'''simple docstring'''
import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self  ,      _lowerCamelCase = False     ):
         __UpperCamelCase						:						int		     =	1_0

         if only_cross_attention:
             __UpperCamelCase						:						Dict		     =	1_2
         else:
             # when only cross attention is not set, the cross attention dim must be the same as the query dim
             __UpperCamelCase						:						Any		     =	query_dim

         return {
             "query_dim": query_dim,
             "cross_attention_dim": cross_attention_dim,
             "heads": 2,
             "dim_head": 4,
             "added_kv_proj_dim": 6,
             "norm_num_groups": 1,
             "only_cross_attention": only_cross_attention,
             "processor": AttnAddedKVProcessor(),
         }







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						List[Any]		     =	2

         __UpperCamelCase						:						Any		     =	torch.rand(a__  ,      a__  ,      3  ,      2     )
         __UpperCamelCase						:						Optional[int]		     =	torch.rand(a__  ,      4  ,      a__     )
         __UpperCamelCase						:						str		     =	None

         return {
             "hidden_states": hidden_states,
             "encoder_hidden_states": encoder_hidden_states,
             "attention_mask": attention_mask,
         }







     def lowerCAmelCase  (		self     ):
         # self and cross attention

         torch.manual_seed(0     )

         __UpperCamelCase						:						str		     =	self.get_constructor_arguments(only_cross_attention=a__     )
         __UpperCamelCase						:						Dict		     =	Attention(**a__     )

         self.assertTrue(attn.to_k is not None     )
         self.assertTrue(attn.to_v is not None     )

         __UpperCamelCase						:						str		     =	self.get_forward_arguments(
             query_dim=constructor_args['query_dim']  ,      added_kv_proj_dim=constructor_args['added_kv_proj_dim']     )

         __UpperCamelCase						:						str		     =	attn(**a__     )

         # only self attention

         torch.manual_seed(0     )

         __UpperCamelCase						:						Dict		     =	self.get_constructor_arguments(only_cross_attention=a__     )
         __UpperCamelCase						:						List[Any]		     =	Attention(**a__     )

         self.assertTrue(attn.to_k is None     )
         self.assertTrue(attn.to_v is None     )

         __UpperCamelCase						:						Optional[int]		     =	self.get_forward_arguments(
             query_dim=constructor_args['query_dim']  ,      added_kv_proj_dim=constructor_args['added_kv_proj_dim']     )

         __UpperCamelCase						:						Optional[Any]		     =	attn(**a__     )

         self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()     )






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'  ,      safety_checker=a__     )

         __UpperCamelCase						:						Optional[int]		     =	pipe(
             'foo'  ,      num_inference_steps=2  ,      generator=torch.Generator('cpu'     ).manual_seed(0     )  ,      output_type='np'  ,      ).images

         # the initial conversion succeeds
         __UpperCamelCase						:						Dict		     =	DiffusionPipeline.from_pretrained(
             'hf-internal-testing/tiny-stable-diffusion-pipe'  ,      device_map='sequential'  ,      safety_checker=a__     )

         __UpperCamelCase						:						Union[str, Any]		     =	pipe(
             'foo'  ,      num_inference_steps=2  ,      generator=torch.Generator('cpu'     ).manual_seed(0     )  ,      output_type='np'  ,      ).images

         with tempfile.TemporaryDirectory() as tmpdir:
             # save the converted model
             pipe.save_pretrained(a__     )

             # can also load the converted weights
             __UpperCamelCase						:						List[str]		     =	DiffusionPipeline.from_pretrained(a__  ,      device_map='sequential'  ,      safety_checker=a__     )

         __UpperCamelCase						:						int		     =	pipe(
             'foo'  ,      num_inference_steps=2  ,      generator=torch.Generator('cpu'     ).manual_seed(0     )  ,      output_type='np'  ,      ).images

         self.assertTrue(np.allclose(a__  ,      a__  ,      atol=1E-5     )     )
         self.assertTrue(np.allclose(a__  ,      a__  ,      atol=1E-5     )     )





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)




