



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  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=13  ,  a_=7  ,  a_=6  ,  a_=17  ,  a_=23  ,  a_=11  ,  a_=True  ,  ):
											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		):
											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=1_000		)
											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		):
											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  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  ):
											a__				      =		DecisionTransformerModel(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		)

											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		):
											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  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      List[Any]         =					(DecisionTransformerModel,) if is_torch_available() else ()
							UpperCamelCase__				:      str         =					()
							UpperCamelCase__				:      Optional[Any]         =					{"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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


							def _a      (							self		):
											a__				      =		DecisionTransformerModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=a_  ,  hidden_size=37		)


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


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a_		)


							@slow
							def _a      (							self		):
											for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__				      =		DecisionTransformerModel.from_pretrained(a_		)
															self.assertIsNotNone(a_		)







							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															a__				      =		model_class(a_		)
															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(a_		)]  ,  a_		)




@require_torch
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							@slow
							def _a      (							self		):
											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(a_		)
											a__				      =		model.config
											torch.manual_seed(0		)
											a__				      =		torch.randn(1  ,  1  ,  config.state_dim		).to(device=a_  ,  dtype=torch.floataa		)  # env.reset()

											a__				      =		torch.tensor(
											    [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]  ,  device=a_		)

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

											for step in range(a_		):
															a__				      =		torch.cat([actions, torch.zeros(1  ,  1  ,  config.act_dim  ,  device=a_		)]  ,  dim=1		)
															a__				      =		torch.cat([rewards, torch.zeros(1  ,  1  ,  device=a_		)]  ,  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=a_  ,  actions=a_  ,  rewards=a_  ,  returns_to_go=a_  ,  timesteps=a_  ,  attention_mask=a_  ,  return_dict=a_  ,  )

															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=a_  ,  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=a_  ,  dtype=torch.long		) * (step + 1)]  ,  dim=1		)






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def 			A_      (	__a     :				List[str] ,		__a     :				Optional[int]		):

				"""simple docstring"""
				assert isinstance(__a ,		__a		)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory""" ,		[False, True]		)
def 			A_      (	__a     :				Tuple ,		__a     :				Dict ,		__a     :				List[str]		):

				"""simple docstring"""
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								a__				      =		JsonDatasetReader(__a ,		cache_dir=__a ,		keep_in_memory=__a		).read()
				_check_json_dataset(__a ,		__a		)

@pytest.mark.parametrize(
    """features""" ,		[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ] ,		)
def 			A_      (	__a     :				int ,		__a     :				Dict ,		__a     :				List[Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				a__				      =		features.copy() if features else default_expected_features
				a__				      =		(
				    Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
				)
				a__				      =		JsonDatasetReader(__a ,		features=__a ,		cache_dir=__a		).read()
				_check_json_dataset(__a ,		__a		)

@pytest.mark.parametrize(
    """features""" ,		[
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ] ,		)
def 			A_      (	__a     :				Tuple ,		__a     :				Tuple ,		__a     :				Tuple		):

				"""simple docstring"""
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
				a__				      =		features.copy() if features else default_expected_features
				a__				      =		(
				    Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
				)
				a__				      =		JsonDatasetReader(__a ,		features=__a ,		cache_dir=__a		).read()
				assert isinstance(__a ,		__a		)
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_3", "col_1", "col_2"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype

def 			A_      (	__a     :				int ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
				a__				      =		{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
				a__				      =		features.copy()
				a__				      =		(
				    Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
				)
				a__				      =		tmp_path / """cache"""
				a__				      =		JsonDatasetReader(__a ,		features=__a ,		cache_dir=__a		).read()
				assert isinstance(__a ,		__a		)
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_2", "col_3", "col_1"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""split""" ,		[None, NamedSplit("""train"""		), """train""", """test"""]		)
def 			A_      (	__a     :				List[Any] ,		__a     :				int ,		__a     :				List[str]		):

				"""simple docstring"""
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				a__				      =		JsonDatasetReader(__a ,		cache_dir=__a ,		split=__a		).read()
				_check_json_dataset(__a ,		__a		)
				assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type""" ,		[str, list]		)
def 			A_      (	__a     :				Dict ,		__a     :				Tuple ,		__a     :				List[str]		):

				"""simple docstring"""
				if issubclass(__a ,		__a		):
								a__				      =		jsonl_path
				elif issubclass(__a ,		__a		):
								a__				      =		[jsonl_path]
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				a__				      =		JsonDatasetReader(__a ,		cache_dir=__a		).read()
				_check_json_dataset(__a ,		__a		)

def 			A_      (	__a     :				str ,		__a     :				List[Any] ,		__a     :				Dict=("train",)		):

				"""simple docstring"""
				assert isinstance(__a ,		__a		)
				for split in splits:
								a__				      =		dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory""" ,		[False, True]		)
def 			A_      (	__a     :				int ,		__a     :				int ,		__a     :				Dict		):

				"""simple docstring"""
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								a__				      =		JsonDatasetReader({"""train""": jsonl_path} ,		cache_dir=__a ,		keep_in_memory=__a		).read()
				_check_json_datasetdict(__a ,		__a		)

