

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     _a						(__magic_name__				):


					'''simple docstring'''






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






					def   __A			( self			):
						with self.assertRaises(A__			):
							A__							:    str        =		pa.array(TypedSequence([1, 2, 3]			)    ,       type=pa.intaa()			)






					def   __A			( self			):
						with self.assertRaises(A__			):
							A__							:    List[str]        =		pa.array(TypedSequence([1, 2, 3]    ,       try_type=Value("""bool"""			)    ,       type=Value("""int64"""			)			)			)






					def   __A			( self			):
						A__							:    Any        =		pa.array(TypedSequence([1, 2, 3]    ,       type=Value("""int32"""			)			)			)
						self.assertEqual(arr.type    ,       pa.intaa()			)






					def   __A			( self			):
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)			):
							A__							:    Any        =		pa.array(TypedSequence(["""foo""", """bar"""]    ,       type=Value("""int64"""			)			)			)






					def   __A			( self			):
						A__							:    int        =		pa.array(TypedSequence([1, 2, 3]    ,       try_type=Value("""int32"""			)			)			)
						self.assertEqual(arr.type    ,       pa.intaa()			)






					def   __A			( self			):
						A__							:    int        =		pa.array(TypedSequence(["""foo""", """bar"""]    ,       try_type=Value("""int64"""			)			)			)
						self.assertEqual(arr.type    ,       pa.string()			)






					def   __A			( self			):
						A__							:    Tuple        =		pa.array(TypedSequence([[[1, 2, 3]]]    ,       type=ArrayaD((1, 3)    ,       """int64"""			)			)			)
						self.assertEqual(arr.type    ,       ArrayaDExtensionType((1, 3)    ,       """int64"""			)			)






					def   __A			( self			):
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)			):
							A__							:    Dict        =		pa.array(TypedSequence(["""foo""", """bar"""]    ,       type=ArrayaD((1, 3)    ,       """int64"""			)			)			)






					def   __A			( self			):
						A__							:    Optional[int]        =		pa.array(TypedSequence([[[1, 2, 3]]]    ,       try_type=ArrayaD((1, 3)    ,       """int64"""			)			)			)
						self.assertEqual(arr.type    ,       ArrayaDExtensionType((1, 3)    ,       """int64"""			)			)






					def   __A			( self			):
						A__							:    List[Any]        =		pa.array(TypedSequence(["""foo""", """bar"""]    ,       try_type=ArrayaD((1, 3)    ,       """int64"""			)			)			)
						self.assertEqual(arr.type    ,       pa.string()			)






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

						A__							:    Dict        =		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__							:    Optional[int]        =		pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image]    ,       type=Image()			)			)
							A__   ,   A__							:    Any        =		mock_cast_to_python_objects.call_args_list[-1]
							self.assertIn("""optimize_list_casting"""    ,       A__			)
							self.assertFalse(kwargs["""optimize_list_casting"""]			)



def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: int					)     ->      Union[str, Any]:
	A__							:    str        =		pa.BufferReader(lowercase_					) if isinstance(lowercase_    ,      pa.Buffer					) else pa.memory_map(lowercase_					)
	A__							:    Any        =		pa.ipc.open_stream(lowercase_					)
	A__							:    pa.Table        =		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 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: Any					)     ->      Union[str, Any]:
	A__							:    List[Any]        =		pa.BufferOutputStream()
	A__							:    Dict        =		pa.schema(lowercase_					) if fields else None
	with ArrowWriter(stream=lowercase_    ,      schema=lowercase_    ,      writer_batch_size=lowercase_					) as writer:
		writer.write({"""col_1""": """foo""", """col_2""": 1}					)
		writer.write({"""col_1""": """bar""", """col_2""": 2}					)
		A__   ,   A__							:    List[Any]        =		writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		A__							:    Any        =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
	assert writer._schema == pa.schema(lowercase_    ,      metadata=writer._schema.metadata					)
	_check_output(output.getvalue()    ,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1					)

def 						UpperCamelCase					()     ->      int:
	A__							:    List[Any]        =		pa.BufferOutputStream()
	A__							:    Optional[int]        =		Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]					)}					)
	with ArrowWriter(stream=lowercase_    ,      features=lowercase_					) as writer:
		writer.write({"""labels""": 0}					)
		writer.write({"""labels""": 1}					)
		A__   ,   A__							:    int        =		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__							:    Any        =		pa.BufferReader(output.getvalue()					)
	A__							:    int        =		pa.ipc.open_stream(lowercase_					)
	A__							:    pa.Table        =		f.read_all()
	A__							:    List[Any]        =		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(lowercase_					)

