code: 
  
  
  import os
  import tempfile
  from pathlib import Path
  from unittest import TestCase
  
  import pyarrow as pa
  import pytest
  
  from datasets.arrow_dataset import Dataset
  from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
  from datasets.info import DatasetInfo
  from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo
  
  from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_=False							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_		,				a_       :   Dict			=	(
  								    filename_skip_take["""filename"""],
  								    filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
  								    filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
  								)
  								open(os.path.join(UpperCamelCase_							)				,    """wb"""							).close()
  								a_       :   Union[str, Any]			=	pa.Table.from_pydict({"""filename""": [Path(UpperCamelCase_							).name] * 100}							)
  								if take == -1:
  															a_       :   Tuple			=	len(UpperCamelCase_							) - skip
  								if skip is not None and take is not None:
  															a_       :   Dict			=	pa_table.slice(UpperCamelCase_				,    UpperCamelCase_							)
  								return pa_table
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	"""my_name"""
  								a_       :   Union[str, Any]			=	SplitInfo(name="""train"""				,    num_examples=100							)
  								a_       :   List[str]			=	SplitInfo(name="""test"""				,    num_examples=100							)
  								a_       :   List[Any]			=	[train_info, test_info]
  								a_       :   List[str]			=	SplitDict()
  								split_dict.add(UpperCamelCase_							)
  								split_dict.add(UpperCamelCase_							)
  								a_       :   Any			=	DatasetInfo(splits=UpperCamelCase_							)
  
  								with tempfile.TemporaryDirectory() as tmp_dir:
  															a_       :   Optional[Any]			=	ReaderTest(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   Optional[Any]			=	"""test[:33%]"""
  															a_       :   Optional[int]			=	Dataset(**reader.read(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)							)
  															self.assertEqual(dset["""filename"""][0]				,    f"""{name}-test"""							)
  															self.assertEqual(dset.num_rows				,    33							)
  															self.assertEqual(dset.num_columns				,    1							)
  
  															a_       :   List[str]			=	["""train""", """test[:33%]"""]
  															a_       :   Dict			=	[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""							)]
  															for instructions in [instructionsa, instructionsa]:
  																						a_       :   str			=	[reader.read(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							) for instr in instructions]
  																						a_		,				a_       :   str			=	(Dataset(**UpperCamelCase_							) for dataset_kwargs in datasets_kwargs)
  																						self.assertEqual(train_dset["""filename"""][0]				,    f"""{name}-train"""							)
  																						self.assertEqual(train_dset.num_rows				,    100							)
  																						self.assertEqual(train_dset.num_columns				,    1							)
  																						self.assertIsInstance(train_dset.split				,    UpperCamelCase_							)
  																						self.assertEqual(str(train_dset.split							)				,    """train"""							)
  																						self.assertEqual(test_dset["""filename"""][0]				,    f"""{name}-test"""							)
  																						self.assertEqual(test_dset.num_rows				,    33							)
  																						self.assertEqual(test_dset.num_columns				,    1							)
  																						self.assertIsInstance(test_dset.split				,    UpperCamelCase_							)
  																						self.assertEqual(str(test_dset.split							)				,    """test[:33%]"""							)
  																						del train_dset, test_dset
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	"""my_name"""
  								a_       :   Tuple			=	SplitInfo(name="""train"""				,    num_examples=1000				,    shard_lengths=[100] * 10							)
  								a_       :   Optional[Any]			=	[train_info]
  								a_       :   str			=	SplitDict()
  								split_dict.add(UpperCamelCase_							)
  								a_       :   List[str]			=	DatasetInfo(splits=UpperCamelCase_							)
  
  								with tempfile.TemporaryDirectory() as tmp_dir:
  															a_       :   Optional[Any]			=	ReaderTest(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   Any			=	"""train[:33%]"""
  															a_       :   Optional[Any]			=	Dataset(**reader.read(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)							)
  															self.assertEqual(dset["""filename"""][0]				,    f"""{name}-train-00000-of-00010"""							)
  															self.assertEqual(dset["""filename"""][-1]				,    f"""{name}-train-00003-of-00010"""							)
  															self.assertEqual(dset.num_rows				,    330							)
  															self.assertEqual(dset.num_columns				,    1							)
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	SplitInfo(name="""train"""				,    num_examples=100							)
  								a_       :   str			=	SplitInfo(name="""test"""				,    num_examples=100							)
  								a_       :   Any			=	SplitDict()
  								split_dict.add(UpperCamelCase_							)
  								split_dict.add(UpperCamelCase_							)
  								a_       :   List[str]			=	DatasetInfo(splits=UpperCamelCase_							)
  
