


import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


SCREAMING_SNAKE_CASE : Tuple          =    (30, 4_87)
SCREAMING_SNAKE_CASE : List[Any]          =    (36, 10_24)
SCREAMING_SNAKE_CASE : Optional[int]          =    (1_00, 1_00)
SCREAMING_SNAKE_CASE : List[Any]          =    1_00

SCREAMING_SNAKE_CASE : Union[str, Any]          =    datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE : List[str]          =    os.path.split(__file__)
SCREAMING_SNAKE_CASE : Tuple          =    os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: List[str]	,	SCREAMING_SNAKE_CASE_				: Optional[int]      ):






							"""simple docstring"""



							with ArrowWriter(features=SCREAMING_SNAKE_CASE_	,	path=os.path.join(SCREAMING_SNAKE_CASE_	,	"""beta.arrow"""      )      ) as writer:
														for key, record in dummy_data:
																					a_       :   List[str]			=	my_features.encode_example(SCREAMING_SNAKE_CASE_      )
																					writer.write(SCREAMING_SNAKE_CASE_      )
														a_		,				a_       :   Optional[Any]			=	writer.finalize()





@get_duration
def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: List[str]      ):






							"""simple docstring"""



							a_       :   Any			=	datasets.Dataset.from_file(
							    filename=os.path.join(SCREAMING_SNAKE_CASE_	,	"""beta.arrow"""      )	,	info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
							for _ in dataset:
														pass





@get_duration
def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: str      ):






							"""simple docstring"""



							a_       :   Optional[int]			=	datasets.Dataset.from_file(
							    filename=os.path.join(SCREAMING_SNAKE_CASE_	,	"""beta.arrow"""      )	,	info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
							dataset.set_format("""numpy"""      )
							for _ in dataset:
														pass





@get_duration
def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: List[Any]      ):






							"""simple docstring"""



							a_       :   List[str]			=	10
							a_       :   Optional[Any]			=	datasets.Dataset.from_file(
							    filename=os.path.join(SCREAMING_SNAKE_CASE_	,	"""beta.arrow"""      )	,	info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
							for i in range(0	,	len(SCREAMING_SNAKE_CASE_      )	,	SCREAMING_SNAKE_CASE_      ):
														a_       :   Optional[Any]			=	dataset[i : i + batch_size]





@get_duration
def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: Dict      ):






							"""simple docstring"""



							a_       :   Any			=	10
							a_       :   Union[str, Any]			=	datasets.Dataset.from_file(
							    filename=os.path.join(SCREAMING_SNAKE_CASE_	,	"""beta.arrow"""      )	,	info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
							dataset.set_format("""numpy"""      )
							for i in range(0	,	len(SCREAMING_SNAKE_CASE_      )	,	SCREAMING_SNAKE_CASE_      ):
														a_       :   List[Any]			=	dataset[i : i + batch_size]





@get_duration
def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: int      ):






							"""simple docstring"""



							a_       :   List[Any]			=	datasets.Dataset.from_file(
							    filename=os.path.join(SCREAMING_SNAKE_CASE_	,	"""beta.arrow"""      )	,	info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
							for col in feats:
														a_       :   Optional[int]			=	dataset[col]





@get_duration
def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: Union[str, Any]      ):






							"""simple docstring"""



							a_       :   Optional[Any]			=	datasets.Dataset.from_file(
							    filename=os.path.join(SCREAMING_SNAKE_CASE_	,	"""beta.arrow"""      )	,	info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
							dataset.set_format("""numpy"""      )
							for col in feats:
														a_       :   Tuple			=	dataset[col]





def 						_lowerCamelCase		(							):






							"""simple docstring"""



							a_       :   Optional[Any]			=	{}
							a_       :   Dict			=	(
							    read_unformated,
							    read_formatted_as_numpy,
							    read_batch_unformated,
							    read_batch_formatted_as_numpy,
							    read_col_unformated,
							    read_col_formatted_as_numpy,
							)
							with tempfile.TemporaryDirectory() as tmp_dir:
														a_       :   Optional[Any]			=	datasets.Features({"""image""": ArrayaD(SCREAMING_SNAKE_CASE_	,	dtype="""float32"""      )}      )
														a_       :   Tuple			=	generate_examples(features=SCREAMING_SNAKE_CASE_	,	num_examples=SCREAMING_SNAKE_CASE_      )
														a_       :   Union[str, Any]			=	write(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
														for read_func in read_functions:
																					a_       :   List[Any]			=	read_func(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
														# )
														a_       :   List[Any]			=	datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""      )      )      )}      )
														a_       :   int			=	generate_examples(
														    features=SCREAMING_SNAKE_CASE_	,	num_examples=SCREAMING_SNAKE_CASE_	,	seq_shapes={"""image""": SPEED_TEST_SHAPE}      )
														a_       :   int			=	write(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
														for read_func in read_functions:
																					a_       :   Any			=	read_func(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
														# )
														a_       :   Optional[int]			=	datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""      )      )}      )
														a_       :   Union[str, Any]			=	generate_examples(
														    features=SCREAMING_SNAKE_CASE_	,	num_examples=SCREAMING_SNAKE_CASE_	,	seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}	,	)
														a_       :   int			=	write(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
														for read_func in read_functions:
																					a_       :   Optional[Any]			=	read_func(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )

							with open(SCREAMING_SNAKE_CASE_	,	"""wb"""      ) as f:
														f.write(json.dumps(SCREAMING_SNAKE_CASE_      ).encode("""utf-8"""      )      )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_array_xd()






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