@pytest.mark.parametrize("""writer_batch_size"""    ,      [None, 1, 10]					)
def 						UpperCamelCase					(lowercase_: int					)     ->      Union[str, Any]:
	A__							:    Optional[int]        =		pa.BufferOutputStream()
	with ArrowWriter(
	    stream=lowercase_    ,      writer_batch_size=lowercase_    ,      hash_salt="""split_name"""    ,      check_duplicates=lowercase_    ,      ) as writer:
		with pytest.raises(lowercase_					):
			writer.write({"""col_1""": """foo""", """col_2""": 1}    ,      key=[1, 2]					)
			A__   ,   A__							:    int        =		writer.finalize()

@pytest.mark.parametrize("""writer_batch_size"""    ,      [None, 2, 10]					)
def 						UpperCamelCase					(lowercase_: int					)     ->      Tuple:
	A__							:    Dict        =		pa.BufferOutputStream()
	with ArrowWriter(
	    stream=lowercase_    ,      writer_batch_size=lowercase_    ,      hash_salt="""split_name"""    ,      check_duplicates=lowercase_    ,      ) as writer:
		with pytest.raises(lowercase_					):
			writer.write({"""col_1""": """foo""", """col_2""": 1}    ,      key=10					)
			writer.write({"""col_1""": """bar""", """col_2""": 2}    ,      key=10					)
			A__   ,   A__							:    Any        =		writer.finalize()

@pytest.mark.parametrize("""writer_batch_size"""    ,      [None, 2, 10]					)
def 						UpperCamelCase					(lowercase_: Union[str, Any]					)     ->      Tuple:
	A__							:    Any        =		pa.BufferOutputStream()
	with ArrowWriter(
	    stream=lowercase_    ,      writer_batch_size=lowercase_    ,      hash_salt="""split_name"""    ,      check_duplicates=lowercase_    ,      ) as writer:
		writer.write({"""col_1""": """foo""", """col_2""": 1}    ,      key=1					)
		writer.write({"""col_1""": """bar""", """col_2""": 2}    ,      key=2					)
		A__   ,   A__							:    Optional[int]        =		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 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: List[Any]					)     ->      List[str]:
	A__							:    str        =		pa.BufferOutputStream()
	A__							:    Dict        =		pa.schema(lowercase_					) if fields else None
	with ArrowWriter(stream=lowercase_    ,      schema=lowercase_    ,      writer_batch_size=lowercase_					) as writer:
		writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}					)
		writer.write_batch({"""col_1""": [], """col_2""": []}					)
		A__   ,   A__							:    Optional[int]        =		writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		A__							:    str        =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
	assert writer._schema == pa.schema(lowercase_    ,      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 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	A__							:    Dict        =		pa.BufferOutputStream()
	A__							:    Optional[Any]        =		pa.schema(lowercase_					) if fields else None
	with ArrowWriter(stream=lowercase_    ,      schema=lowercase_    ,      writer_batch_size=lowercase_					) as writer:
		writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}					)					)
		A__   ,   A__							:    Optional[Any]        =		writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		A__							:    Optional[Any]        =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
	assert writer._schema == pa.schema(lowercase_    ,      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 						UpperCamelCase					(lowercase_: List[Any]    ,      lowercase_: List[str]					)     ->      Tuple:
	A__							:    int        =		pa.BufferOutputStream()
	A__							:    int        =		pa.schema(lowercase_					) if fields else None
	with ArrowWriter(stream=lowercase_    ,      schema=lowercase_    ,      writer_batch_size=lowercase_					) 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__							:    List[str]        =		writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		A__							:    Tuple        =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
	assert writer._schema == pa.schema(lowercase_    ,      metadata=writer._schema.metadata					)
	_check_output(output.getvalue()    ,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1					)

def 						UpperCamelCase					()     ->      Union[str, Any]:
	with tempfile.TemporaryDirectory() as tmp_dir:
		A__							:    List[str]        =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
		A__							:    int        =		os.path.join(lowercase_    ,      """test.arrow"""					)
		with ArrowWriter(path=lowercase_    ,      schema=pa.schema(lowercase_					)					) as writer:
			writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}					)
			A__   ,   A__							:    Any        =		writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		assert writer._schema == pa.schema(lowercase_    ,      metadata=writer._schema.metadata					)
		_check_output(lowercase_    ,      1					)

def 						UpperCamelCase					(lowercase_: Dict					)     ->      Any:
	if pa.types.is_list(lowercase_					):
		return get_base_dtype(arr_type.value_type					)
	else:
		return arr_type