  								with tempfile.TemporaryDirectory() as tmp_dir:
  															a_       :   List[Any]			=	ReaderTest(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   Any			=	[
  															    {"""filename""": os.path.join(UpperCamelCase_				,    """train"""							)},
  															    {"""filename""": os.path.join(UpperCamelCase_				,    """test"""							), """skip""": 10, """take""": 10},
  															]
  															a_       :   Optional[int]			=	Dataset(**reader.read_files(UpperCamelCase_				,    original_instructions="""train+test[10:20]"""							)							)
  															self.assertEqual(dset.num_rows				,    110							)
  															self.assertEqual(dset.num_columns				,    1							)
  															del dset
  
  
  
  
  
  @pytest.mark.parametrize("""in_memory"""	,	[False, True]      )
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]	,	SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Union[str, Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[Any]			=	arrow_file
  							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  														a_       :   Optional[int]			=	ArrowReader.read_table(SCREAMING_SNAKE_CASE_	,	in_memory=SCREAMING_SNAKE_CASE_      )
  							assert table.shape == dataset.data.shape
  							assert set(table.column_names      ) == set(dataset.data.column_names      )
  							assert dict(table.to_pydict()      ) == dict(dataset.data.to_pydict()      )  # to_pydict returns OrderedDict
  
  
  
  
  
  @pytest.mark.parametrize("""in_memory"""	,	[False, True]      )
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: Union[str, Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[Any]			=	arrow_file
  							a_       :   Any			=	ArrowReader(""""""	,	SCREAMING_SNAKE_CASE_      )
  							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  														a_       :   List[str]			=	reader.read_files([{"""filename""": filename}]	,	in_memory=SCREAMING_SNAKE_CASE_      )
  							assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  							a_       :   List[Any]			=	dataset_kwargs["""arrow_table"""]
  							assert table.shape == dataset.data.shape
  							assert set(table.column_names      ) == set(dataset.data.column_names      )
  							assert dict(table.to_pydict()      ) == dict(dataset.data.to_pydict()      )  # to_pydict returns OrderedDict
  
  
  
  
  
  def 						_lowerCamelCase		(							):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							assert ReadInstruction("""train"""	,	to=10	,	unit="""abs"""      ).to_spec() == "train[:10]"
  							assert ReadInstruction("""train"""	,	from_=-80	,	to=10	,	unit="""%"""      ).to_spec() == "train[-80%:10%]"
  
  							a_       :   str			=	"""train+test"""
  							assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_      ).to_spec() == spec_train_test
  
  							a_       :   Optional[int]			=	"""train[2:10]"""
  							assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_      ).to_spec() == spec_train_abs
  
  							a_       :   Tuple			=	"""train[15%:-20%]"""
  							assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_      ).to_spec() == spec_train_pct
  
  							a_       :   List[Any]			=	"""train[:10%](closest)"""
  							assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_      ).to_spec() == "train[:10%]"
  
  							a_       :   Any			=	"""train[:10%](pct1_dropremainder)"""
  							assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_      ).to_spec() == spec_train_pct_rounding
  
  							a_       :   int			=	"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
  							assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_      ).to_spec() == spec_train_test_pct_rounding
  
  
  
  
  
  def 						_lowerCamelCase		(							):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   int			=	"""dummy"""
  							a_       :   str			=	[SplitInfo(name="""train"""	,	num_examples=1_00      )]
  							a_       :   str			=	"""train[:33%]"""
  							a_       :   Tuple			=	"""arrow"""
  							a_       :   str			=	"""prefix"""
  
  							a_       :   Tuple			=	make_file_instructions(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  							assert isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  							assert file_instructions.num_examples == 33
  							assert file_instructions.file_instructions == [
  							    {"filename": os.path.join(SCREAMING_SNAKE_CASE_	,	F"""{name}-train.arrow"""      ), "skip": 0, "take": 33}
  							]
  
