


"""simple docstring"""







import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase__				    =       {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase__				    =       logging.get_logger(__name__)


class 				SCREAMING_SNAKE_CASE__    (		_UpperCamelCase    ):
       """simple docstring"""


       a  :    Optional[Any]       ='maskformer'
       a  :    Optional[int]       ={'hidden_size': 'mask_feature_size'}
       a  :    int       =['resnet', 'swin']
       a  :    Any       =['detr']

       def __init__(						self		,					snake_case__ = 256		,					snake_case__ = 256		,					snake_case__ = 0.1		,					snake_case__ = False		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = 0.02		,					snake_case__ = 1.0		,					snake_case__ = 1.0		,					snake_case__ = 1.0		,					snake_case__ = 20.0		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowerCAmelCase					:					List[str]								=							SwinConfig(
               image_size=384		,					in_channels=3		,					patch_size=4		,					embed_dim=128		,					depths=[2, 2, 18, 2]		,					num_heads=[4, 8, 16, 32]		,					window_size=12		,					drop_path_rate=0.3		,					out_features=["stage1", "stage2", "stage3", "stage4"]		,					)

         if isinstance(_UpperCAmelCase		,					_UpperCAmelCase						):
           lowerCAmelCase					:					Union[str, Any]								=							backbone_config.pop("model_type"						)
           lowerCAmelCase					:					Tuple								=							CONFIG_MAPPING[backbone_model_type]
           lowerCAmelCase					:					List[str]								=							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 MaskFormer. """
               f"""Supported model types: {",".join(self.backbones_supported						)}"""						)

         if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowerCAmelCase					:					int								=							DetrConfig()
         else:
           # verify that the decoder is supported
           lowerCAmelCase					:					Optional[int]								=							(
               decoder_config.pop("model_type"						) if isinstance(_UpperCAmelCase		,					_UpperCAmelCase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
             raise ValueError(
                 f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                 f""" {",".join(self.decoders_supported						)}"""						)
           if isinstance(_UpperCAmelCase		,					_UpperCAmelCase						):
             lowerCAmelCase					:					int								=							CONFIG_MAPPING[decoder_type]
             lowerCAmelCase					:					List[Any]								=							config_class.from_dict(_UpperCAmelCase						)

         lowerCAmelCase					:					Tuple								=							backbone_config
         lowerCAmelCase					:					int								=							decoder_config
         # main feature dimension for the model
         lowerCAmelCase					:					Any								=							fpn_feature_size
         lowerCAmelCase					:					Tuple								=							mask_feature_size
         # initializer
         lowerCAmelCase					:					List[Any]								=							init_std
         lowerCAmelCase					:					Optional[int]								=							init_xavier_std
         # Hungarian matcher && loss
         lowerCAmelCase					:					List[Any]								=							cross_entropy_weight
         lowerCAmelCase					:					Dict								=							dice_weight
         lowerCAmelCase					:					List[str]								=							mask_weight
         lowerCAmelCase					:					int								=							use_auxiliary_loss
         lowerCAmelCase					:					List[Any]								=							no_object_weight
         lowerCAmelCase					:					List[str]								=							output_auxiliary_logits

         lowerCAmelCase					:					Tuple								=							self.decoder_config.encoder_attention_heads
         lowerCAmelCase					:					Union[str, Any]								=							self.decoder_config.num_hidden_layers
         super().__init__(**_UpperCAmelCase						)

       @classmethod
       def 	lowercase__							(						cls		,					snake_case__		,					snake_case__		,					**snake_case__						):






         """simple docstring"""





         return cls(
             backbone_config=_UpperCAmelCase		,					decoder_config=_UpperCAmelCase		,					**_UpperCAmelCase		,					)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							copy.deepcopy(self.__dict__						)
         lowerCAmelCase					:					Optional[int]								=							self.backbone_config.to_dict()
         lowerCAmelCase					:					Tuple								=							self.decoder_config.to_dict()
         lowerCAmelCase					:					Any								=							self.__class__.model_type
         return output









"""simple docstring"""







import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  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
@pytest.mark.parametrize("keep_in_memory"    ,				[False, True]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[int]								=							{"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					:					str								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				keep_in_memory=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@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  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[str]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					int								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				split=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"    ,				[str, list]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Optional[Any]								=							parquet_path
  elif issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Dict								=							[parquet_path]
  lowerCAmelCase					:					Optional[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Any								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   Tuple=("train",)    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  for split in splits:
    lowerCAmelCase					:					List[Any]								=							dataset_dict[split]
    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
@pytest.mark.parametrize("keep_in_memory"    ,				[False, True]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							tmp_path / "cache"
  lowerCAmelCase					:					List[Any]								=							{"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					:					Optional[Any]								=							ParquetDatasetReader(
        {"train": parquet_path}    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				keep_in_memory=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@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  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							tmp_path / "cache"
  lowerCAmelCase					:					Union[str, Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[Any]								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[Any]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader({"train": parquet_path}    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if split:
    lowerCAmelCase					:					List[str]								=							{split: parquet_path}
  else:
    lowerCAmelCase					:					List[str]								=							"train"
    lowerCAmelCase					:					str								=							{"train": parquet_path, "test": parquet_path}
  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					List[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0
  lowerCAmelCase					:					List[str]								=							pq.ParquetFile(tmp_path / "foo.parquet"    )
  lowerCAmelCase					:					Union[str, Any]								=							pf.read()
  assert dataset.data.table == output_table
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							str(shared_datadir / "test_image_rgb.jpg"    )
  lowerCAmelCase					:					List[str]								=							{"image": [image_path]}
  lowerCAmelCase					:					str								=							Features({"image": Image()}    )
  lowerCAmelCase					:					Optional[Any]								=							Dataset.from_dict(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0

  lowerCAmelCase					:					Any								=							Dataset.from_parquet(str(tmp_path / "foo.parquet"    )    )
  assert dataset.features == reloaded_dataset.features

  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader(str(tmp_path / "foo.parquet"    )    ,				streaming=SCREAMING_SNAKE_CASE    ).read()
  assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    "feature, expected"    ,				[
        (Features({"foo": Value("int32"    )}    ), None),
        (Features({"image": Image(), "foo": Value("int32"    )}    ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()    )}    ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,				)
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  assert get_writer_batch_size(SCREAMING_SNAKE_CASE    ) == expected





