


"""simple docstring"""




import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil
class 					__lowerCAmelCase					(   _SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				__a           =			pa.array(TypedSequence([1, 2, 3]      )      )
				self.assertEqual(arr.type							, pa.intaa()      )

		def 			__UpperCAmelCase       (  self      ):
				with self.assertRaises(A_      ):
						__a           =			pa.array(TypedSequence([1, 2, 3]      )							, type=pa.intaa()      )

		def 			__UpperCAmelCase       (  self      ):
				with self.assertRaises(A_      ):
						__a           =			pa.array(TypedSequence([1, 2, 3]							, try_type=Value('''bool'''      )							, type=Value('''int64'''      )      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			pa.array(TypedSequence([1, 2, 3]							, type=Value('''int32'''      )      )      )
				self.assertEqual(arr.type							, pa.intaa()      )

		def 			__UpperCAmelCase       (  self      ):
				with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
						__a           =			pa.array(TypedSequence(['''foo''', '''bar''']							, type=Value('''int64'''      )      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			pa.array(TypedSequence([1, 2, 3]							, try_type=Value('''int32'''      )      )      )
				self.assertEqual(arr.type							, pa.intaa()      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			pa.array(TypedSequence(['''foo''', '''bar''']							, try_type=Value('''int64'''      )      )      )
				self.assertEqual(arr.type							, pa.string()      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			pa.array(TypedSequence([[[1, 2, 3]]]							, type=ArrayaD((1, 3)							, '''int64'''      )      )      )
				self.assertEqual(arr.type							, ArrayaDExtensionType((1, 3)							, '''int64'''      )      )

		def 			__UpperCAmelCase       (  self      ):
				with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
						__a           =			pa.array(TypedSequence(['''foo''', '''bar''']							, type=ArrayaD((1, 3)							, '''int64'''      )      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			pa.array(TypedSequence([[[1, 2, 3]]]							, try_type=ArrayaD((1, 3)							, '''int64'''      )      )      )
				self.assertEqual(arr.type							, ArrayaDExtensionType((1, 3)							, '''int64'''      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			pa.array(TypedSequence(['''foo''', '''bar''']							, try_type=ArrayaD((1, 3)							, '''int64'''      )      )      )
				self.assertEqual(arr.type							, pa.string()      )





		@require_pil
		def 			__UpperCAmelCase       (  self      ):
				import PIL.Image

				__a           =			PIL.Image.fromarray(np.arange(10							, dtype=np.uinta      ).reshape(2							, 5      )      )
				with patch(
				    '''datasets.arrow_writer.cast_to_python_objects'''							, side_effect=A_      ) as mock_cast_to_python_objects:
						__a           =			pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image]							, type=Image()      )      )
						__a      ,     __a           =			mock_cast_to_python_objects.call_args_list[-1]
						self.assertIn('''optimize_list_casting'''							, A_      )
						self.assertFalse(kwargs['''optimize_list_casting''']      )







def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Any			) ->      int:
		__a           =			pa.BufferReader(lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,					pa.Buffer			) else pa.memory_map(lowerCAmelCase__			)
		__a           =			pa.ipc.open_stream(lowerCAmelCase__			)
		__a           =			f.read_all()
		assert len(pa_table.to_batches()			) == expected_num_chunks
		assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
		del pa_table

@pytest.mark.parametrize('''writer_batch_size'''    ,					[None, 1, 10]			)
@pytest.mark.parametrize(
    '''fields'''    ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]			)
def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Optional[Any]			) ->      Optional[int]:
		__a           =			pa.BufferOutputStream()
		__a           =			pa.schema(lowerCAmelCase__			) if fields else None
		with ArrowWriter(stream=lowerCAmelCase__    ,					schema=lowerCAmelCase__    ,					writer_batch_size=lowerCAmelCase__			) as writer:
				writer.write({'''col_1''': '''foo''', '''col_2''': 1}			)
				writer.write({'''col_1''': '''bar''', '''col_2''': 2}			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				__a           =			{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
		assert writer._schema == pa.schema(lowerCAmelCase__    ,					metadata=writer._schema.metadata			)
		_check_output(output.getvalue()    ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)

def        lowercase							(		) ->      Any:
		__a           =			pa.BufferOutputStream()
		__a           =			Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']			)}			)
		with ArrowWriter(stream=lowerCAmelCase__    ,					features=lowerCAmelCase__			) as writer:
				writer.write({'''labels''': 0}			)
				writer.write({'''labels''': 1}			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		assert writer._schema == features.arrow_schema
		assert writer._schema.metadata == features.arrow_schema.metadata
		__a           =			pa.BufferReader(output.getvalue()			)
		__a           =			pa.ipc.open_stream(lowerCAmelCase__			)
		__a           =			f.read_all()
		__a           =			pa_table.schema
		assert pa_table.num_rows == 2
		assert schema == features.arrow_schema
		assert schema.metadata == features.arrow_schema.metadata
		assert features == Features.from_arrow_schema(lowerCAmelCase__			)