  							a_       :   int			=	[SplitInfo(name="""train"""	,	num_examples=1_00	,	shard_lengths=[10] * 10      )]
  							a_       :   List[Any]			=	make_file_instructions(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  							assert isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  							assert file_instructions.num_examples == 33
  							assert file_instructions.file_instructions == [
  							    {"filename": os.path.join(SCREAMING_SNAKE_CASE_	,	F"""{name}-train-00000-of-00010.arrow"""      ), "skip": 0, "take": -1},
  							    {"filename": os.path.join(SCREAMING_SNAKE_CASE_	,	F"""{name}-train-00001-of-00010.arrow"""      ), "skip": 0, "take": -1},
  							    {"filename": os.path.join(SCREAMING_SNAKE_CASE_	,	F"""{name}-train-00002-of-00010.arrow"""      ), "skip": 0, "take": -1},
  							    {"filename": os.path.join(SCREAMING_SNAKE_CASE_	,	F"""{name}-train-00003-of-00010.arrow"""      ), "skip": 0, "take": 3},
  							]
  
  
  
  
  
  @pytest.mark.parametrize("""name, expected_exception"""	,	[(None, TypeError), ("""""", ValueError)]      )
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Optional[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   int			=	[SplitInfo(name="""train"""	,	num_examples=1_00      )]
  							a_       :   Tuple			=	"""train"""
  							a_       :   List[str]			=	"""arrow"""
  							a_       :   int			=	"""prefix_path"""
  							with pytest.raises(SCREAMING_SNAKE_CASE_      ):
  														a_       :   Any			=	make_file_instructions(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  
  
  
style_context: 
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : List[Any]          =    {
      "shi-labs/oneformer_ade20k_swin_tiny": (
          "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
      ),
      # See all OneFormer models at https://huggingface.co/models?filter=oneformer
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Tuple         = """oneformer"""
  	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if backbone_config is None:
  															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
  															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
  															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
  								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  															a_       :   str			=	backbone_config.get("""model_type"""							)
  															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
  															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)
  
  								a_       :   str			=	backbone_config
  
  								a_       :   List[str]			=	ignore_value
  								a_       :   Optional[int]			=	num_queries
  								a_       :   Optional[Any]			=	no_object_weight
  								a_       :   List[Any]			=	class_weight
  								a_       :   Union[str, Any]			=	mask_weight
  								a_       :   Union[str, Any]			=	dice_weight
  								a_       :   Optional[Any]			=	contrastive_weight
  								a_       :   Any			=	contrastive_temperature
  								a_       :   Optional[int]			=	train_num_points
  								a_       :   Tuple			=	oversample_ratio
  								a_       :   str			=	importance_sample_ratio
  								a_       :   List[Any]			=	init_std
  								a_       :   int			=	init_xavier_std
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   Optional[int]			=	is_training
  								a_       :   List[Any]			=	use_auxiliary_loss
  								a_       :   str			=	output_auxiliary_logits
  								a_       :   Dict			=	strides
  								a_       :   List[Any]			=	task_seq_len
  								a_       :   int			=	text_encoder_width
  								a_       :   List[Any]			=	text_encoder_context_length
  								a_       :   List[str]			=	text_encoder_num_layers
  								a_       :   Union[str, Any]			=	text_encoder_vocab_size
  								a_       :   Optional[Any]			=	text_encoder_proj_layers
  								a_       :   Optional[int]			=	text_encoder_n_ctx
  								a_       :   Optional[Any]			=	conv_dim
  								a_       :   str			=	mask_dim
  								a_       :   Optional[Any]			=	hidden_dim
  								a_       :   Any			=	encoder_feedforward_dim
  								a_       :   Dict			=	norm
  								a_       :   List[Any]			=	encoder_layers
  								a_       :   int			=	decoder_layers
  								a_       :   Any			=	use_task_norm
  								a_       :   Union[str, Any]			=	num_attention_heads
  								a_       :   List[str]			=	dropout
  								a_       :   List[Any]			=	dim_feedforward
  								a_       :   Union[str, Any]			=	pre_norm
  								a_       :   Dict			=	enforce_input_proj
  								a_       :   Tuple			=	query_dec_layers
  								a_       :   Any			=	common_stride
  								a_       :   List[str]			=	decoder_layers
  
  								super().__init__(**UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict[str, any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
  								a_       :   Any			=	self.backbone_config.to_dict()
  								a_       :   Union[str, Any]			=	self.__class__.model_type
  								return output
  
  
  