






'''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 			_snake_case		(     _A							):




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				pa.array(TypedSequence([1, 2, 3]							)							)
										self.assertEqual(arr.type    ,							pa.intaa()							)




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										with self.assertRaises(__SCREAMING_SNAKE_CASE							):
																	snake_case_      =				pa.array(TypedSequence([1, 2, 3]							)    ,							type=pa.intaa()							)




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										with self.assertRaises(__SCREAMING_SNAKE_CASE							):
																	snake_case_      =				pa.array(TypedSequence([1, 2, 3]    ,							try_type=Value("bool"							)    ,							type=Value("int64"							)							)							)




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

										'''simple docstring'''




										snake_case_      =				pa.array(TypedSequence([1, 2, 3]    ,							type=Value("int32"							)							)							)
										self.assertEqual(arr.type    ,							pa.intaa()							)




			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				pa.array(TypedSequence([1, 2, 3]    ,							try_type=Value("int32"							)							)							)
										self.assertEqual(arr.type    ,							pa.intaa()							)




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				pa.array(TypedSequence(["foo", "bar"]    ,							try_type=Value("int64"							)							)							)
										self.assertEqual(arr.type    ,							pa.string()							)




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

										'''simple docstring'''




										snake_case_      =				pa.array(TypedSequence([[[1, 2, 3]]]    ,							type=ArrayaD((1, 3)    ,							"int64"							)							)							)
										self.assertEqual(arr.type    ,							ArrayaDExtensionType((1, 3)    ,							"int64"							)							)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




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




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

										'''simple docstring'''




										snake_case_      =				pa.array(TypedSequence([[[1, 2, 3]]]    ,							try_type=ArrayaD((1, 3)    ,							"int64"							)							)							)
										self.assertEqual(arr.type    ,							ArrayaDExtensionType((1, 3)    ,							"int64"							)							)




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

										'''simple docstring'''




										snake_case_      =				pa.array(TypedSequence(["foo", "bar"]    ,							try_type=ArrayaD((1, 3)    ,							"int64"							)							)							)
										self.assertEqual(arr.type    ,							pa.string()							)




			@require_pil
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										import PIL.Image

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


def 					UpperCamelCase_(     snake_case							:	Dict							,    snake_case							:	int					):

							'''simple docstring'''






							snake_case_      =				pa.BufferReader(lowercase__					) if isinstance(lowercase__							,    pa.Buffer					) else pa.memory_map(lowercase__					)
							snake_case_      =				pa.ipc.open_stream(lowercase__					)
							snake_case_      =				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, 1_0]					)
@pytest.mark.parametrize(
    "fields"							,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def 					UpperCamelCase_(     snake_case							:	Tuple							,    snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				pa.BufferOutputStream()
							snake_case_      =				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}					)
														snake_case_ ,				snake_case_      =				writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_      =				{"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_(     ):

							'''simple docstring'''






							snake_case_      =				pa.BufferOutputStream()
							snake_case_      =				Features({"labels": ClassLabel(names=["neg", "pos"]					)}					)
							with ArrowWriter(stream=lowercase__							,    features=lowercase__					) as writer:
														writer.write({"labels": 0}					)
														writer.write({"labels": 1}					)
														snake_case_ ,				snake_case_      =				writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							assert writer._schema == features.arrow_schema
							assert writer._schema.metadata == features.arrow_schema.metadata
							snake_case_      =				pa.BufferReader(output.getvalue()					)
							snake_case_      =				pa.ipc.open_stream(lowercase__					)
							snake_case_      =				f.read_all()
							snake_case_      =				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, 1_0]					)
def 					UpperCamelCase_(     snake_case							:	Union[str, Any]					):

							'''simple docstring'''






							snake_case_      =				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]					)
																					snake_case_ ,				snake_case_      =				writer.finalize()


@pytest.mark.parametrize("writer_batch_size"							,    [None, 2, 1_0]					)
def 					UpperCamelCase_(     snake_case							:	Tuple					):

							'''simple docstring'''






							snake_case_      =				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_0					)
																					writer.write({"col_1": "bar", "col_2": 2}							,    key=1_0					)
																					snake_case_ ,				snake_case_      =				writer.finalize()


@pytest.mark.parametrize("writer_batch_size"							,    [None, 2, 1_0]					)
def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				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					)
														snake_case_ ,				snake_case_      =				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, 1_0]					)
@pytest.mark.parametrize(
    "fields"							,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				pa.BufferOutputStream()
							snake_case_      =				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": []}					)
														snake_case_ ,				snake_case_      =				writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_      =				{"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, 1_0]					)
@pytest.mark.parametrize(
    "fields"							,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	Dict					):

							'''simple docstring'''






							snake_case_      =				pa.BufferOutputStream()
							snake_case_      =				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]}					)					)
														snake_case_ ,				snake_case_      =				writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_      =				{"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, 1_0]					)
@pytest.mark.parametrize(
    "fields"							,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]					)
def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Optional[int]					):

