
"""simple docstring"""





import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import DecisionTransformerModel
				from transformers.models.decision_transformer.modeling_decision_transformer import (
				    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)






class     _UpperCAmelCase:



		def __init__(					self				,		__a				,		__a=13				,		__a=7				,		__a=6				,		__a=17				,		__a=23				,		__a=11				,		__a=True				,		)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  parent
							_UpperCamelCase							 =  batch_size
							_UpperCamelCase							 =  seq_length
							_UpperCamelCase							 =  act_dim
							_UpperCamelCase							 =  state_dim
							_UpperCamelCase							 =  hidden_size
							_UpperCamelCase							 =  max_length
							_UpperCamelCase							 =  is_training



		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  floats_tensor((self.batch_size, self.seq_length, self.state_dim))
							_UpperCamelCase							 =  floats_tensor((self.batch_size, self.seq_length, self.act_dim))
							_UpperCamelCase							 =  floats_tensor((self.batch_size, self.seq_length, 1))
							_UpperCamelCase							 =  floats_tensor((self.batch_size, self.seq_length, 1))
							_UpperCamelCase							 =  ids_tensor((self.batch_size, self.seq_length)				,		vocab_size=10_00)
							_UpperCamelCase							 =  random_attention_mask((self.batch_size, self.seq_length))

							_UpperCamelCase							 =  self.get_config()

							return (
							    config,
							    states,
							    actions,
							    rewards,
							    returns_to_go,
							    timesteps,
							    attention_mask,
							)



		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							return DecisionTransformerConfig(
							    batch_size=self.batch_size				,		seq_length=self.seq_length				,		act_dim=self.act_dim				,		state_dim=self.state_dim				,		hidden_size=self.hidden_size				,		max_length=self.max_length				,		)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  DecisionTransformerModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase							 =  model(__a				,		__a				,		__a				,		__a				,		__a				,		__a)

							self.parent.assertEqual(result.state_preds.shape				,		states.shape)
							self.parent.assertEqual(result.action_preds.shape				,		actions.shape)
							self.parent.assertEqual(result.return_preds.shape				,		returns_to_go.shape)
							self.parent.assertEqual(
							    result.last_hidden_state.shape				,		(self.batch_size, self.seq_length * 3, self.hidden_size))  # seq length *3 as there are 3 modelities: states, returns and actions




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






							'''simple docstring'''
							_UpperCamelCase							 =  self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							) ,  (
							    _UpperCamelCase
							) ,  (
							    _UpperCamelCase
							) ,  (
							    _UpperCamelCase
							) ,  (
							    _UpperCamelCase
							) ,  (
							    _UpperCamelCase
							) ,  (
							    _UpperCamelCase
							) ,  
							)							 =  config_and_inputs
							_UpperCamelCase							 =  {
							    '''states''': states,
							    '''actions''': actions,
							    '''rewards''': rewards,
							    '''returns_to_go''': returns_to_go,
							    '''timesteps''': timesteps,
							    '''attention_mask''': attention_mask,
							}
							return config, inputs_dict






@require_torch
class     _UpperCAmelCase(    lowerCamelCase      ,     lowerCamelCase      ,     lowerCamelCase      ,     unittest.TestCase	):
		lowercase__						=      (DecisionTransformerModel,) if is_torch_available() else ()
		lowercase__						=      ()
		lowercase__						=      {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

		# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
		lowercase__						=      False

		# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False
		lowercase__						=      False



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  DecisionTransformerModelTester(self)
							_UpperCamelCase							 =  ConfigTester(self				,		config_class=__a				,		hidden_size=37)



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		@slow
		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase							 =  DecisionTransformerModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase							 =  model_class(__a)
												_UpperCamelCase							 =  inspect.signature(model.forward)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_UpperCamelCase							 =  [*signature.parameters.keys()]

												_UpperCamelCase							 =  [
												    '''states''',
												    '''actions''',
												    '''rewards''',
												    '''returns_to_go''',
												    '''timesteps''',
												    '''attention_mask''',
												]

												self.assertListEqual(arg_names[: len(__a)]				,		__a)






@require_torch
class     _UpperCAmelCase(    unittest.TestCase	):



		@slow
		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  2  # number of steps of autoregressive prediction we will perform
							_UpperCamelCase							 =  10  # defined by the RL environment, may be normalized
							_UpperCamelCase							 =  DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''')
							_UpperCamelCase							 =  model.to(__a)
							_UpperCamelCase							 =  model.config
							torch.manual_seed(0)
							_UpperCamelCase							 =  torch.randn(1				,		1				,		config.state_dim).to(device=__a				,		dtype=torch.floataa)  # env.reset()

							_UpperCamelCase							 =  torch.tensor(
							    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]				,		device=__a)

							_UpperCamelCase							 =  torch.tensor(__a				,		device=__a				,		dtype=torch.floataa).reshape(1				,		1				,		1)
							_UpperCamelCase							 =  state
							_UpperCamelCase							 =  torch.zeros(1				,		0				,		config.act_dim				,		device=__a				,		dtype=torch.floataa)
							_UpperCamelCase							 =  torch.zeros(1				,		0				,		device=__a				,		dtype=torch.floataa)
							_UpperCamelCase							 =  torch.tensor(0				,		device=__a				,		dtype=torch.long).reshape(1				,		1)

							for step in range(__a):
												_UpperCamelCase							 =  torch.cat([actions, torch.zeros(1				,		1				,		config.act_dim				,		device=__a)]				,		dim=1)
												_UpperCamelCase							 =  torch.cat([rewards, torch.zeros(1				,		1				,		device=__a)]				,		dim=1)

												_UpperCamelCase							 =  torch.ones(1				,		states.shape[1]).to(dtype=torch.long				,		device=states.device)

												with torch.no_grad():
																	_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  model(
																	    states=__a				,		actions=__a				,		rewards=__a				,		returns_to_go=__a				,		timesteps=__a				,		attention_mask=__a				,		return_dict=__a				,		)

												self.assertEqual(action_pred.shape				,		actions.shape)
												self.assertTrue(torch.allclose(action_pred[0, -1]				,		expected_outputs[step]				,		atol=1e-4))
												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  (  # env.step(action)
												    torch.randn(1				,		1				,		config.state_dim).to(device=__a				,		dtype=torch.floataa),
												    1.0,
												    False,
												    {},
												)

												_UpperCamelCase							 =  action_pred[0, -1]
												_UpperCamelCase							 =  torch.cat([states, state]				,		dim=1)
												_UpperCamelCase							 =  returns_to_go[0, -1] - reward
												_UpperCamelCase							 =  torch.cat([returns_to_go, pred_return.reshape(1				,		1				,		1)]				,		dim=1)
												_UpperCamelCase							 =  torch.cat(
												    [timesteps, torch.ones((1, 1)				,		device=__a				,		dtype=torch.long) * (step + 1)]				,		dim=1)








"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






