






"""simple docstring"""

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import torch


class 		__lowerCamelCase				( TensorFormatter[Mapping, """torch.Tensor""", Mapping]			):





		'''simple docstring'''






		def __init__(   self							:	Tuple						,			a_							:	List[str]=None						,			**a_							:	List[Any]	):
				super().__init__(features=a_	)
				lowerCAmelCase_   :							Any									=			torch_tensor_kwargs
				import torch  # noqa import torch at initialization





		def    lowerCamelCase       (   self							:	str						,			a_							:	Union[str, Any]	):
				import torch

				if isinstance(a_						,			a_	) and column:
						if all(
						    isinstance(a_						,			torch.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype
						    for x in column	):
								return torch.stack(a_	)
				return column





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	str	):
				import torch

				if isinstance(a_						,			(str, bytes, type(a_	))	):
						return value
				elif isinstance(a_						,			(np.character, np.ndarray)	) and np.issubdtype(value.dtype						,			np.character	):
						return value.tolist()

				lowerCAmelCase_   :							Optional[int]									=			{}

				if isinstance(a_						,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype						,			np.integer	):
						lowerCAmelCase_   :							List[str]									=			{"dtype": torch.intaa}
				elif isinstance(a_						,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype						,			np.floating	):
						lowerCAmelCase_   :							List[str]									=			{"dtype": torch.floataa}
				elif config.PIL_AVAILABLE and "PIL" in sys.modules:
						import PIL.Image

						if isinstance(a_						,			PIL.Image.Image	):
								lowerCAmelCase_   :							int									=			np.asarray(a_	)
				return torch.tensor(a_						,			**{**default_dtype, **self.torch_tensor_kwargs}	)





		def    lowerCamelCase       (   self							:	Dict						,			a_							:	Optional[Any]	):
				import torch

				# support for torch, tf, jax etc.
				if hasattr(a_						,			"__array__"	) and not isinstance(a_						,			torch.Tensor	):
						lowerCAmelCase_   :							Dict									=			data_struct.__array__()
				# support for nested types like struct of list of struct
				if isinstance(a_						,			np.ndarray	):
						if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
								return self._consolidate([self.recursive_tensorize(a_	) for substruct in data_struct]	)
				elif isinstance(a_						,			(list, tuple)	):
						return self._consolidate([self.recursive_tensorize(a_	) for substruct in data_struct]	)
				return self._tensorize(a_	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	dict	):
				return map_nested(self._recursive_tensorize						,			a_						,			map_list=a_	)





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	pa.Table	):
				lowerCAmelCase_   :							Any									=			self.numpy_arrow_extractor().extract_row(a_	)
				lowerCAmelCase_   :							str									=			self.python_features_decoder.decode_row(a_	)
				return self.recursive_tensorize(a_	)





		def    lowerCamelCase       (   self							:	Any						,			a_							:	pa.Table	):
				lowerCAmelCase_   :							Tuple									=			self.numpy_arrow_extractor().extract_column(a_	)
				lowerCAmelCase_   :							Tuple									=			self.python_features_decoder.decode_column(a_						,			pa_table.column_names[0]	)
				lowerCAmelCase_   :							Optional[int]									=			self.recursive_tensorize(a_	)
				lowerCAmelCase_   :							List[Any]									=			self._consolidate(a_	)
				return column





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	pa.Table	):
				lowerCAmelCase_   :							Optional[Any]									=			self.numpy_arrow_extractor().extract_batch(a_	)
				lowerCAmelCase_   :							Optional[Any]									=			self.python_features_decoder.decode_batch(a_	)
				lowerCAmelCase_   :							Optional[int]									=			self.recursive_tensorize(a_	)
				for column_name in batch:
						lowerCAmelCase_   :							Dict									=			self._consolidate(batch[column_name]	)
				return batch








"""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


lowercase__          = {
    """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
}

lowercase__          = logging.get_logger(__name__)


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				Optional[int]            =							"""maskformer"""
		a_	:				Optional[int]            =							{"""hidden_size""": """mask_feature_size"""}
		a_	:				Optional[int]            =							["""resnet""", """swin"""]
		a_	:				int            =							["""detr"""]





		def __init__(   self							:	str						,			a_							:	int = 2_56						,			a_							:	int = 2_56						,			a_							:	float = 0.1						,			a_							:	bool = False						,			a_							:	Optional[Dict] = None						,			a_							:	Optional[Dict] = None						,			a_							:	float = 0.02						,			a_							:	float = 1.0						,			a_							:	float = 1.0						,			a_							:	float = 1.0						,			a_							:	float = 20.0						,			a_							:	Optional[bool] = None						,			**a_							:	str						,			):
				if backbone_config is None:
						# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
						lowerCAmelCase_   :							Tuple									=			SwinConfig(
						    image_size=3_84						,			in_channels=3						,			patch_size=4						,			embed_dim=1_28						,			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(a_						,			a_	):
						lowerCAmelCase_   :							Optional[Any]									=			backbone_config.pop("model_type"	)
						lowerCAmelCase_   :							Any									=			CONFIG_MAPPING[backbone_model_type]
						lowerCAmelCase_   :							str									=			config_class.from_dict(a_	)

				# 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_   :							Union[str, Any]									=			DetrConfig()
				else:
						# verify that the decoder is supported
						lowerCAmelCase_   :							Optional[Any]									=			(
						    decoder_config.pop("model_type"	) if isinstance(a_						,			a_	) 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(a_						,			a_	):
								lowerCAmelCase_   :							Optional[int]									=			CONFIG_MAPPING[decoder_type]
								lowerCAmelCase_   :							List[Any]									=			config_class.from_dict(a_	)

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

				lowerCAmelCase_   :							int									=			self.decoder_config.encoder_attention_heads
				lowerCAmelCase_   :							str									=			self.decoder_config.num_hidden_layers
				super().__init__(**a_	)





		@classmethod
		def    lowerCamelCase       (   cls							:	int						,			a_							:	PretrainedConfig						,			a_							:	PretrainedConfig						,			**a_							:	Tuple	):
				return cls(
				    backbone_config=a_						,			decoder_config=a_						,			**a_						,			)





		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							Optional[int]									=			copy.deepcopy(self.__dict__	)
				lowerCAmelCase_   :							Optional[Any]									=			self.backbone_config.to_dict()
				lowerCAmelCase_   :							Union[str, Any]									=			self.decoder_config.to_dict()
				lowerCAmelCase_   :							List[str]									=			self.__class__.model_type
				return output
