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				:      str							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Dict=13							,							__lowerCamelCase				:      List[str]=7							,							__lowerCamelCase				:      List[str]=6							,							__lowerCamelCase				:      Tuple=17							,							__lowerCamelCase				:      Union[str, Any]=23							,							__lowerCamelCase				:      Dict=11							,							__lowerCamelCase				:      Union[str, Any]=True							,							)							->		Dict:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		seq_length
									lowercase							=		act_dim
									lowercase							=		state_dim
									lowercase							=		hidden_size
									lowercase							=		max_length
									lowercase							=		is_training

						def        __a		(	self				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									lowercase							=		floats_tensor((self.batch_size, self.seq_length, self.state_dim)   )
									lowercase							=		floats_tensor((self.batch_size, self.seq_length, self.act_dim)   )
									lowercase							=		floats_tensor((self.batch_size, self.seq_length, 1)   )
									lowercase							=		floats_tensor((self.batch_size, self.seq_length, 1)   )
									lowercase							=		ids_tensor((self.batch_size, self.seq_length)							,							vocab_size=10_00   )
									lowercase							=		random_attention_mask((self.batch_size, self.seq_length)   )

									lowercase							=		self.get_config()

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

						def        __a		(	self				:      Optional[int]   )							->		int:


									'''simple docstring'''

									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				:      List[str]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Optional[Any]							,							)							->		List[str]:


									'''simple docstring'''

									lowercase							=		DecisionTransformerModel(config=__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()
									lowercase							=		model(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

									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				:      List[Any]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.prepare_config_and_inputs()
									(
									    (
									    lowercase
									)		,(
									    lowercase
									)		,(
									    lowercase
									)		,(
									    lowercase
									)		,(
									    lowercase
									)		,(
									    lowercase
									)		,(
									    lowercase
									)		,
									)							=		config_and_inputs
									lowercase							=		{
									    '''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					(    _A       ,     _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (DecisionTransformerModel,) if is_torch_available() else ()
						lowercase					 =     ()
						lowercase					 =     {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

						# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      Optional[Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		DecisionTransformerModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase							,							hidden_size=37   )

						def        __a		(	self				:      str   )							->		Optional[int]:


									'''simple docstring'''

									self.config_tester.run_common_tests()

						def        __a		(	self				:      Any   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase   )

						@slow
						def        __a		(	self				:      Any   )							->		str:


									'''simple docstring'''

									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase							=		DecisionTransformerModel.from_pretrained(__lowerCamelCase   )
												self.assertIsNotNone(__lowerCamelCase   )



						def        __a		(	self				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

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

												lowercase							=		[
												    '''states''',
												    '''actions''',
												    '''rewards''',
												    '''returns_to_go''',
												    '''timesteps''',
												    '''attention_mask''',
												]

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






@require_torch
class      __lowercase					(    unittest.TestCase    ):

						@slow
						def        __a		(	self				:      str   )							->		Optional[int]:


									'''simple docstring'''

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

									lowercase							=		torch.tensor(
									    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]							,							device=__lowerCamelCase   )

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

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

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

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

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

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






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

A_      =      TypeVar("KEY")
A_      =      TypeVar("VAL")






@dataclass(frozen=_A       ,     slots=_A    )
class      __lowercase					(    Generic[KEY, VAL]    ):
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _Item    ):

						def __init__(	self				:      Optional[int]   )							->		None:


									'''simple docstring'''

									super().__init__(__lowerCamelCase							,							__lowerCamelCase   )



						def __bool__(	self				:      str   )							->		bool:


									'''simple docstring'''

									return False





A_      =      _DeletedItem()






class      __lowercase					(    MutableMapping[KEY, VAL]    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      int = 8							,							__lowerCamelCase				:      float = 0.75   )							->		None:


									'''simple docstring'''

									lowercase							=		initial_block_size
									lowercase							=		[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase							=		capacity_factor
									lowercase							=		0

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KEY   )							->		int:


									'''simple docstring'''

									return hash(__lowerCamelCase   ) % len(self._buckets   )

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      int   )							->		int:


									'''simple docstring'''

									return (ind + 1) % len(self._buckets   )

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		bool:


									'''simple docstring'''

									lowercase							=		self._buckets[ind]
									if not stored:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												self._len += 1
												return True
									elif stored.key == key:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												return True
									else:
												return False

						def        __a		(	self				:      Optional[Any]   )							->		bool:


									'''simple docstring'''

									lowercase							=		len(self._buckets   ) * self._capacity_factor
									return len(self   ) >= int(__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		bool:


									'''simple docstring'''

									if len(self._buckets   ) <= self._initial_block_size:
												return False
									lowercase							=		len(self._buckets   ) * self._capacity_factor / 2
									return len(self   ) < limit

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		self._buckets
									lowercase							=		[None] * new_size
									lowercase							=		0
									for item in old_buckets:
												if item:
															self._add_item(item.key							,							item.val   )

						def        __a		(	self				:      List[str]   )							->		None:


									'''simple docstring'''

									self._resize(len(self._buckets   ) * 2   )

						def        __a		(	self				:      Any   )							->		None:


									'''simple docstring'''

									self._resize(len(self._buckets   ) // 2   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY   )							->		Iterator[int]:


									'''simple docstring'''

									lowercase							=		self._get_bucket_index(__lowerCamelCase   )
									for _ in range(len(self._buckets   )   ):
												yield ind
												lowercase							=		self._get_next_ind(__lowerCamelCase   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												if self._try_set(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   ):
															break

						def __setitem__(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

									if self._is_full():
												self._size_up()

									self._add_item(__lowerCamelCase							,							__lowerCamelCase   )

						def __delitem__(	self				:      List[str]							,							__lowerCamelCase				:      KEY   )							->		None:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												lowercase							=		self._buckets[ind]
												if item is None:
															raise KeyError(__lowerCamelCase   )
												if item is _deleted:
															continue
												if item.key == key:
															lowercase							=		_deleted
															self._len -= 1
															break
									if self._is_sparse():
												self._size_down()

						def __getitem__(	self				:      Any							,							__lowerCamelCase				:      KEY   )							->		VAL:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												lowercase							=		self._buckets[ind]
												if item is None:
															break
												if item is _deleted:
															continue
												if item.key == key:
															return item.val
									raise KeyError(__lowerCamelCase   )

						def __len__(	self				:      str   )							->		int:


									'''simple docstring'''

									return self._len

						def __iter__(	self				:      Union[str, Any]   )							->		Iterator[KEY]:


									'''simple docstring'''

									yield from (item.key for item in self._buckets if item)



						def __repr__(	self				:      Any   )							->		str:


									'''simple docstring'''

									lowercase							=		''' ,'''.join(
									    f'{item.key}: {item.val}' for item in self._buckets if item   )
									return f'HashMap({val_string})'