@pytest.mark.parametrize('''writer_batch_size'''    ,					[None, 1, 10]			)
def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Any:
		__a           =			pa.BufferOutputStream()
		with ArrowWriter(
		    stream=lowerCAmelCase__    ,					writer_batch_size=lowerCAmelCase__    ,					hash_salt='''split_name'''    ,					check_duplicates=lowerCAmelCase__    ,					) as writer:
				with pytest.raises(lowerCAmelCase__			):
						writer.write({'''col_1''': '''foo''', '''col_2''': 1}    ,					key=[1, 2]			)
						__a      ,     __a           =			writer.finalize()

@pytest.mark.parametrize('''writer_batch_size'''    ,					[None, 2, 10]			)
def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      Dict:
		__a           =			pa.BufferOutputStream()
		with ArrowWriter(
		    stream=lowerCAmelCase__    ,					writer_batch_size=lowerCAmelCase__    ,					hash_salt='''split_name'''    ,					check_duplicates=lowerCAmelCase__    ,					) as writer:
				with pytest.raises(lowerCAmelCase__			):
						writer.write({'''col_1''': '''foo''', '''col_2''': 1}    ,					key=10			)
						writer.write({'''col_1''': '''bar''', '''col_2''': 2}    ,					key=10			)
						__a      ,     __a           =			writer.finalize()

@pytest.mark.parametrize('''writer_batch_size'''    ,					[None, 2, 10]			)
def        lowercase							(		lowerCAmelCase__ :     List[Any]			) ->      Tuple:
		__a           =			pa.BufferOutputStream()
		with ArrowWriter(
		    stream=lowerCAmelCase__    ,					writer_batch_size=lowerCAmelCase__    ,					hash_salt='''split_name'''    ,					check_duplicates=lowerCAmelCase__    ,					) as writer:
				writer.write({'''col_1''': '''foo''', '''col_2''': 1}    ,					key=1			)
				writer.write({'''col_1''': '''bar''', '''col_2''': 2}    ,					key=2			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		_check_output(output.getvalue()    ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)

@pytest.mark.parametrize('''writer_batch_size'''    ,					[None, 1, 10]			)
@pytest.mark.parametrize(
    '''fields'''    ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]			)
def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Dict			) ->      Optional[int]:
		__a           =			pa.BufferOutputStream()
		__a           =			pa.schema(lowerCAmelCase__			) if fields else None
		with ArrowWriter(stream=lowerCAmelCase__    ,					schema=lowerCAmelCase__    ,					writer_batch_size=lowerCAmelCase__			) as writer:
				writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}			)
				writer.write_batch({'''col_1''': [], '''col_2''': []}			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				__a           =			{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
		assert writer._schema == pa.schema(lowerCAmelCase__    ,					metadata=writer._schema.metadata			)
		_check_output(output.getvalue()    ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)

@pytest.mark.parametrize('''writer_batch_size'''    ,					[None, 1, 10]			)
@pytest.mark.parametrize(
    '''fields'''    ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]			)
def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Optional[Any]			) ->      Dict:
		__a           =			pa.BufferOutputStream()
		__a           =			pa.schema(lowerCAmelCase__			) if fields else None
		with ArrowWriter(stream=lowerCAmelCase__    ,					schema=lowerCAmelCase__    ,					writer_batch_size=lowerCAmelCase__			) as writer:
				writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}			)			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				__a           =			{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
		assert writer._schema == pa.schema(lowerCAmelCase__    ,					metadata=writer._schema.metadata			)
		_check_output(output.getvalue()    ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)

@pytest.mark.parametrize('''writer_batch_size'''    ,					[None, 1, 10]			)
@pytest.mark.parametrize(
    '''fields'''    ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]			)
def        lowercase							(		lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     Any			) ->      Dict:
		__a           =			pa.BufferOutputStream()
		__a           =			pa.schema(lowerCAmelCase__			) if fields else None
		with ArrowWriter(stream=lowerCAmelCase__    ,					schema=lowerCAmelCase__    ,					writer_batch_size=lowerCAmelCase__			) as writer:
				writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}			)			)
				writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}			)			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				__a           =			{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
		assert writer._schema == pa.schema(lowerCAmelCase__    ,					metadata=writer._schema.metadata			)
		_check_output(output.getvalue()    ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)

def        lowercase							(		) ->      Union[str, Any]:

