


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

import pytest
from parameterized import parameterized, 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.p3.16xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]			)
class        _a							(			unittest.TestCase			):

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





									"""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=UpperCamelCase_    ,						)
									assert hasattr(self    ,						'''env'''							)

							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Tuple							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
									# distributed data settings
									lowercase__            =      {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

									# creates estimator
									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=UpperCamelCase_    ,						instance_count=UpperCamelCase_    ,						instance_type=self.instance_type    ,						debugger_hook_config=UpperCamelCase_    ,						hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}    ,						metric_definitions=self.env.metric_definitions    ,						distribution=UpperCamelCase_    ,						py_version='''py36'''    ,						)

							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	Optional[int]							)			->   List[str]:





									"""simple docstring"""







									TrainingJobAnalytics(UpperCamelCase_							).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'							)


							@parameterized.expand([(2,)]							)
							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      self.create_estimator(UpperCamelCase_							)

									# run training
									estimator.fit()

									# result dataframe
									lowercase__            =      TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

									# extract kpis
									lowercase__            =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']							)
									lowercase__            =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']							)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowercase__            =      (
									    Session().describe_training_job(estimator.latest_training_job.name							).get('''TrainingTimeInSeconds'''    ,						999_999							)
									)

									# 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}    ,						UpperCamelCase_							)






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							:

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Any=13    ,						UpperCamelCase_:	Optional[Any]=7    ,						UpperCamelCase_:	Optional[Any]=6    ,						UpperCamelCase_:	Any=17    ,						UpperCamelCase_:	str=23    ,						UpperCamelCase_:	List[Any]=11    ,						UpperCamelCase_:	Optional[int]=True    ,						)			->   List[Any]:





									"""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       lowerCamelCase_ (							self:	Optional[Any]							)			->   Tuple:





									"""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=1_000							)
									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       lowerCamelCase_ (							self:	Dict							)			->   List[Any]:





									"""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:	List[str]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int    ,						)			->   Dict:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModel(config=UpperCamelCase_							)
									model.to(UpperCamelCase_							)
									model.eval()
									lowercase__            =      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       lowerCamelCase_ (							self:	List[Any]							)			->   str:





									"""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        _a							(			UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			str        = (DecisionTransformerModel,) if is_torch_available() else ()
							_lowercase	:			List[str]        = ()
							_lowercase	:			List[Any]        = {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModelTester(self							)
									lowercase__            =      ConfigTester(self    ,						config_class=UpperCamelCase_    ,						hidden_size=37							)

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





									"""simple docstring"""







									self.config_tester.run_common_tests()

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





									"""simple docstring"""







									lowercase__            =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase_							)

							@slow
							def       lowerCamelCase_ (							self:	Dict							)			->   Optional[Any]:





									"""simple docstring"""







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


							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											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(UpperCamelCase_							)]    ,						UpperCamelCase_							)


@require_torch
class        _a							(			unittest.TestCase			):

							@slow
							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





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

									lowercase__            =      torch.tensor(
									    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]    ,						device=UpperCamelCase_							)

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

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


