





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   :    Optional[int]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=13							,	__lowerCAmelCase   :    int=7							,	__lowerCAmelCase   :    Optional[int]=6							,	__lowerCAmelCase   :    List[str]=17							,	__lowerCAmelCase   :    Tuple=23							,	__lowerCAmelCase   :    Optional[int]=11							,	__lowerCAmelCase   :    Any=True							,	)	->	str:







					"""simple docstring"""






					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   :    str   )	->	Optional[int]:







					"""simple docstring"""






					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=10_00   )
					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   :    List[Any]   )	->	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   :    Optional[Any]							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Dict							,	)	->	Optional[Any]:







					"""simple docstring"""






					A__        =   DecisionTransformerModel(config=__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					A__        =   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   :    Any   )	->	str:







					"""simple docstring"""






					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   A			(SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     unittest.TestCase    ):
			'''simple docstring'''




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

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

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






			def    a_       (			self   :    Optional[int]   )	->	Dict:







					"""simple docstring"""






					A__        =   DecisionTransformerModelTester(self   )
					A__        =   ConfigTester(self							,	config_class=__lowerCAmelCase							,	hidden_size=37   )






			def    a_       (			self   :    List[Any]   )	->	Tuple:







					"""simple docstring"""






					self.config_tester.run_common_tests()






			def    a_       (			self   :    Dict   )	->	Dict:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase   )






			@slow
			def    a_       (			self   :    Union[str, Any]   )	->	Optional[Any]:







					"""simple docstring"""






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







			def    a_       (			self   :    int   )	->	int:







					"""simple docstring"""






					A__      ,			A__        =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							A__        =   model_class(__lowerCAmelCase   )
							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(__lowerCAmelCase   )]							,	__lowerCAmelCase   )


@require_torch
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			@slow
			def    a_       (			self   :    Dict   )	->	str:







					"""simple docstring"""






					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(__lowerCAmelCase   )
					A__        =   model.config
					torch.manual_seed(0   )
					A__        =   torch.randn(1							,	1							,	config.state_dim   ).to(device=__lowerCAmelCase							,	dtype=torch.floataa   )  # env.reset()

					A__        =   torch.tensor(
					    [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]							,	device=__lowerCAmelCase   )

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

					for step in range(__lowerCAmelCase   ):
							A__        =   torch.cat([actions, torch.zeros(1							,	1							,	config.act_dim							,	device=__lowerCAmelCase   )]							,	dim=1   )
							A__        =   torch.cat([rewards, torch.zeros(1							,	1							,	device=__lowerCAmelCase   )]							,	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=__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   )   )
							A__      ,			A__      ,			A__      ,			A__        =   (  # env.step(action)
							    torch.randn(1							,	1							,	config.state_dim   ).to(device=__lowerCAmelCase							,	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=__lowerCAmelCase							,	dtype=torch.long   ) * (step + 1)]							,	dim=1   )










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					int =      {
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any							=    '''xlm-roberta'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=3_05_22							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Tuple=12							,	__lowerCAmelCase   :    str=12							,	__lowerCAmelCase   :    Union[str, Any]=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    Tuple=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-12							,	__lowerCAmelCase   :    Union[str, Any]=1							,	__lowerCAmelCase   :    str=0							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Tuple="absolute"							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    str							,	)	->	Optional[Any]:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )

					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   hidden_act
					A__        =   intermediate_size
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   initializer_range
					A__        =   layer_norm_eps
					A__        =   position_embedding_type
					A__        =   use_cache
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__        =   {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__        =   {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]   )



