






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   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=1_3				,		_UpperCamelCase=7				,		_UpperCamelCase=6				,		_UpperCamelCase=1_7				,		_UpperCamelCase=2_3				,		_UpperCamelCase=1_1				,		_UpperCamelCase=True				,		)	->							int:
							UpperCAmelCase_    :      Dict            =						parent
							UpperCAmelCase_    :      str            =						batch_size
							UpperCAmelCase_    :      List[Any]            =						seq_length
							UpperCAmelCase_    :      Any            =						act_dim
							UpperCAmelCase_    :      Dict            =						state_dim
							UpperCAmelCase_    :      List[str]            =						hidden_size
							UpperCAmelCase_    :      List[Any]            =						max_length
							UpperCAmelCase_    :      Optional[Any]            =						is_training



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						floats_tensor((self.batch_size, self.seq_length, self.state_dim)   )
							UpperCAmelCase_    :      Optional[Any]            =						floats_tensor((self.batch_size, self.seq_length, self.act_dim)   )
							UpperCAmelCase_    :      Tuple            =						floats_tensor((self.batch_size, self.seq_length, 1)   )
							UpperCAmelCase_    :      Optional[Any]            =						floats_tensor((self.batch_size, self.seq_length, 1)   )
							UpperCAmelCase_    :      str            =						ids_tensor((self.batch_size, self.seq_length)				,		vocab_size=1_0_0_0   )
							UpperCAmelCase_    :      Optional[Any]            =						random_attention_mask((self.batch_size, self.seq_length)   )

							UpperCAmelCase_    :      Dict            =						self.get_config()

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



		def 					__UpperCAmelCase			(							self   )	->							Optional[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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							List[str]:
							UpperCAmelCase_    :      List[str]            =						DecisionTransformerModel(config=_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      Dict            =						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 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						self.prepare_config_and_inputs()
							(
							    (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  (
							    UpperCAmelCase_
							)       ,  
							)    :      List[str]            =						config_and_inputs
							UpperCAmelCase_    :      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   lowerCamelCase      (_snake_case ,  _snake_case ,  _snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    int       						=  (DecisionTransformerModel,) if is_torch_available() else ()
		_snake_case    :    Tuple       						=  ()
		_snake_case    :    Any       						=  {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      str            =						DecisionTransformerModelTester(self   )
							UpperCAmelCase_    :      str            =						ConfigTester(self				,		config_class=_UpperCamelCase				,		hidden_size=3_7   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							self.config_tester.run_common_tests()



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      Any            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase   )



		@slow
		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_    :      int            =						DecisionTransformerModel.from_pretrained(_UpperCamelCase   )
												self.assertIsNotNone(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_    :      List[Any]            =						model_class(_UpperCamelCase   )
												UpperCAmelCase_    :      Optional[int]            =						inspect.signature(model.forward   )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCAmelCase_    :      Union[str, Any]            =						[*signature.parameters.keys()]

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

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





@require_torch
class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		@slow
		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Optional[Any]            =						2  # number of steps of autoregressive prediction we will perform
							UpperCAmelCase_    :      Optional[Any]            =						1_0  # defined by the RL environment, may be normalized
							UpperCAmelCase_    :      Tuple            =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'   )
							UpperCAmelCase_    :      int            =						model.to(_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						model.config
							torch.manual_seed(0   )
							UpperCAmelCase_    :      str            =						torch.randn(1				,		1				,		config.state_dim   ).to(device=_UpperCamelCase				,		dtype=torch.floataa   )  # env.reset()

							UpperCAmelCase_    :      Tuple            =						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   )

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

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

												UpperCAmelCase_    :      List[str]            =						torch.ones(1				,		states.shape[1]   ).to(dtype=torch.long				,		device=states.device   )

												with torch.no_grad():
																	UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						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   )   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						(  # env.step(action)
												    torch.randn(1				,		1				,		config.state_dim   ).to(device=_UpperCamelCase				,		dtype=torch.floataa   ),
												    1.0,
												    False,
												    {},
												)

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











"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'  ,		'False'							)							) is not True  ,		reason='Skipping test because should only be run when releasing minor transformers version'  ,		)
@pytest.mark.usefixtures('sm_env'							)
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]							)
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

				"""simple docstring"""

				if self.framework == "pytorch":
							subprocess.run(
							    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()       ,			encoding="utf-8"       ,			check=_SCREAMING_SNAKE_CASE       ,			)
				assert hasattr(self       ,			"env")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Any=1)				->       Dict:

				"""simple docstring"""

				return HuggingFace(
				    entry_point=self.script       ,			source_dir=self.env.test_path       ,			role=self.env.role       ,			image_uri=self.env.image_uri       ,			base_job_name=F"""{self.env.base_job_name}-single"""       ,			instance_count=_SCREAMING_SNAKE_CASE       ,			instance_type=self.instance_type       ,			debugger_hook_config=_SCREAMING_SNAKE_CASE       ,			hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,			metric_definitions=self.env.metric_definitions       ,			py_version="py36"       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







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

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator()

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Tuple												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Optional[Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
				__lowerCAmelCase      :  Union[str, Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__lowerCAmelCase      :  Tuple												=       (
				    Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds"       ,			99_9999)
				)

				# assert kpis
				assert train_runtime <= self.results["train_runtime"]
				assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
				assert all(t <= self.results["eval_loss"] for t in eval_loss)

				# dump tests result into json file to share in PR
				with open(F"""{estimator.latest_training_job.name}.json"""       ,			"w") as outfile:
							json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}       ,			_SCREAMING_SNAKE_CASE)