






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 							__a							:





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__=13 ,    UpperCamelCase__=7 ,    UpperCamelCase__=6 ,    UpperCamelCase__=17 ,    UpperCamelCase__=23 ,    UpperCamelCase__=11 ,    UpperCamelCase__=True ,    ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   parent
						SCREAMING_SNAKE_CASE_   :	Any													=   batch_size
						SCREAMING_SNAKE_CASE_   :	List[str]													=   seq_length
						SCREAMING_SNAKE_CASE_   :	Dict													=   act_dim
						SCREAMING_SNAKE_CASE_   :	List[str]													=   state_dim
						SCREAMING_SNAKE_CASE_   :	Tuple													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	List[str]													=   max_length
						SCREAMING_SNAKE_CASE_   :	List[str]													=   is_training


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   floats_tensor((self.batch_size, self.seq_length, self.state_dim)       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   floats_tensor((self.batch_size, self.seq_length, self.act_dim)       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   floats_tensor((self.batch_size, self.seq_length, 1)       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   floats_tensor((self.batch_size, self.seq_length, 1)       )
						SCREAMING_SNAKE_CASE_   :	str													=   ids_tensor((self.batch_size, self.seq_length) ,    vocab_size=1000       )
						SCREAMING_SNAKE_CASE_   :	str													=   random_attention_mask((self.batch_size, self.seq_length)       )

						SCREAMING_SNAKE_CASE_   :	int													=   self.get_config()

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


		def   __snake_case			(						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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   DecisionTransformerModel(config=UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()
						SCREAMING_SNAKE_CASE_   :	Any													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   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_
						)							,       
						)   :	Dict													=   config_and_inputs
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {
						    '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 							__a							(  __A ,	__A ,	__A ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Dict				    =						(DecisionTransformerModel,) if is_torch_available() else ()
		UpperCAmelCase__      :				List[str]				    =						()
		UpperCAmelCase__      :				str				    =						{"""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__      :				Tuple				    =						False
		UpperCAmelCase__      :				List[Any]				    =						False
		UpperCAmelCase__      :				str				    =						False
		UpperCAmelCase__      :				Union[str, Any]				    =						False
		UpperCAmelCase__      :				Any				    =						False
		UpperCAmelCase__      :				Union[str, Any]				    =						False
		UpperCAmelCase__      :				str				    =						False
		UpperCAmelCase__      :				Optional[int]				    =						False
		UpperCAmelCase__      :				Optional[Any]				    =						False


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   DecisionTransformerModelTester(self       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ConfigTester(self ,    config_class=UpperCamelCase__ ,    hidden_size=37       )


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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase__       )


		@slow
		def   __snake_case			(						self       ):
						for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   :	Any													=   DecisionTransformerModel.from_pretrained(UpperCamelCase__       )
										self.assertIsNotNone(UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.model_tester.prepare_config_and_inputs_for_common()

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

										SCREAMING_SNAKE_CASE_   :	List[str]													=   [
										    'states',
										    'actions',
										    'rewards',
										    'returns_to_go',
										    'timesteps',
										    'attention_mask',
										]

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






@require_torch
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.tensor(
						    [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] ,    device=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor(UpperCamelCase__ ,    device=UpperCamelCase__ ,    dtype=torch.floataa       ).reshape(1 ,    1 ,    1       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   state
						SCREAMING_SNAKE_CASE_   :	Any													=   torch.zeros(1 ,    0 ,    config.act_dim ,    device=UpperCamelCase__ ,    dtype=torch.floataa       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.zeros(1 ,    0 ,    device=UpperCamelCase__ ,    dtype=torch.floataa       )
						SCREAMING_SNAKE_CASE_   :	Any													=   torch.tensor(0 ,    device=UpperCamelCase__ ,    dtype=torch.long       ).reshape(1 ,    1       )

						for step in range(UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	str													=   torch.cat([actions, torch.zeros(1 ,    1 ,    config.act_dim ,    device=UpperCamelCase__       )] ,    dim=1       )
										SCREAMING_SNAKE_CASE_   :	Dict													=   torch.cat([rewards, torch.zeros(1 ,    1 ,    device=UpperCamelCase__       )] ,    dim=1       )

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   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_   :	List[str]													=   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_   :	str													=   (  # env.step(action)
										    torch.randn(1 ,    1 ,    config.state_dim       ).to(device=UpperCamelCase__ ,    dtype=torch.floataa       ),
										    1.0,
										    False,
										    {},
										)

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







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""Translation""" ,	init=__A ,	repr=__A						)


		def __call__(						self       ):
						return pa.struct({lang: pa.string() for lang in sorted(self.languages       )}       )






		def   __snake_case			(						self       ):
						from .features import Value

						return {k: Value('string'       ) for k in sorted(self.languages       )}






@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[List]				    =						None
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""TranslationVariableLanguages""" ,	init=__A ,	repr=__A						)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   sorted(set(self.languages       )       ) if self.languages else None
						SCREAMING_SNAKE_CASE_   :	Dict													=   len(self.languages       ) if self.languages else None


		def __call__(						self       ):
						return pa.struct({'language': pa.list_(pa.string()       ), 'translation': pa.list_(pa.string()       )}       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   set(self.languages       )
						if self.languages and set(UpperCamelCase__       ) - lang_set:
										raise ValueError(
										    F'''Some languages in example ({', '.join(sorted(set(UpperCamelCase__       ) - lang_set       )       )}) are not in valid set ({', '.join(UpperCamelCase__       )}).'''       )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						SCREAMING_SNAKE_CASE_   :	List[str]													=   []
						for lang, text in translation_dict.items():
										if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
														translation_tuples.append((lang, text)       )
										else:
														translation_tuples.extend([(lang, el) for el in text]       )

        # Ensure translations are in ascending order by language code.
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   zip(*sorted(UpperCamelCase__       )       )

						return {"language": languages, "translation": translations}






		def   __snake_case			(						self       ):
						from .features import Sequence, Value

						return {
						    "language": Sequence(Value('string'       )       ),
						    "translation": Sequence(Value('string'       )       ),
						}