		with tempfile.TemporaryDirectory() as tmp_dir:
				__a           =			{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				__a           =			os.path.join(lowerCAmelCase__    ,					'''test.arrow'''			)
				with ArrowWriter(path=lowerCAmelCase__    ,					schema=pa.schema(lowerCAmelCase__			)			) as writer:
						writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}			)
						__a      ,     __a           =			writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				assert writer._schema == pa.schema(lowerCAmelCase__    ,					metadata=writer._schema.metadata			)
				_check_output(lowerCAmelCase__    ,					1			)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      str:

		if pa.types.is_list(lowerCAmelCase__			):
				return get_base_dtype(arr_type.value_type			)
		else:
				return arr_type

def        lowercase							(		lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     List[str]			) ->      Optional[Any]:

		if isinstance(lst[0]    ,					lowerCAmelCase__			):
				change_first_primitive_element_in_list(lst[0]    ,					lowerCAmelCase__			)
		else:
				__a           =			value

@pytest.mark.parametrize('''optimized_int_type, expected_dtype'''    ,					[(None, pa.intaa()), (Value('''int32'''			), pa.intaa())]			)
@pytest.mark.parametrize('''sequence'''    ,					[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int			) ->      Any:
		__a           =			pa.array(TypedSequence(lowerCAmelCase__    ,					optimized_int_type=lowerCAmelCase__			)			)
		assert get_base_dtype(arr.type			) == expected_dtype

@pytest.mark.parametrize(
    '''col, expected_dtype'''    ,					[
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ]    ,					)
@pytest.mark.parametrize('''sequence'''    ,					[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     Optional[Any]			) ->      Optional[Any]:
		__a           =			pa.array(OptimizedTypedSequence(lowerCAmelCase__    ,					col=lowerCAmelCase__			)			)
		assert get_base_dtype(arr.type			) == expected_dtype

		# not in range
		if col != "other":
				# avoids errors due to in-place modifications
				__a           =			copy.deepcopy(lowerCAmelCase__			)
				__a           =			np.iinfo(expected_dtype.to_pandas_dtype()			).max + 1
				change_first_primitive_element_in_list(lowerCAmelCase__    ,					lowerCAmelCase__			)
				__a           =			pa.array(OptimizedTypedSequence(lowerCAmelCase__    ,					col=lowerCAmelCase__			)			)
				assert get_base_dtype(arr.type			) == pa.intaa()

@pytest.mark.parametrize('''raise_exception'''    ,					[False, True]			)
def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Union[str, Any]			) ->      Dict:
		__a           =			str(tmp_path / '''dataset-train.arrow'''			)
		try:
				with ArrowWriter(path=lowerCAmelCase__			) as writer:
						if raise_exception:
								raise pa.lib.ArrowInvalid()
						else:
								writer.stream.close()
		except pa.lib.ArrowInvalid:
				pass
		finally:
				assert writer.stream.closed

def        lowercase							(		lowerCAmelCase__ :     List[Any]			) ->      int:
		__a           =			'''mock://dataset-train.arrow'''
		with ArrowWriter(path=lowerCAmelCase__    ,					storage_options=mockfs.storage_options			) as writer:
				assert isinstance(writer._fs    ,					type(lowerCAmelCase__			)			)
				assert writer._fs.storage_options == mockfs.storage_options
				writer.write({'''col_1''': '''foo''', '''col_2''': 1}			)
				writer.write({'''col_1''': '''bar''', '''col_2''': 2}			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		assert mockfs.exists(lowerCAmelCase__			)

def        lowercase							(		) ->      List[Any]:
		__a           =			pa.BufferOutputStream()
		with ParquetWriter(stream=lowerCAmelCase__			) as writer:
				writer.write({'''col_1''': '''foo''', '''col_2''': 1}			)
				writer.write({'''col_1''': '''bar''', '''col_2''': 2}			)
				__a      ,     __a           =			writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		__a           =			pa.BufferReader(output.getvalue()			)
		__a           =			pq.read_table(lowerCAmelCase__			)
		assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}

@require_pil
@pytest.mark.parametrize('''embed_local_files'''    ,					[False, True]			)
def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Optional[Any]			) ->      Dict:
		import PIL.Image

		__a           =			str(tmp_path / '''test_image_rgb.jpg'''			)
		PIL.Image.fromarray(np.zeros((5, 5)    ,					dtype=np.uinta			)			).save(lowerCAmelCase__    ,					format='''png'''			)
		__a           =			pa.BufferOutputStream()
		with ParquetWriter(
		    stream=lowerCAmelCase__    ,					features=Features({'''image''': Image()}			)    ,					embed_local_files=lowerCAmelCase__			) as writer:
				writer.write({'''image''': image_path}			)
				writer.finalize()
		__a           =			pa.BufferReader(output.getvalue()			)
		__a           =			pq.read_table(lowerCAmelCase__			)
		__a           =			pa_table.to_pydict()
		if embed_local_files:
				assert isinstance(out['''image'''][0]['''path''']    ,					lowerCAmelCase__			)
				with open(lowerCAmelCase__    ,					'''rb'''			) as f:
						assert out["image"][0]["bytes"] == f.read()
		else:
				assert out["image"][0]["path"] == image_path
				assert out["image"][0]["bytes"] is None