							'''simple docstring'''






							snake_case_      =				pa.BufferOutputStream()
							snake_case_      =				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]}					)					)
														snake_case_ ,				snake_case_      =				writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_      =				{"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_(     ):

							'''simple docstring'''






							with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_      =				{"col_1": pa.string(), "col_2": pa.intaa()}
														snake_case_      =				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]}					)
																					snake_case_ ,				snake_case_      =				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_(     snake_case							:	List[str]					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	Dict							,    snake_case							:	List[str]					):

							'''simple docstring'''






							if isinstance(lst[0]							,    lowercase__					):
														change_first_primitive_element_in_list(lst[0]							,    lowercase__					)
							else:
														snake_case_      =				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_(     snake_case							:	Dict							,    snake_case							:	Optional[int]							,    snake_case							:	Any					):

							'''simple docstring'''






							snake_case_      =				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_(     snake_case							:	Any							,    snake_case							:	int							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							snake_case_      =				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
														snake_case_      =				copy.deepcopy(lowercase__					)
														snake_case_      =				np.iinfo(expected_dtype.to_pandas_dtype()					).max + 1
														change_first_primitive_element_in_list(lowercase__							,    lowercase__					)
														snake_case_      =				pa.array(OptimizedTypedSequence(lowercase__							,    col=lowercase__					)					)
														assert get_base_dtype(arr.type					) == pa.intaa()


@pytest.mark.parametrize("raise_exception"							,    [False, True]					)
def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	List[str]					):

							'''simple docstring'''






							snake_case_      =				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_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				"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}					)
														snake_case_ ,				snake_case_      =				writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							assert mockfs.exists(lowercase__					)


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				pa.BufferOutputStream()
							with ParquetWriter(stream=lowercase__					) as writer:
														writer.write({"col_1": "foo", "col_2": 1}					)
														writer.write({"col_1": "bar", "col_2": 2}					)
														snake_case_ ,				snake_case_      =				writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							snake_case_      =				pa.BufferReader(output.getvalue()					)
							snake_case_      =				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_(     snake_case							:	Optional[int]							,    snake_case							:	Any					):

							'''simple docstring'''






							import PIL.Image

							snake_case_      =				str(tmp_path / "test_image_rgb.jpg"					)
							PIL.Image.fromarray(np.zeros((5, 5)							,    dtype=np.uinta					)					).save(lowercase__							,    format="png"					)
							snake_case_      =				pa.BufferOutputStream()
							with ParquetWriter(
							    stream=lowercase__							,    features=Features({"image": Image()}					)							,    embed_local_files=lowercase__					) as writer:
														writer.write({"image": image_path}					)
														writer.finalize()
							snake_case_      =				pa.BufferReader(output.getvalue()					)
							snake_case_      =				pq.read_table(lowercase__					)
							snake_case_      =				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_(     ):

							'''simple docstring'''






							snake_case_      =				pa.schema([pa.field("col_1"							,    pa.string()							,    nullable=lowercase__					)]					)
							snake_case_      =				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()					)]					)










'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       List[Any]						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Dict							=   "decision_transformer"
			lowerCAmelCase_					:				List[Any]							=   ["past_key_values"]
			lowerCAmelCase_					:				Tuple							=   {
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}




			def __init__( self    ,							a__=17    ,							a__=4    ,							a__=128    ,							a__=4_096    ,							a__=True    ,							a__=1    ,							a__=1_024    ,							a__=3    ,							a__=1    ,							a__=None    ,							a__="relu"    ,							a__=0.1    ,							a__=0.1    ,							a__=0.1    ,							a__=1e-5    ,							a__=0.0_2    ,							a__=True    ,							a__=True    ,							a__=50_256    ,							a__=50_256    ,							a__=False    ,							a__=False    ,							**a__    ,							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				state_dim
										snake_case_      =				act_dim
										snake_case_      =				hidden_size
										snake_case_      =				max_ep_len
										snake_case_      =				action_tanh
										snake_case_      =				vocab_size
										snake_case_      =				n_positions
										snake_case_      =				n_layer
										snake_case_      =				n_head
										snake_case_      =				n_inner
										snake_case_      =				activation_function
										snake_case_      =				resid_pdrop
										snake_case_      =				embd_pdrop
										snake_case_      =				attn_pdrop
										snake_case_      =				layer_norm_epsilon
										snake_case_      =				initializer_range
										snake_case_      =				scale_attn_weights
										snake_case_      =				use_cache
										snake_case_      =				scale_attn_by_inverse_layer_idx
										snake_case_      =				reorder_and_upcast_attn

										snake_case_      =				bos_token_id
										snake_case_      =				eos_token_id

										super().__init__(bos_token_id=a__    ,							eos_token_id=a__    ,							**a__							)




