


"""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_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       List[str]=1_3    ,    UpperCAmelCase__    :       Any=7    ,    UpperCAmelCase__    :       int=6    ,    UpperCAmelCase__    :       Optional[Any]=1_7    ,    UpperCAmelCase__    :       Any=2_3    ,    UpperCAmelCase__    :       Tuple=1_1    ,    UpperCAmelCase__    :       int=True    ,    ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       parent
						__SCREAMING_SNAKE_CASE		     =       batch_size
						__SCREAMING_SNAKE_CASE		     =       seq_length
						__SCREAMING_SNAKE_CASE		     =       act_dim
						__SCREAMING_SNAKE_CASE		     =       state_dim
						__SCREAMING_SNAKE_CASE		     =       hidden_size
						__SCREAMING_SNAKE_CASE		     =       max_length
						__SCREAMING_SNAKE_CASE		     =       is_training




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((self.batch_size, self.seq_length, self.state_dim)     )
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((self.batch_size, self.seq_length, self.act_dim)     )
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((self.batch_size, self.seq_length, 1)     )
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((self.batch_size, self.seq_length, 1)     )
						__SCREAMING_SNAKE_CASE		     =       ids_tensor((self.batch_size, self.seq_length)    ,    vocab_size=1_0_0_0     )
						__SCREAMING_SNAKE_CASE		     =       random_attention_mask((self.batch_size, self.seq_length)     )

						__SCREAMING_SNAKE_CASE		     =       self.get_config()

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




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:
						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    :       Dict    ,    UpperCAmelCase__    :       Optional[Any]    ,    UpperCAmelCase__    :       Optional[Any]    ,    UpperCAmelCase__    :       Optional[Any]    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       List[str]    ,    ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       DecisionTransformerModel(config=UpperCAmelCase__     )
						model.to(UpperCAmelCase__     )
						model.eval()
						__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )

						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    :       Optional[int]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.prepare_config_and_inputs()
						(
						    (
						    __SCREAMING_SNAKE_CASE					
						)     ,						(
						    __SCREAMING_SNAKE_CASE					
						)     ,						(
						    __SCREAMING_SNAKE_CASE					
						)     ,						(
						    __SCREAMING_SNAKE_CASE					
						)     ,						(
						    __SCREAMING_SNAKE_CASE					
						)     ,						(
						    __SCREAMING_SNAKE_CASE					
						)     ,						(
						    __SCREAMING_SNAKE_CASE					
						)     ,											
						)		     =       config_and_inputs
						__SCREAMING_SNAKE_CASE		     =       {
						    "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_			(				lowercase_    ,						lowercase_    ,						lowercase_    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Optional[int]					      =    (DecisionTransformerModel,) if is_torch_available() else ()
	snake_case__       :						List[str]					      =    ()
	snake_case__       :						List[Any]					      =    {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

	# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
	snake_case__       :						Optional[int]					      =    False

	# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
	snake_case__       :						Tuple					      =    False
	snake_case__       :						Optional[int]					      =    False
	snake_case__       :						int					      =    False
	snake_case__       :						Optional[Any]					      =    False
	snake_case__       :						Optional[Any]					      =    False
	snake_case__       :						int					      =    False
	snake_case__       :						str					      =    False
	snake_case__       :						Union[str, Any]					      =    False
	snake_case__       :						Optional[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       DecisionTransformerModelTester(self     )
						__SCREAMING_SNAKE_CASE		     =       ConfigTester(self    ,    config_class=UpperCAmelCase__    ,    hidden_size=3_7     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						self.config_tester.run_common_tests()




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase__     )




	@slow
	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Optional[int]:
						for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__SCREAMING_SNAKE_CASE		     =       DecisionTransformerModel.from_pretrained(UpperCAmelCase__     )
											self.assertIsNotNone(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			int:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.model_tester.prepare_config_and_inputs_for_common()

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

											__SCREAMING_SNAKE_CASE		     =       [
											    "states",
											    "actions",
											    "rewards",
											    "returns_to_go",
											    "timesteps",
											    "attention_mask",
											]

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




@require_torch
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	@slow
	def 						UpperCAmelCase_   (				self    :       Any     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       2  # number of steps of autoregressive prediction we will perform
						__SCREAMING_SNAKE_CASE		     =       1_0  # defined by the RL environment, may be normalized
						__SCREAMING_SNAKE_CASE		     =       DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"     )
						__SCREAMING_SNAKE_CASE		     =       model.to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       model.config
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       torch.randn(1    ,    1    ,    config.state_dim     ).to(device=UpperCAmelCase__    ,    dtype=torch.floataa     )  # env.reset()

						__SCREAMING_SNAKE_CASE		     =       torch.tensor(
						    [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]    ,    device=UpperCAmelCase__     )

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

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

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

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

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

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




"""simple docstring"""







from __future__ import annotations

import math




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       int     ) ->			None:
						__SCREAMING_SNAKE_CASE		     =       size
						# approximate the overall size of segment tree with given value
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]
						# create array to store lazy update
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]  # flag for lazy update




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2 + 1




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       list[int]     ) ->			None:
						if left_element == right_element:
											__SCREAMING_SNAKE_CASE		     =       a[left_element - 1]
						else:
											__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
											self.build(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											self.build(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       max(
											    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			bool:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True

						if right_element < a or left_element > b:
											return True
						if left_element >= a and right_element <= b:
											__SCREAMING_SNAKE_CASE		     =       val
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True
											return True
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						self.update(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						self.update(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       max(
						    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )
						return True




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			int | float:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True
						if right_element < a or left_element > b:
											return -math.inf
						if left_element >= a and right_element <= b:
											return self.segment_tree[idx]
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						__SCREAMING_SNAKE_CASE		     =       self.query(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.query(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						return max(UpperCAmelCase__    ,    UpperCAmelCase__     )




	def __str__(				self    :       int     ) ->			str:
						return str([self.query(1    ,    1    ,    self.size    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ) for i in range(1    ,    self.size + 1     )]     )


if __name__ == "__main__":
		a__       :							Tuple        =  [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
		a__       :							Dict        =  1_5
		a__       :							int        =  SegmentTree(size)
		segt.build(1, 1, size, A)
		print(segt.query(1, 1, size, 4, 6))
		print(segt.query(1, 1, size, 7, 1_1))
		print(segt.query(1, 1, size, 7, 1_2))
		segt.update(1, 1, size, 1, 3, 1_1_1)
		print(segt.query(1, 1, size, 1, 1_5))
		segt.update(1, 1, size, 7, 8, 2_3_5)
		print(segt)
