
"""simple docstring"""







import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch
def 					__lowerCAmelCase       (_UpperCamelCase	):  # picklable for multiprocessing
	return x.sum()
def 					__lowerCAmelCase       (_UpperCamelCase	):  # picklable for multiprocessing
	return i + 1




@dataclass
class 				A__ :
		A_		: int
		A_		: str




class 				A__ (			_lowerCamelCase):

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   {}
			__lowerCAmelCase    :		Any		    =   []
			__lowerCAmelCase    :		int		    =   1
			__lowerCAmelCase    :		Union[str, Any]		    =   [1, 2]
			__lowerCAmelCase    :		Any		    =   {'a': 1, 'b': 2}
			__lowerCAmelCase    :		Union[str, Any]		    =   {'a': [1, 2], 'b': [3, 4]}
			__lowerCAmelCase    :		Optional[int]		    =   {'a': {'1': 1}, 'b': 2}
			__lowerCAmelCase    :		Optional[int]		    =   {'a': 1, 'b': 2, 'c': 3, 'd': 4}
			__lowerCAmelCase    :		Tuple		    =   {}
			__lowerCAmelCase    :		Optional[int]		    =   []
			__lowerCAmelCase    :		Tuple		    =   2
			__lowerCAmelCase    :		int		    =   [2, 3]
			__lowerCAmelCase    :		str		    =   {'a': 2, 'b': 3}
			__lowerCAmelCase    :		Any		    =   {'a': [2, 3], 'b': [4, 5]}
			__lowerCAmelCase    :		Union[str, Any]		    =   {'a': {'1': 2}, 'b': 3}
			__lowerCAmelCase    :		Any		    =   {'a': 2, 'b': 3, 'c': 4, 'd': 5}
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   2
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Any		    =   {'a': np.eye(2				), 'b': np.zeros(3				), 'c': np.ones(2				)}
			__lowerCAmelCase    :		int		    =   {'a': 2, 'b': 0, 'c': 2}
			__lowerCAmelCase    :		List[Any]		    =   {
			    'a': np.eye(2				).astype(_SCREAMING_SNAKE_CASE				),
			    'b': np.zeros(3				).astype(_SCREAMING_SNAKE_CASE				),
			    'c': np.ones(2				).astype(_SCREAMING_SNAKE_CASE				),
			}
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	map_numpy=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(
			    {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	map_numpy=_SCREAMING_SNAKE_CASE				).items()} ,	{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,	)
			self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	map_numpy=_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(
			    {k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	map_numpy=_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				).items()} ,	{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,	)
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):  # can't pickle a local lambda
				map_nested(lambda _SCREAMING_SNAKE_CASE				: x + 1 ,	_SCREAMING_SNAKE_CASE ,	num_proc=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   {'a': 1, 'b': 2}
			__lowerCAmelCase    :		Any		    =   {'a': 3, 'b': 4}
			__lowerCAmelCase    :		List[Any]		    =   {'a': 5, 'b': 6}
			__lowerCAmelCase    :		int		    =   sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]				)
			self.assertEqual(sorted(zip_dict(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				) ,	_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):




			class 				A__ :
					A_		: Optional[int]            =			'bar'



			__lowerCAmelCase    :		Dict		    =   Foo()
			self.assertEqual(foo.my_attr ,	'bar'				)
			with temporary_assignment(_SCREAMING_SNAKE_CASE ,	'my_attr' ,	'BAR'				):
				self.assertEqual(foo.my_attr ,	'BAR'				)
			self.assertEqual(foo.my_attr ,	'bar'				)



@pytest.mark.parametrize(
    'iterable_length, num_proc, expected_num_proc' ,						[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ] ,						)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	with patch('datasets.utils.py_utils._single_map_nested'	) as mock_single_map_nested, patch(
	    'datasets.parallel.parallel.Pool'	) as mock_multiprocessing_pool:
		__lowerCAmelCase    :		int		    =   {F"{i}": i for i in range(_UpperCamelCase	)}
		__lowerCAmelCase    :		List[Any]		    =   map_nested(lambda _UpperCamelCase	: x + 10 ,						_UpperCamelCase ,						num_proc=_UpperCamelCase ,						parallel_min_length=16	)
		if expected_num_proc == 1:
			assert mock_single_map_nested.called
			assert not mock_multiprocessing_pool.called
		else:
			assert not mock_single_map_nested.called
			assert mock_multiprocessing_pool.called
			assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc




class 				A__ (			_lowerCamelCase):

		@require_tf
		def 							__lowerCamelCase							(   self				):
			import tensorflow as tf
			from tensorflow.keras import layers