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict					)     ->      int:
	if isinstance(lst[0]    ,      lowercase_					):
		change_first_primitive_element_in_list(lst[0]    ,      lowercase_					)
	else:
		A__							:    Optional[int]        =		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 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: int    ,      lowercase_: Union[str, Any]					)     ->      Dict:
	A__							:    Optional[int]        =		pa.array(TypedSequence(lowercase_    ,      optimized_int_type=lowercase_					)					)
	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 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: Optional[Any]    ,      lowercase_: Union[str, Any]					)     ->      List[Any]:
	# in range
	A__							:    str        =		pa.array(OptimizedTypedSequence(lowercase_    ,      col=lowercase_					)					)
	assert get_base_dtype(arr.type					) == expected_dtype

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

@pytest.mark.parametrize("""raise_exception"""    ,      [False, True]					)
def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: List[Any]					)     ->      Any:
	A__							:    List[str]        =		str(tmp_path / """dataset-train.arrow"""					)
	try:
		with ArrowWriter(path=lowercase_					) as writer:
			if raise_exception:
				raise pa.lib.ArrowInvalid()
			else:
				writer.stream.close()
	except pa.lib.ArrowInvalid:
		pass
	finally:
		assert writer.stream.closed

def 						UpperCamelCase					(lowercase_: List[str]					)     ->      List[str]:
	A__							:    Tuple        =		"""mock://dataset-train.arrow"""
	with ArrowWriter(path=lowercase_    ,      storage_options=mockfs.storage_options					) as writer:
		assert isinstance(writer._fs    ,      type(lowercase_					)					)
		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__							:    Optional[int]        =		writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	assert mockfs.exists(lowercase_					)

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

@require_pil
@pytest.mark.parametrize("""embed_local_files"""    ,      [False, True]					)
def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: List[str]					)     ->      Union[str, Any]:
	import PIL.Image

	A__							:    List[Any]        =		str(tmp_path / """test_image_rgb.jpg"""					)
	PIL.Image.fromarray(np.zeros((5, 5)    ,      dtype=np.uinta					)					).save(lowercase_    ,      format="""png"""					)
	A__							:    List[str]        =		pa.BufferOutputStream()
	with ParquetWriter(
	    stream=lowercase_    ,      features=Features({"""image""": Image()}					)    ,      embed_local_files=lowercase_					) as writer:
		writer.write({"""image""": image_path}					)
		writer.finalize()
	A__							:    Tuple        =		pa.BufferReader(output.getvalue()					)
	A__							:    pa.Table        =		pq.read_table(lowercase_					)
	A__							:    Optional[int]        =		pa_table.to_pydict()
	if embed_local_files:
		assert isinstance(out["""image"""][0]["""path"""]    ,      lowercase_					)
		with open(lowercase_    ,      """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 						UpperCamelCase					()     ->      Union[str, Any]:
	A__							:    Optional[Any]        =		pa.schema([pa.field("""col_1"""    ,      pa.string()    ,      nullable=lowercase_					)]					)
	A__							:    Union[str, Any]        =		pa.BufferOutputStream()
	with ArrowWriter(stream=lowercase_					) as writer:
		writer._build_writer(inferred_schema=lowercase_					)
		assert writer._schema == pa.schema([pa.field("""col_1"""    ,      pa.string()					)]					)






import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A_       : List[str]         =			{
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
	A_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

	A_       : str         =			ValueGuidedRLPipeline.from_pretrained(
	    'bglick13/hopper-medium-v2-value-function-hor32',
	    env=env,
	)

	env.seed(0)
	A_       : List[Any]         =			env.reset()
	A_       : Optional[int]         =			0
	A_       : str         =			0
	A_       : Optional[Any]         =			1000
	A_       : Union[str, Any]         =			[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			A_       : Tuple         =			pipeline(obs, planning_horizon=32)

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[str]         =			env.get_normalized_score(total_reward)

			# update return
			total_reward += reward
			total_score += score
			print(
			    f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
			    f''' {total_score}'''
			)

			# save observations for rendering
			rollout.append(next_observation.copy())

			A_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

	print(f'''Total reward: {total_reward}''')



