
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
							import sqlitea

							import sqlalchemy







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__ = None   ,A__ = None   ,A__ = False   ,**A__   ,):
												super().__init__(features=A__   ,cache_dir=A__   ,keep_in_memory=A__   ,**A__)
												lowercase							=						Sql(
												    cache_dir=A__   ,features=A__   ,sql=A__   ,con=A__   ,**A__   ,)





							def  A__      (				self):
												lowercase							=						None
												lowercase							=						None
												lowercase							=						None
												lowercase							=						None

												self.builder.download_and_prepare(
												    download_config=A__   ,download_mode=A__   ,verification_mode=A__   ,base_path=A__   ,)

												# Build dataset for splits
												lowercase							=						self.builder.as_dataset(
												    split='''train'''   ,verification_mode=A__   ,in_memory=self.keep_in_memory)
												return dataset







class 				lowercase		:




							def __init__(				self   ,A__   ,A__   ,A__   ,A__ = None   ,A__ = None   ,**A__   ,):
												if num_proc is not None and num_proc <= 0:
																	raise ValueError(f'num_proc {num_proc} must be an integer > 0.')

												lowercase							=						dataset
												lowercase							=						name
												lowercase							=						con
												lowercase							=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
												lowercase							=						num_proc
												lowercase							=						to_sql_kwargs




							def  A__      (				self):
												lowercase							=						self.to_sql_kwargs.pop('''sql'''   ,A__)
												lowercase							=						self.to_sql_kwargs.pop('''con'''   ,A__)
												lowercase							=						self.to_sql_kwargs.pop('''index'''   ,A__)

												lowercase							=						self._write(index=A__   ,**self.to_sql_kwargs)
												return written




							def  A__      (				self   ,A__):
												lowercase	,						lowercase	,						lowercase							=						args
												lowercase							=						{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
												lowercase							=						query_table(
												    table=self.dataset.data   ,key=slice(A__   ,offset + self.batch_size)   ,indices=self.dataset._indices   ,)
												lowercase							=						batch.to_pandas()
												lowercase							=						df.to_sql(self.name   ,self.con   ,index=A__   ,**A__)
												return num_rows or len(A__)





							def  A__      (				self   ,A__   ,**A__):
												lowercase							=						0

												if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0   ,len(self.dataset)   ,self.batch_size)   ,unit='''ba'''   ,disable=not logging.is_progress_bar_enabled()   ,desc='''Creating SQL from Arrow format'''   ,):
																						written += self._batch_sql((offset, index, to_sql_kwargs))
												else:
																	lowercase	,						lowercase							=						len(self.dataset), self.batch_size
																	with multiprocessing.Pool(self.num_proc) as pool:
																						for num_rows in logging.tqdm(
																						    pool.imap(
																						        self._batch_sql   ,[(offset, index, to_sql_kwargs) for offset in range(0   ,A__   ,A__)]   ,)   ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,unit='''ba'''   ,disable=not logging.is_progress_bar_enabled()   ,desc='''Creating SQL from Arrow format'''   ,):
																											written += num_rows

												return written






import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__   :			Dict												=    {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Any        =		"""mask2former"""
		a__       :						Dict        =		["""swin"""]
		a__       :						Any        =		{"""hidden_size""": """hidden_dim"""}






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Dict] = None							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 10_24							,			SCREAMING_SNAKE_CASE__		:							str = "relu"							,			SCREAMING_SNAKE_CASE__		:							int = 6							,			SCREAMING_SNAKE_CASE__		:							int = 10							,			SCREAMING_SNAKE_CASE__		:							int = 8							,			SCREAMING_SNAKE_CASE__		:							float = 0.0							,			SCREAMING_SNAKE_CASE__		:							int = 20_48							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							int = 4							,			SCREAMING_SNAKE_CASE__		:							int = 2_55							,			SCREAMING_SNAKE_CASE__		:							int = 1_00							,			SCREAMING_SNAKE_CASE__		:							float = 0.1							,			SCREAMING_SNAKE_CASE__		:							float = 2.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							int = 1_25_44							,			SCREAMING_SNAKE_CASE__		:							float = 3.0							,			SCREAMING_SNAKE_CASE__		:							float = 0.75							,			SCREAMING_SNAKE_CASE__		:							float = 0.02							,			SCREAMING_SNAKE_CASE__		:							float = 1.0							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							List[int] = [4, 8, 16, 32]							,			SCREAMING_SNAKE_CASE__		:							bool = None							,			**SCREAMING_SNAKE_CASE__		:							Tuple							,			)     ->							str:
						if backbone_config is None:
										logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''  )
										__lowerCamelCase    					=	CONFIG_MAPPING['''swin'''](
										    image_size=2_24							,			in_channels=3							,			patch_size=4							,			embed_dim=96							,			depths=[2, 2, 18, 2]							,			num_heads=[3, 6, 12, 24]							,			window_size=7							,			drop_path_rate=0.3							,			use_absolute_embeddings=SCREAMING_SNAKE_CASE__							,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,			)

						if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
										__lowerCamelCase    					=	backbone_config.pop('''model_type'''  )
										__lowerCamelCase    					=	CONFIG_MAPPING[backbone_model_type]
										__lowerCamelCase    					=	config_class.from_dict(SCREAMING_SNAKE_CASE__  )

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
										logger.warning_once(
										    f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
										    f'''Supported model types: {','.join(self.backbones_supported  )}'''  )

						__lowerCamelCase    					=	backbone_config
						__lowerCamelCase    					=	feature_size
						__lowerCamelCase    					=	mask_feature_size
						__lowerCamelCase    					=	hidden_dim
						__lowerCamelCase    					=	encoder_feedforward_dim
						__lowerCamelCase    					=	activation_function
						__lowerCamelCase    					=	encoder_layers
						__lowerCamelCase    					=	decoder_layers
						__lowerCamelCase    					=	num_attention_heads
						__lowerCamelCase    					=	dropout
						__lowerCamelCase    					=	dim_feedforward
						__lowerCamelCase    					=	pre_norm
						__lowerCamelCase    					=	enforce_input_projection
						__lowerCamelCase    					=	common_stride
						__lowerCamelCase    					=	ignore_value
						__lowerCamelCase    					=	num_queries
						__lowerCamelCase    					=	no_object_weight
						__lowerCamelCase    					=	class_weight
						__lowerCamelCase    					=	mask_weight
						__lowerCamelCase    					=	dice_weight
						__lowerCamelCase    					=	train_num_points
						__lowerCamelCase    					=	oversample_ratio
						__lowerCamelCase    					=	importance_sample_ratio
						__lowerCamelCase    					=	init_std
						__lowerCamelCase    					=	init_xavier_std
						__lowerCamelCase    					=	use_auxiliary_loss
						__lowerCamelCase    					=	feature_strides
						__lowerCamelCase    					=	output_auxiliary_logits
						__lowerCamelCase    					=	decoder_layers

						super().__init__(**SCREAMING_SNAKE_CASE__  )






		@classmethod
		def  __A       (		cls		:							Any							,			SCREAMING_SNAKE_CASE__		:							PretrainedConfig							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							List[Any]:
						return cls(
						    backbone_config=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							Any  )     ->							Dict[str, any]:
						__lowerCamelCase    					=	copy.deepcopy(self.__dict__  )
						__lowerCamelCase    					=	self.backbone_config.to_dict()
						__lowerCamelCase    					=	self.__class__.model_type
						return output



