



'''simple docstring'''






import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
						from transformers import MarkupLMFeatureExtractor

class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def __init__(		self    :  Any	,    __snake_case    :  str	)    ->	Dict:
								UpperCAmelCase				: str											=	parent







	def   A			(		self    :  Union[str, Any]	)    ->	Dict:
								return {}


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Optional[Any]											=	'''<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR=\"FFFFFF\">\n    <HR>\n    <a href=\"http://google.com\">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style=\"color:#0000FF\">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'''

							UpperCAmelCase				: Union[str, Any]											=	'''\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '''

							return [html_string_a, html_string_a]






@require_bsa
class        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= MarkupLMFeatureExtractor if is_bsa_available() else None







	def   A			(		self    :  Optional[Any]	)    ->	List[str]:
								UpperCAmelCase				: List[Any]											=	MarkupLMFeatureExtractionTester(self	)







	@property
	def   A			(		self    :  Optional[Any]	)    ->	str:
								return self.feature_extract_tester.prepare_feat_extract_dict()







	def   A			(		self    :  Optional[Any]	)    ->	List[str]:
								UpperCAmelCase				: Union[str, Any]											=	self.feature_extraction_class()

								# Test not batched input
								UpperCAmelCase				: Any											=	get_html_strings()[0]
								UpperCAmelCase				: Tuple											=	feature_extractor(lowercase_	)

								# fmt: off
								UpperCAmelCase				: str											=	[['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
								UpperCAmelCase				: Any											=	[['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
								# fmt: on

								self.assertEqual(encoding.nodes	,    lowercase_	)
								self.assertEqual(encoding.xpaths	,    lowercase_	)

								# Test batched
								UpperCAmelCase				: Optional[int]											=	get_html_strings()
								UpperCAmelCase				: List[str]											=	feature_extractor(lowercase_	)

								# fmt: off
								UpperCAmelCase				: Tuple											=	expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
								UpperCAmelCase				: Union[str, Any]											=	expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]

								self.assertEqual(len(encoding.nodes	)	,    2	)
								self.assertEqual(len(encoding.xpaths	)	,    2	)

								self.assertEqual(encoding.nodes	,    lowercase_	)
								self.assertEqual(encoding.xpaths	,    lowercase_	)










'''simple docstring'''






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  UNetaDModel	,    __snake_case    :  UNetaDModel	,    __snake_case    :  DDPMScheduler	,    __snake_case    :  List[Any]	,    )    ->	List[str]:
								super().__init__()
								UpperCAmelCase				: Any											=	value_function
								UpperCAmelCase				: List[str]											=	unet
								UpperCAmelCase				: Dict											=	scheduler
								UpperCAmelCase				: Union[str, Any]											=	env
								UpperCAmelCase				: Dict											=	env.get_dataset()
								UpperCAmelCase				: Tuple											=	{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase				: Optional[int]											=	self.data[key].mean()
															except:  # noqa: E722
																						pass
								UpperCAmelCase				: Any											=	{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase				: str											=	self.data[key].std()
															except:  # noqa: E722
																						pass
								UpperCAmelCase				: int											=	env.observation_space.shape[0]
								UpperCAmelCase				: Optional[Any]											=	env.action_space.shape[0]







	def   A			(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	)    ->	Optional[int]:
								return (x_in - self.means[key]) / self.stds[key]







	def   A			(		self    :  Dict	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								return x_in * self.stds[key] + self.means[key]







	def   A			(		self    :  Any	,    __snake_case    :  int	)    ->	List[Any]:
								if type(__snake_case	) is dict:
															return {k: self.to_torch(__snake_case	) for k, v in x_in.items()}
								elif torch.is_tensor(__snake_case	):
															return x_in.to(self.unet.device	)
								return torch.tensor(__snake_case	,    device=self.unet.device	)







	def   A			(		self    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  Union[str, Any]	)    ->	Dict:
								for key, val in cond.items():
															UpperCAmelCase				: List[Any]											=	val.clone()
								return x_in







