



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  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=13  ,  a_=7  ,  a_=6  ,  a_=17  ,  a_=23  ,  a_=11  ,  a_=True  ,  ):
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		seq_length
											a__				      =		act_dim
											a__				      =		state_dim
											a__				      =		hidden_size
											a__				      =		max_length
											a__				      =		is_training


							def _a      (							self		):
											a__				      =		floats_tensor((self.batch_size, self.seq_length, self.state_dim)		)
											a__				      =		floats_tensor((self.batch_size, self.seq_length, self.act_dim)		)
											a__				      =		floats_tensor((self.batch_size, self.seq_length, 1)		)
											a__				      =		floats_tensor((self.batch_size, self.seq_length, 1)		)
											a__				      =		ids_tensor((self.batch_size, self.seq_length)  ,  vocab_size=1_000		)
											a__				      =		random_attention_mask((self.batch_size, self.seq_length)		)

											a__				      =		self.get_config()

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


							def _a      (							self		):
											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 _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  ):
											a__				      =		DecisionTransformerModel(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		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 _a      (							self		):
											a__				      =		self.prepare_config_and_inputs()
											(
											    (
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			
											)				      =		config_and_inputs
											a__				      =		{
											    """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  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






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

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

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


							def _a      (							self		):
											a__				      =		DecisionTransformerModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=a_  ,  hidden_size=37		)


							def _a      (							self		):
											self.config_tester.run_common_tests()


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a_		)


							@slow
							def _a      (							self		):
											for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__				      =		DecisionTransformerModel.from_pretrained(a_		)
															self.assertIsNotNone(a_		)







							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

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

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

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




@require_torch
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							@slow
							def _a      (							self		):
											a__				      =		2  # number of steps of autoregressive prediction we will perform
											a__				      =		10  # defined by the RL environment, may be normalized
											a__				      =		DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""		)
											a__				      =		model.to(a_		)
											a__				      =		model.config
											torch.manual_seed(0		)
											a__				      =		torch.randn(1  ,  1  ,  config.state_dim		).to(device=a_  ,  dtype=torch.floataa		)  # env.reset()

											a__				      =		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=a_		)

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

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

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

															with torch.no_grad():
																			a__      ,			a__      ,			a__				      =		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		)		)
															a__      ,			a__      ,			a__      ,			a__				      =		(  # env.step(action)
															    torch.randn(1  ,  1  ,  config.state_dim		).to(device=a_  ,  dtype=torch.floataa		),
															    1.0,
															    False,
															    {},
															)

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






def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head:
								return True
				# split the list to two parts
				a__      ,			a__				      =		head.next, head
				while fast and fast.next:
								a__				      =		fast.next.next
								a__				      =		slow.next
				a__				      =		slow.next
				a__				      =		None  # Don't forget here! But forget still works!
				# reverse the second part
				a__				      =		None
				while second:
								a__				      =		second.next
								a__				      =		node
								a__				      =		second
								a__				      =		nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								a__				      =		node.next
								a__				      =		head.next
				return True

def 			A_      (	__a     :				int		):

				"""simple docstring"""
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				a__				      =		a__				      =		a__				      =		head
				while fast and fast.next:
								a__      ,			a__				      =		fast.next.next, slow.next

				# 2. Push the second half into the stack
				a__				      =		[slow.val]
				while slow.next:
								a__				      =		slow.next
								stack.append(slow.val		)

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								a__				      =		cur.next

				return True




def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head or not head.next:
								return True
				a__				      =		{}
				a__				      =		0
				while head:
								if head.val in d:
												d[head.val].append(__a		)
								else:
												a__				      =		[pos]
								a__				      =		head.next
								pos += 1
				a__				      =		pos - 1
				a__				      =		0
				for v in d.values():
								if len(__a		) % 2 != 0:
												middle += 1
								else:
												a__				      =		0
												for i in range(0 ,		len(__a		)		):
																if v[i] + v[len(__a		) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True

