





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       ,  __lowerCAmelCase       ,  __lowerCAmelCase=1_3       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=6       ,  __lowerCAmelCase=1_7       ,  __lowerCAmelCase=2_3       ,  __lowerCAmelCase=1_1       ,  __lowerCAmelCase=True       ,  ):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
				lowerCamelCase__								=		floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
				lowerCamelCase__								=		floats_tensor((self.batch_size, self.seq_length, 1)							)
				lowerCamelCase__								=		floats_tensor((self.batch_size, self.seq_length, 1)							)
				lowerCamelCase__								=		ids_tensor((self.batch_size, self.seq_length)       ,  vocab_size=1_0_0_0							)
				lowerCamelCase__								=		random_attention_mask((self.batch_size, self.seq_length)							)

				lowerCamelCase__								=		self.get_config()

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





			def 		__lowerCamelCase				(    self							):



				'''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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		DecisionTransformerModel(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.prepare_config_and_inputs()
				(
				    (
				    lowerCamelCase__
				)				,	(
				    lowerCamelCase__
				)				,	(
				    lowerCamelCase__
				)				,	(
				    lowerCamelCase__
				)				,	(
				    lowerCamelCase__
				)				,	(
				    lowerCamelCase__
				)				,	(
				    lowerCamelCase__
				)				,	
				)								=		config_and_inputs
				lowerCamelCase__								=		{
				    '''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    (    lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(DecisionTransformerModel,) if is_torch_available() else ()
			lowerCAmelCase_							     =						()
			lowerCAmelCase_							     =						{"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		DecisionTransformerModelTester(self							)
				lowerCamelCase__								=		ConfigTester(self       ,  config_class=__lowerCAmelCase       ,  hidden_size=3_7							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCamelCase__								=		[
					    '''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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		2  # number of steps of autoregressive prediction we will perform
				lowerCamelCase__								=		1_0  # defined by the RL environment, may be normalized
				lowerCamelCase__								=		DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''							)
				lowerCamelCase__								=		model.to(__lowerCAmelCase							)
				lowerCamelCase__								=		model.config
				torch.manual_seed(0							)
				lowerCamelCase__								=		torch.randn(1       ,  1       ,  config.state_dim							).to(device=__lowerCAmelCase       ,  dtype=torch.floataa							)  # env.reset()

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

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

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

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

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

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










import numpy as np

from transformers import Pipeline





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		np.max(__snake_case       ,axis=-1       ,keepdims=__snake_case							)
	lowerCamelCase__								=		np.exp(outputs - maxes							)
	return shifted_exp / shifted_exp.sum(axis=-1       ,keepdims=__snake_case							)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{}
				if "second_text" in kwargs:
					lowerCamelCase__								=		kwargs['''second_text''']
				return preprocess_kwargs, {}, {}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







				return self.tokenizer(__lowerCAmelCase       ,  text_pair=__lowerCAmelCase       ,  return_tensors=self.framework							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.model(**__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		model_outputs.logits[0].numpy()
				lowerCamelCase__								=		softmax(__lowerCAmelCase							)

				lowerCamelCase__								=		np.argmax(__lowerCAmelCase							)
				lowerCamelCase__								=		self.model.config.idalabel[best_class]
				lowerCamelCase__								=		probabilities[best_class].item()
				lowerCamelCase__								=		logits.tolist()
				return {"label": label, "score": score, "logits": logits}