			__lowerCAmelCase    :		Tuple		    =   layers.Dense(2				)

			def gen_random_output():
				__lowerCAmelCase    :		Optional[int]		    =   tf.random.uniform((1, 3)				)
				return model(_SCREAMING_SNAKE_CASE				).numpy()

			with temp_seed(42 ,	set_tensorflow=_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		str		    =   gen_random_output()
			with temp_seed(42 ,	set_tensorflow=_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Optional[int]		    =   gen_random_output()
			__lowerCAmelCase    :		List[Any]		    =   gen_random_output()

			np.testing.assert_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			self.assertGreater(np.abs(outa - outa				).sum() ,	0				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			import torch

			def gen_random_output():
				__lowerCAmelCase    :		Any		    =   torch.nn.Linear(3 ,	2				)
				__lowerCAmelCase    :		Union[str, Any]		    =   torch.rand(1 ,	3				)
				return model(_SCREAMING_SNAKE_CASE				).detach().numpy()

			with temp_seed(42 ,	set_pytorch=_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Union[str, Any]		    =   gen_random_output()
			with temp_seed(42 ,	set_pytorch=_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Any		    =   gen_random_output()
			__lowerCAmelCase    :		str		    =   gen_random_output()

			np.testing.assert_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			self.assertGreater(np.abs(outa - outa				).sum() ,	0				)







		def 							__lowerCamelCase							(   self				):
			def gen_random_output():
				return np.random.rand(1 ,	3				)

			with temp_seed(42				):
				__lowerCAmelCase    :		Tuple		    =   gen_random_output()
			with temp_seed(42				):
				__lowerCAmelCase    :		Dict		    =   gen_random_output()
			__lowerCAmelCase    :		Optional[Any]		    =   gen_random_output()

			np.testing.assert_equal(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			self.assertGreater(np.abs(outa - outa				).sum() ,	0				)



@pytest.mark.parametrize('input_data' ,						[{}]	)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Union[str, Any]		    =   NestedDataStructure(_UpperCamelCase	).data
	assert output_data == input_data
@pytest.mark.parametrize(
    'data, expected_output' ,						[
        ({}, []),
        ([], []),
        ('foo', ['foo']),
        (['foo', 'bar'], ['foo', 'bar']),
        ([['foo', 'bar']], ['foo', 'bar']),
        ([[['foo'], ['bar']]], ['foo', 'bar']),
        ([[['foo'], 'bar']], ['foo', 'bar']),
        ({'a': 1, 'b': 2}, [1, 2]),
        ({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
        ({'a': {'1': 1}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
    ] ,						)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Union[str, Any]		    =   NestedDataStructure(_UpperCamelCase	).flatten()
	assert output == expected_output
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		str		    =   A(x=1 ,						y='foobar'	)
	__lowerCAmelCase    :		Union[str, Any]		    =   {'x': 1, 'y': 'foobar'}
	assert asdict(_UpperCamelCase	) == expected_output

	__lowerCAmelCase    :		Optional[int]		    =   {'a': {'b': A(x=10 ,						y='foo'	)}, 'c': [A(x=20 ,						y='bar'	)]}
	__lowerCAmelCase    :		Tuple		    =   {'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
	assert asdict(_UpperCamelCase	) == expected_output

	with pytest.raises(_UpperCamelCase	):
		asdict([1, A(x=10 ,						y='foo'	)]	)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	return text.split()
def 					__lowerCAmelCase       (_UpperCamelCase	):
	yield (time.time(), content)
	time.sleep(2	)
	yield (time.time(), content)
def 					__lowerCAmelCase       ():
	with Pool(2	) as pool:
		__lowerCAmelCase    :		str		    =   list(iflatmap_unordered(_UpperCamelCase ,						_split_text ,						kwargs_iterable=[{'text': 'hello there'}] * 10	)	)
		assert out.count('hello'	) == 10
		assert out.count('there'	) == 10
		assert len(_UpperCamelCase	) == 20

	# check multiprocess from pathos (uses dill for pickling)
	with multiprocess.Pool(2	) as pool:
		__lowerCAmelCase    :		str		    =   list(iflatmap_unordered(_UpperCamelCase ,						_split_text ,						kwargs_iterable=[{'text': 'hello there'}] * 10	)	)
		assert out.count('hello'	) == 10
		assert out.count('there'	) == 10
		assert len(_UpperCamelCase	) == 20

	# check that we get items as fast as possible
	with Pool(2	) as pool:
		__lowerCAmelCase    :		Dict		    =   []
		for yield_time, content in iflatmap_unordered(
		    _UpperCamelCase ,						_aseconds_generator_of_aitems_with_timing ,						kwargs_iterable=[{'content': 'a'}, {'content': 'b'}]	):
			assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
			out.append(_UpperCamelCase	)
		assert out.count('a'	) == 2
		assert out.count('b'	) == 2
		assert len(_UpperCamelCase	) == 4

"""simple docstring"""







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCamelCase__           =	{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
							lowerCamelCase__           =	"""hopper-medium-v2"""
							lowerCamelCase__           =	gym.make(env_name)

							lowerCamelCase__           =	ValueGuidedRLPipeline.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32""",
							    env=env,
							)

							env.seed(0)
							lowerCamelCase__           =	env.reset()
							lowerCamelCase__           =	0
							lowerCamelCase__           =	0
							lowerCamelCase__           =	1_000
							lowerCamelCase__           =	[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCamelCase__           =	pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__           =	env.step(denorm_actions)
																					lowerCamelCase__           =	env.get_normalized_score(total_reward)

																					# update return
																					total_reward += reward
																					total_score += score
																					print(
																					    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																					    f' {total_score}'
																					)

																					# save observations for rendering
																					rollout.append(next_observation.copy())

																					lowerCamelCase__           =	next_observation
							except KeyboardInterrupt:
														pass

							print(f'Total reward: {total_reward}')