@pytest.mark.parametrize(
    """features""" ,		[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ] ,		)
def 			A_      (	__a     :				Tuple ,		__a     :				Dict ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				a__				      =		features.copy() if features else default_expected_features
				a__				      =		(
				    Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
				)
				a__				      =		JsonDatasetReader({"""train""": jsonl_path} ,		features=__a ,		cache_dir=__a		).read()
				_check_json_datasetdict(__a ,		__a		)

@pytest.mark.parametrize("""split""" ,		[None, NamedSplit("""train"""		), """train""", """test"""]		)
def 			A_      (	__a     :				Dict ,		__a     :				Any ,		__a     :				Tuple		):

				"""simple docstring"""
				if split:
								a__				      =		{split: jsonl_path}
				else:
								a__				      =		"""train"""
								a__				      =		{"""train""": jsonl_path, """test""": jsonl_path}
				a__				      =		tmp_path / """cache"""
				a__				      =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				a__				      =		JsonDatasetReader(__a ,		cache_dir=__a		).read()
				_check_json_datasetdict(__a ,		__a ,		splits=list(path.keys()		)		)
				assert all(dataset[split].split == split for split in path.keys()		)

def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				return json.load(__a		)

def 			A_      (	__a     :				Tuple		):

				"""simple docstring"""
				return [json.loads(__a		) for line in buffer]




class  __snake_case      :







							'''simple docstring'''






							@pytest.mark.parametrize("""lines, load_json_function"""  ,  [(True, load_json_lines), (False, load_json)]		)
							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											with io.BytesIO() as buffer:
															JsonDatasetWriter(a_  ,  a_  ,  lines=a_		).write()
															buffer.seek(0		)
															a__				      =		load_json_function(a_		)
											assert isinstance(a_  ,  a_		)
											assert isinstance(exported_content[0]  ,  a_		)
											assert len(a_		) == 10


							@pytest.mark.parametrize(
							    """orient, container, keys, len_at"""  ,  [
							        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
							        ("""split""", dict, {"""columns""", """data"""}, """data"""),
							        ("""index""", dict, set("""0123456789"""		), None),
							        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
							        ("""values""", list, None, None),
							        ("""table""", dict, {"""schema""", """data"""}, """data"""),
							    ]  ,  )
							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											with io.BytesIO() as buffer:
															JsonDatasetWriter(a_  ,  a_  ,  lines=a_  ,  orient=a_		).write()
															buffer.seek(0		)
															a__				      =		load_json(a_		)
											assert isinstance(a_  ,  a_		)
											if keys:
															if container is dict:
																			assert exported_content.keys() == keys
															else:
																			assert exported_content[0].keys() == keys
											else:
															assert not hasattr(a_  ,  """keys"""		) and not hasattr(exported_content[0]  ,  """keys"""		)
											if len_at:
															assert len(exported_content[len_at]		) == 10
											else:
															assert len(a_		) == 10


							@pytest.mark.parametrize("""lines, load_json_function"""  ,  [(True, load_json_lines), (False, load_json)]		)
							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											with io.BytesIO() as buffer:
															JsonDatasetWriter(a_  ,  a_  ,  lines=a_  ,  num_proc=2		).write()
															buffer.seek(0		)
															a__				      =		load_json_function(a_		)
											assert isinstance(a_  ,  a_		)
											assert isinstance(exported_content[0]  ,  a_		)
											assert len(a_		) == 10


							@pytest.mark.parametrize(
							    """orient, container, keys, len_at"""  ,  [
							        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
							        ("""split""", dict, {"""columns""", """data"""}, """data"""),
							        ("""index""", dict, set("""0123456789"""		), None),
							        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
							        ("""values""", list, None, None),
							        ("""table""", dict, {"""schema""", """data"""}, """data"""),
							    ]  ,  )
							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											with io.BytesIO() as buffer:
															JsonDatasetWriter(a_  ,  a_  ,  lines=a_  ,  orient=a_  ,  num_proc=2		).write()
															buffer.seek(0		)
															a__				      =		load_json(a_		)
											assert isinstance(a_  ,  a_		)
											if keys:
															if container is dict:
																			assert exported_content.keys() == keys
															else:
																			assert exported_content[0].keys() == keys
											else:
															assert not hasattr(a_  ,  """keys"""		) and not hasattr(exported_content[0]  ,  """keys"""		)
											if len_at:
															assert len(exported_content[len_at]		) == 10
											else:
															assert len(a_		) == 10


							def _a      (							self  ,  a_		):
											with pytest.raises(a_		):
															with io.BytesIO() as buffer:
																			JsonDatasetWriter(a_  ,  a_  ,  num_proc=0		)







							@pytest.mark.parametrize("""compression, extension"""  ,  [("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]		)
							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		tmp_path_factory.mktemp("""data"""		) / F'''test.json.{extension}'''
											a__				      =		str(shared_datadir / F'''test_file.json.{extension}'''		)
											JsonDatasetWriter(a_  ,  a_  ,  compression=a_		).write()

											with fsspec.open(a_  ,  """rb"""  ,  compression="""infer"""		) as f:
															a__				      =		f.read()
											with fsspec.open(a_  ,  """rb"""  ,  compression="""infer"""		) as f:
															a__				      =		f.read()
											assert exported_content == original_content

