


'''simple docstring'''

import copy
from typing import Dict, List, Optional

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


a_						:       int      			=				{
    """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
}

a_						:       Tuple      			=				logging.get_logger(__name__)





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"mask2former"
  _lowerCamelCase						=						["swin"]
  _lowerCamelCase						=						{"hidden_size": "hidden_dim"}






  def __init__(					self    ,						UpperCamelCase = None    ,						UpperCamelCase = 256    ,						UpperCamelCase = 256    ,						UpperCamelCase = 256    ,						UpperCamelCase = 1024    ,						UpperCamelCase = "relu"    ,						UpperCamelCase = 6    ,						UpperCamelCase = 10    ,						UpperCamelCase = 8    ,						UpperCamelCase = 0.0    ,						UpperCamelCase = 2048    ,						UpperCamelCase = False    ,						UpperCamelCase = False    ,						UpperCamelCase = 4    ,						UpperCamelCase = 255    ,						UpperCamelCase = 100    ,						UpperCamelCase = 0.1    ,						UpperCamelCase = 2.0    ,						UpperCamelCase = 5.0    ,						UpperCamelCase = 5.0    ,						UpperCamelCase = 1_2544    ,						UpperCamelCase = 3.0    ,						UpperCamelCase = 0.75    ,						UpperCamelCase = 0.02    ,						UpperCamelCase = 1.0    ,						UpperCamelCase = True    ,						UpperCamelCase = [4, 8, 16, 32]    ,						UpperCamelCase = None    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   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=224    ,						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=UpperCamelCase    ,						out_features=["stage1", "stage2", "stage3", "stage4"]    ,						)

   if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
    lowerCamelCase_  				=						backbone_config.pop("model_type"  )
    lowerCamelCase_  				=						CONFIG_MAPPING[backbone_model_type]
    lowerCamelCase_  				=						config_class.from_dict(UpperCamelCase  )

   # 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__(**UpperCamelCase  )






  @classmethod
  def 						snake_case	(					cls    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return cls(
       backbone_config=UpperCamelCase    ,						**UpperCamelCase    ,						)







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						copy.deepcopy(self.__dict__  )
   lowerCamelCase_  				=						self.backbone_config.to_dict()
   lowerCamelCase_  				=						self.__class__.model_type
   return output







'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy





def __snake_case      (   UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Dict	):
 assert isinstance(UpperCAmelCase_      ,       UpperCAmelCase_	)
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype





@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"      ,       [False, True]	)
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Union[str, Any]	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowerCamelCase_  				=						SqlDatasetReader(
      "dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_      ,       keep_in_memory=UpperCAmelCase_	).read()
 _check_sql_dataset(UpperCAmelCase_      ,       UpperCAmelCase_	)





@require_sqlalchemy
@pytest.mark.parametrize(
    "features"      ,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,       )
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		str	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCamelCase_  				=						features.copy() if features else default_expected_features
 lowerCamelCase_  				=						(
     Features({feature: Value(UpperCAmelCase_	) for feature, dtype in features.items()}	) if features is not None else None
 )
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       features=UpperCAmelCase_      ,       cache_dir=UpperCAmelCase_	).read()
 _check_sql_dataset(UpperCAmelCase_      ,       UpperCAmelCase_	)





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]	):
 with contextlib.closing(sqlitea.connect(UpperCAmelCase_	)	) as con:
  lowerCamelCase_  				=						con.cursor()
  cur.execute("SELECT * FROM dataset"	)
  for row in cur:
   yield row





@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=1	).write()

 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)
 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)

 for rowa, rowa in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
  assert rowa == rowa





@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=2	).write()

 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)
 lowerCamelCase_  				=						iter_sql_file(UpperCAmelCase_	)

 for rowa, rowa in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
  assert rowa == rowa







@require_sqlalchemy
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						tmp_path / "cache"
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "tmp.sql"	)
 lowerCamelCase_  				=						SqlDatasetReader("dataset"      ,       "sqlite:///" + sqlite_path      ,       cache_dir=UpperCAmelCase_	).read()
 with pytest.raises(UpperCAmelCase_	):
  SqlDatasetWriter(UpperCAmelCase_      ,       "dataset"      ,       "sqlite:///" + output_sqlite_path      ,       num_proc=0	).write()