def        lowercase							(		) ->      Any:
		__a           =			pa.schema([pa.field('''col_1'''    ,					pa.string()    ,					nullable=lowerCAmelCase__			)]			)
		__a           =			pa.BufferOutputStream()
		with ArrowWriter(stream=lowerCAmelCase__			) as writer:
				writer._build_writer(inferred_schema=lowerCAmelCase__			)
				assert writer._schema == pa.schema([pa.field('''col_1'''    ,					pa.string()			)]			)








import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     value_function
				UpperCamelCase		=     unet
				UpperCamelCase		=     scheduler
				UpperCamelCase		=     env
				UpperCamelCase		=     env.get_dataset()
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].mean()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].std()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     env.observation_space.shape[0]
				UpperCamelCase		=     env.action_space.shape[0]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return (x_in - self.means[key]) / self.stds[key]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				return x_in * self.stds[key] + self.means[key]



		def __UpperCamelCase ( self				,      A_       )  ->					Any:
				"""simple docstring"""
				if type(A_       ) is dict:
						return {k: self.to_torch(A_       ) for k, v in x_in.items()}
				elif torch.is_tensor(A_       ):
						return x_in.to(self.unet.device       )
				return torch.tensor(A_				,      device=self.unet.device       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for key, val in cond.items():
						UpperCamelCase		=     val.clone()
				return x_in



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     x.shape[0]
				UpperCamelCase		=     None
				for i in tqdm.tqdm(self.scheduler.timesteps       ):
						# create batch of timesteps to pass into model
						UpperCamelCase		=     torch.full((batch_size,)				,      A_				,      device=self.unet.device				,      dtype=torch.long       )
						for _ in range(A_       ):
								with torch.enable_grad():
										x.requires_grad_()

										# permute to match dimension for pre-trained models
										UpperCamelCase		=     self.value_function(x.permute(0				,      2				,      1       )				,      A_       ).sample
										UpperCamelCase		=     torch.autograd.grad([y.sum()]				,      [x]       )[0]

										UpperCamelCase		=     self.scheduler._get_variance(A_       )
										UpperCamelCase		=     torch.exp(0.5 * posterior_variance       )
										UpperCamelCase		=     model_std * grad

								UpperCamelCase		=     0
								UpperCamelCase		=     x.detach()
								UpperCamelCase		=     x + scale * grad
								UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )

						UpperCamelCase		=     self.unet(x.permute(0				,      2				,      1       )				,      A_       ).sample.permute(0				,      2				,      1       )

						# TODO: verify deprecation of this kwarg
						UpperCamelCase		=     self.scheduler.step(A_				,      A_				,      A_				,      predict_epsilon=A_       )['prev_sample']

						# apply conditions to the trajectory (set the initial state)
						UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
						UpperCamelCase		=     self.to_torch(A_       )
				return x, y



		def __call__( self				,      A_				,      A_=64				,      A_=32				,      A_=2				,      A_=0.1       )  ->					List[str]:
				"""simple docstring"""
				# normalize the observations and create  batch dimension
				UpperCamelCase		=     self.normalize(A_				,      'observations'       )
				UpperCamelCase		=     obs[None].repeat(A_				,      axis=0       )

				UpperCamelCase		=     {0: self.to_torch(A_       )}
				UpperCamelCase		=     (batch_size, planning_horizon, self.state_dim + self.action_dim)

				# generate initial noise and apply our conditions (to make the trajectories start at current state)
				UpperCamelCase		=     randn_tensor(A_				,      device=self.unet.device       )
				UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
				UpperCamelCase		=     self.to_torch(A_       )

				# run the diffusion process
				UpperCamelCase	,  UpperCamelCase		=     self.run_diffusion(A_				,      A_				,      A_				,      A_       )

				# sort output trajectories by value
				UpperCamelCase		=     y.argsort(0				,      descending=A_       ).squeeze()
				UpperCamelCase		=     x[sorted_idx]
				UpperCamelCase		=     sorted_values[:, :, : self.action_dim]
				UpperCamelCase		=     actions.detach().cpu().numpy()
				UpperCamelCase		=     self.de_normalize(A_				,      key='actions'       )

				# select the action with the highest value
				if y is not None:
						UpperCamelCase		=     0
				else:
						# if we didn't run value guiding, select a random action
						UpperCamelCase		=     np.random.randint(0				,      A_       )

				UpperCamelCase		=     denorm_actions[selected_index, 0]
				return denorm_actions
