






"""simple docstring"""



from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   float | Decimal    ,						lowerCamelCase__     :   float = 10**-10  )    ->     float:
							lowerCamelCase_	:							str									=a
							while True:
														lowerCamelCase_	:							Union[str, Any]									=Decimal(lowerCamelCase__  ) - (
														    Decimal(eval(lowerCamelCase__  )  ) / Decimal(eval(str(diff(lowerCamelCase__  )  )  )  )  # noqa: S307
														)
														# This number dictates the accuracy of the answer
														if abs(eval(lowerCamelCase__  )  ) < precision:  # noqa: S307
																					return float(lowerCamelCase__  )


# Let's Execute
if __name__ == "__main__":
						# Find root of trigonometric function
						# Find value of pi
						print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
						# Find root of polynomial
						print(f'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
						# Find Square Root of 5
						print(f'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
						# Exponential Roots
						print(f'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')











"""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        lowercase__      :


			def __init__(				self					:  List[Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Tuple=13       ,	snake_case__					:  str=7       ,	snake_case__					:  Union[str, Any]=6       ,	snake_case__					:  str=17       ,	snake_case__					:  Any=23       ,	snake_case__					:  int=11       ,	snake_case__					:  Tuple=True       ,	):
										lowerCamelCase_	:							str									=parent
										lowerCamelCase_	:							Union[str, Any]									=batch_size
										lowerCamelCase_	:							List[Any]									=seq_length
										lowerCamelCase_	:							Union[str, Any]									=act_dim
										lowerCamelCase_	:							Optional[Any]									=state_dim
										lowerCamelCase_	:							Optional[Any]									=hidden_size
										lowerCamelCase_	:							Tuple									=max_length
										lowerCamelCase_	:							List[Any]									=is_training


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
										lowerCamelCase_	:							List[Any]									=floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase_	:							List[Any]									=ids_tensor((self.batch_size, self.seq_length)       ,	vocab_size=1000 )
										lowerCamelCase_	:							Optional[int]									=random_attention_mask((self.batch_size, self.seq_length) )

										lowerCamelCase_	:							List[str]									=self.get_config()

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


			def      UpperCAmelCase__						(				self					:  Any ):
										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					:  Optional[Any]       ,	snake_case__					:  Tuple       ,	snake_case__					:  Dict       ,	snake_case__					:  int       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  List[str]       ,	snake_case__					:  List[str]       ,	):
										lowerCamelCase_	:							Tuple									=DecisionTransformerModel(config=snake_case__ )
										model.to(snake_case__ )
										model.eval()
										lowerCamelCase_	:							str									=model(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__ )

										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					:  List[str] ):
										lowerCamelCase_	:							List[str]									=self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						
										)	:							Optional[int]									=config_and_inputs
										lowerCamelCase_	:							Optional[int]									={
										    "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        lowercase__      (							snake_case__,       snake_case__,       snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Optional[Any]             =			(DecisionTransformerModel,) if is_torch_available() else ()
			_UpperCAmelCase		:int             =			()
			_UpperCAmelCase		:int             =			{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

			# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
			_UpperCAmelCase		:Union[str, Any]             =			False

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


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Dict									=DecisionTransformerModelTester(self )
										lowerCamelCase_	:							str									=ConfigTester(self       ,	config_class=snake_case__       ,	hidden_size=37 )


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										self.config_tester.run_common_tests()


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Tuple									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__ )


			@slow
			def      UpperCAmelCase__						(				self					:  List[str] ):
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_	:							str									=DecisionTransformerModel.from_pretrained(snake_case__ )
																	self.assertIsNotNone(snake_case__ )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_							,						lowerCamelCase_	:							Dict									=self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowerCamelCase_	:							List[str]									=[
																	    "states",
																	    "actions",
																	    "rewards",
																	    "returns_to_go",
																	    "timesteps",
																	    "attention_mask",
																	]

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







@require_torch
class        lowercase__      (							unittest.TestCase       ):


			@slow
			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Optional[int]									=2  # number of steps of autoregressive prediction we will perform
										lowerCamelCase_	:							int									=10  # defined by the RL environment, may be normalized
										lowerCamelCase_	:							List[Any]									=DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert" )
										lowerCamelCase_	:							Union[str, Any]									=model.to(snake_case__ )
										lowerCamelCase_	:							Any									=model.config
										torch.manual_seed(0 )
										lowerCamelCase_	:							Optional[Any]									=torch.randn(1       ,	1       ,	config.state_dim ).to(device=snake_case__       ,	dtype=torch.floataa )  # env.reset()

										lowerCamelCase_	:							Optional[Any]									=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=snake_case__ )

										lowerCamelCase_	:							int									=torch.tensor(snake_case__       ,	device=snake_case__       ,	dtype=torch.floataa ).reshape(1       ,	1       ,	1 )
										lowerCamelCase_	:							str									=state
										lowerCamelCase_	:							Optional[int]									=torch.zeros(1       ,	0       ,	config.act_dim       ,	device=snake_case__       ,	dtype=torch.floataa )
										lowerCamelCase_	:							int									=torch.zeros(1       ,	0       ,	device=snake_case__       ,	dtype=torch.floataa )
										lowerCamelCase_	:							Tuple									=torch.tensor(0       ,	device=snake_case__       ,	dtype=torch.long ).reshape(1       ,	1 )

										for step in range(snake_case__ ):
																	lowerCamelCase_	:							str									=torch.cat([actions, torch.zeros(1       ,	1       ,	config.act_dim       ,	device=snake_case__ )]       ,	dim=1 )
																	lowerCamelCase_	:							Union[str, Any]									=torch.cat([rewards, torch.zeros(1       ,	1       ,	device=snake_case__ )]       ,	dim=1 )

																	lowerCamelCase_	:							Optional[int]									=torch.ones(1       ,	states.shape[1] ).to(dtype=torch.long       ,	device=states.device )

																	with torch.no_grad():
																								lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Dict									=model(
																								    states=snake_case__       ,	actions=snake_case__       ,	rewards=snake_case__       ,	returns_to_go=snake_case__       ,	timesteps=snake_case__       ,	attention_mask=snake_case__       ,	return_dict=snake_case__       ,	)

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

																	lowerCamelCase_	:							str									=action_pred[0, -1]
																	lowerCamelCase_	:							Optional[int]									=torch.cat([states, state]       ,	dim=1 )
																	lowerCamelCase_	:							Optional[Any]									=returns_to_go[0, -1] - reward
																	lowerCamelCase_	:							str									=torch.cat([returns_to_go, pred_return.reshape(1       ,	1       ,	1 )]       ,	dim=1 )
																	lowerCamelCase_	:							int									=torch.cat(
																	    [timesteps, torch.ones((1, 1)       ,	device=snake_case__       ,	dtype=torch.long ) * (step + 1)]       ,	dim=1 )