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Dict	,    __snake_case    :  List[Any]	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								UpperCAmelCase				: Any											=	x.shape[0]
								UpperCAmelCase				: Any											=	None
								for i in tqdm.tqdm(self.scheduler.timesteps	):
															# create batch of timesteps to pass into model
															UpperCAmelCase				: Tuple											=	torch.full((batch_size,)	,    __snake_case	,    device=self.unet.device	,    dtype=torch.long	)
															for _ in range(__snake_case	):
																						with torch.enable_grad():
																													x.requires_grad_()

																													# permute to match dimension for pre-trained models
																													UpperCAmelCase				: Any											=	self.value_function(x.permute(0	,    2	,    1	)	,    __snake_case	).sample
																													UpperCAmelCase				: Dict											=	torch.autograd.grad([y.sum()]	,    [x]	)[0]

																													UpperCAmelCase				: Tuple											=	self.scheduler._get_variance(__snake_case	)
																													UpperCAmelCase				: Optional[int]											=	torch.exp(0.5 * posterior_variance	)
																													UpperCAmelCase				: str											=	model_std * grad

																						UpperCAmelCase				: Dict											=	0
																						UpperCAmelCase				: Optional[int]											=	x.detach()
																						UpperCAmelCase				: List[str]											=	x + scale * grad
																						UpperCAmelCase				: List[str]											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)

															UpperCAmelCase				: str											=	self.unet(x.permute(0	,    2	,    1	)	,    __snake_case	).sample.permute(0	,    2	,    1	)

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase				: Optional[int]											=	self.scheduler.step(__snake_case	,    __snake_case	,    __snake_case	,    predict_epsilon=__snake_case	)['''prev_sample''']

															# apply conditions to the trajectory (set the initial state)
															UpperCAmelCase				: Any											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
															UpperCAmelCase				: List[str]											=	self.to_torch(__snake_case	)
								return x, y







	def __call__(		self    :  Any	,    __snake_case    :  List[str]	,    __snake_case    :  Optional[Any]=64	,    __snake_case    :  Optional[int]=32	,    __snake_case    :  Union[str, Any]=2	,    __snake_case    :  Optional[int]=0.1	)    ->	Tuple:
								# normalize the observations and create  batch dimension
								UpperCAmelCase				: Optional[Any]											=	self.normalize(__snake_case	,    '''observations'''	)
								UpperCAmelCase				: Optional[int]											=	obs[None].repeat(__snake_case	,    axis=0	)

								UpperCAmelCase				: Tuple											=	{0: self.to_torch(__snake_case	)}
								UpperCAmelCase				: Tuple											=	(batch_size, planning_horizon, self.state_dim + self.action_dim)

								# generate initial noise and apply our conditions (to make the trajectories start at current state)
								UpperCAmelCase				: Union[str, Any]											=	randn_tensor(__snake_case	,    device=self.unet.device	)
								UpperCAmelCase				: int											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
								UpperCAmelCase				: List[Any]											=	self.to_torch(__snake_case	)

								# run the diffusion process
								UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self.run_diffusion(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

								# sort output trajectories by value
								UpperCAmelCase				: str											=	y.argsort(0	,    descending=__snake_case	).squeeze()
								UpperCAmelCase				: Tuple											=	x[sorted_idx]
								UpperCAmelCase				: List[Any]											=	sorted_values[:, :, : self.action_dim]
								UpperCAmelCase				: str											=	actions.detach().cpu().numpy()
								UpperCAmelCase				: List[str]											=	self.de_normalize(__snake_case	,    key='''actions'''	)

								# select the action with the highest value
								if y is not None:
															UpperCAmelCase				: List[Any]											=	0
								else:
															# if we didn't run value guiding, select a random action
															UpperCAmelCase				: Optional[int]											=	np.random.randint(0	,    __snake_case	)

								UpperCAmelCase				: Optional[int]											=	denorm_actions[selected_index, 0]
								return denorm_actions





