

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 					a__						(					_UpperCAmelCase		):
			"""simple docstring"""







			__lowerCamelCase        =       'maskformer'
			__lowerCamelCase        =       {'hidden_size': 'mask_feature_size'}
			__lowerCamelCase        =       ['resnet', 'swin']
			__lowerCamelCase        =       ['detr']


			def __init__(    self      ,      lowercase = 256      ,      lowercase = 256      ,      lowercase = 0.1      ,      lowercase = False      ,      lowercase = None      ,      lowercase = None      ,      lowercase = 0.02      ,      lowercase = 1.0      ,      lowercase = 1.0      ,      lowercase = 1.0      ,      lowercase = 20.0      ,      lowercase = None      ,      **lowercase      ,      )  ->						str:
							'''simple docstring'''






							if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											A__        =			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(lowercase__      ,      lowercase__       ):
											A__        =			backbone_config.pop("model_type"       )
											A__        =			CONFIG_MAPPING[backbone_model_type]
											A__        =			config_class.from_dict(lowercase__       )

							# 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
											A__        =			DetrConfig()
							else:
											# verify that the decoder is supported
											A__        =			(
											    decoder_config.pop("model_type"       ) if isinstance(lowercase__      ,      lowercase__       ) 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(lowercase__      ,      lowercase__       ):
															A__        =			CONFIG_MAPPING[decoder_type]
															A__        =			config_class.from_dict(lowercase__       )

							A__        =			backbone_config
							A__        =			decoder_config
							# main feature dimension for the model
							A__        =			fpn_feature_size
							A__        =			mask_feature_size
							# initializer
							A__        =			init_std
							A__        =			init_xavier_std
							# Hungarian matcher && loss
							A__        =			cross_entropy_weight
							A__        =			dice_weight
							A__        =			mask_weight
							A__        =			use_auxiliary_loss
							A__        =			no_object_weight
							A__        =			output_auxiliary_logits

							A__        =			self.decoder_config.encoder_attention_heads
							A__        =			self.decoder_config.num_hidden_layers
							super().__init__(**lowercase__       )


			@classmethod
			def      UpperCamelCase		(    cls      ,      lowercase      ,      lowercase      ,      **lowercase       )  ->						int:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			copy.deepcopy(self.__dict__       )
							A__        =			self.backbone_config.to_dict()
							A__        =			self.decoder_config.to_dict()
							A__        =			self.__class__.model_type
							return output




lowerCAmelCase__	   = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							bytes      )			->      bytes:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			F'a bytes-like object is required, not \'{data.__class__.__name__}\''
								raise TypeError(SCREAMING_SNAKE_CASE_      )

				A__        =			"".join(bin(SCREAMING_SNAKE_CASE_      )[2:].zfill(8      ) for byte in data      )

				A__        =			len(SCREAMING_SNAKE_CASE_      ) % 6 != 0

				if padding_needed:
								# The padding that will be added later
								A__        =			b"=" * ((6 - len(SCREAMING_SNAKE_CASE_      ) % 6) // 2)

								# Append binary_stream with arbitrary binary digits (0's by default) to make its
								# length a multiple of 6.
								binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE_      ) % 6)
				else:
								A__        =			b""

				# Encode every 6 binary digits to their corresponding Base64 character
				return (
				    "".join(
				        B64_CHARSET[int(binary_stream[index : index + 6]	,       2      )]
				        for index in range(0	,       len(SCREAMING_SNAKE_CASE_      )	,       6      )      ).encode()
				    + padding
				)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str      )			->      bytes:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ) and not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			(
								    "argument should be a bytes-like object or ASCII string, "
								    F'not \'{encoded_data.__class__.__name__}\''
								)
								raise TypeError(SCREAMING_SNAKE_CASE_      )

				# In case encoded_data is a bytes-like object, make sure it contains only
				# ASCII characters so we convert it to a string object
				if isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								try:
												A__        =			encoded_data.decode("utf-8"      )
								except UnicodeDecodeError:
												raise ValueError("base64 encoded data should only contain ASCII characters"      )

				A__        =			encoded_data.count("="      )

				# Check if the encoded string contains non base64 characters
				if padding:
								assert all(
								    char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
				else:
								assert all(
								    char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

				# Check the padding
				assert len(SCREAMING_SNAKE_CASE_      ) % 4 == 0 and padding < 3, "Incorrect padding"

				if padding:
								# Remove padding if there is one
								A__        =			encoded_data[:-padding]

								A__        =			"".join(
								    bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE_      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
				else:
								A__        =			"".join(
								    bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE_      )      )[2:].zfill(6      ) for char in encoded_data      )

				A__        =			[
				    int(binary_stream[index : index + 8]	,       2      )
				    for index in range(0	,       len(SCREAMING_SNAKE_CASE_      )	,       8      )
				]

				return bytes(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

