamelCase      =						(
                self.weight[j]
                + self.learning_rate
                * (self.target[i] - y)
                * self.sample[i][j]
            )
          _lowerCamelCase      =						True
            # print('Epoch: \n',epoch_count)
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self	,		a__		):
    if len(self.sample		) == 0:
      raise ValueError('''Sample data can not be empty'''		)
    sample.insert(0	,		self.bias		)
    _lowerCamelCase      =						0
    for i in range(self.col_sample + 1		):
      _lowerCamelCase      =						u + self.weight[i] * sample[i]

    _lowerCamelCase      =						self.sign(a__		)

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


  def     _UpperCAmelCase  (					self	,		a__		):
    return 1 if u >= 0 else -1







_UpperCAmelCase    			=					[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

_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=0.0_1, epoch_number=1000, 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)




