

import random



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a , __a , __a = 0.01 , __a = 1000 , __a = -1 , )				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       sample
  if len(self.sample				) == 0:
   raise ValueError('Sample data can not be empty'				)
  UpperCAmelCase__           =       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__           =       learning_rate
  UpperCAmelCase__           =       epoch_number
  UpperCAmelCase__           =       bias
  UpperCAmelCase__           =       len(__a				)
  UpperCAmelCase__           =       len(sample[0]				)  # number of columns in dataset
  UpperCAmelCase__           =       []




 def 						UpperCamelCase__							(self				)				->      None:






  """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				)

  UpperCAmelCase__           =       0

  while True:
   UpperCAmelCase__           =       False
   for i in range(self.number_sample				):
    UpperCAmelCase__           =       0
    for j in range(self.col_sample + 1				):
     UpperCAmelCase__           =       u + self.weight[j] * self.sample[i][j]
    UpperCAmelCase__           =       self.sign(__a				)
    if y != self.target[i]:
     for j in range(self.col_sample + 1				):
      UpperCAmelCase__           =       (
          self.weight[j]
          + self.learning_rate
          * (self.target[i] - y)
          * self.sample[i][j]
      )
     UpperCAmelCase__           =       True
     # print('Epoch: \n',epoch_count)
   UpperCAmelCase__           =       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				)				->      None:






  """simple docstring"""





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

  UpperCAmelCase__           =       self.sign(__a				)

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




 def 						UpperCamelCase__							(self , __a				)				->      int:






  """simple docstring"""





  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)








import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.
def 		UpperCamelCase_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Union[str, Any]						,					snake_case__:						Any						,					snake_case__:						Any						,					snake_case__:						Union[str, Any]     ) ->				Any:
 # we don't care about the past rewards in this model

 UpperCAmelCase__           =       states.reshape(1						,					-1						,					model.config.state_dim     )
 UpperCAmelCase__           =       actions.reshape(1						,					-1						,					model.config.act_dim     )
 UpperCAmelCase__           =       returns_to_go.reshape(1						,					-1						,					1     )
 UpperCAmelCase__           =       timesteps.reshape(1						,					-1     )

 if model.config.max_length is not None:
  UpperCAmelCase__           =       states[:, -model.config.max_length :]
  UpperCAmelCase__           =       actions[:, -model.config.max_length :]
  UpperCAmelCase__           =       returns_to_go[:, -model.config.max_length :]
  UpperCAmelCase__           =       timesteps[:, -model.config.max_length :]

  # pad all tokens to sequence length
  UpperCAmelCase__           =       torch.cat(
      [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
  UpperCAmelCase__           =       attention_mask.to(dtype=torch.long						,					device=states.device     ).reshape(1						,					-1     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)						,					device=states.device						,					),
          states,
      ]						,					dim=1						,					).to(dtype=torch.floataa     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)						,					device=actions.device						,					),
          actions,
      ]						,					dim=1						,					).to(dtype=torch.floataa     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)						,					device=returns_to_go.device						,					),
          returns_to_go,
      ]						,					dim=1						,					).to(dtype=torch.floataa     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (timesteps.shape[0], model.config.max_length - timesteps.shape[1])						,					device=timesteps.device     ),
          timesteps,
      ]						,					dim=1						,					).to(dtype=torch.long     )
 else:
  UpperCAmelCase__           =       None

 UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       model(
     states=snake_case__						,					actions=snake_case__						,					rewards=snake_case__						,					returns_to_go=snake_case__						,					timesteps=snake_case__						,					attention_mask=snake_case__						,					return_dict=snake_case__						,					)

 return action_preds[0, -1]


# build the environment

_UpperCamelCase     =    gym.make('''Hopper-v3''')
_UpperCamelCase     =    env.observation_space.shape[0]
_UpperCamelCase     =    env.action_space.shape[0]
_UpperCamelCase     =    1000
_UpperCamelCase     =    '''cuda'''
_UpperCamelCase     =    1_0_0_0.0  # normalization for rewards/returns
_UpperCamelCase     =    3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase     =    np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_UpperCamelCase     =    np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_UpperCamelCase     =    torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase     =    torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_UpperCamelCase     =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
_UpperCamelCase     =    model.to(device)
model.eval()

for ep in range(10):
 _UpperCamelCase ,       _UpperCamelCase     =    0, 0
 _UpperCamelCase     =    env.reset()
 _UpperCamelCase     =    torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 _UpperCamelCase     =    torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros(0, device=device, dtype=torch.floataa)

 _UpperCamelCase     =    torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
 for t in range(max_ep_len):
  env.render()
  # add padding
  _UpperCamelCase     =    torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  _UpperCamelCase     =    torch.cat([rewards, torch.zeros(1, device=device)])

  _UpperCamelCase     =    get_action(
      model,
      (states.to(dtype=torch.floataa) - state_mean) / state_std,
      actions.to(dtype=torch.floataa),
      rewards.to(dtype=torch.floataa),
      target_return.to(dtype=torch.floataa),
      timesteps.to(dtype=torch.long),
  )
  _UpperCamelCase     =    action
  _UpperCamelCase     =    action.detach().cpu().numpy()

  _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     =    env.step(action)

  _UpperCamelCase     =    torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  _UpperCamelCase     =    torch.cat([states, cur_state], dim=0)
  _UpperCamelCase     =    reward

  _UpperCamelCase     =    target_return[0, -1] - (reward / scale)
  _UpperCamelCase     =    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  _UpperCamelCase     =    torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

  episode_return += reward
  episode_length += 1

  if done:
   break



