"""simple docstring"""



import copy
from typing import Dict, Optional

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


__magic_name__    				=					logging.get_logger(__name__)

__magic_name__    				=					{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Union[str, Any]  			="oneformer"
 a_       :	List[str]  			={"hidden_size": "hidden_dim"}


 def __init__(   self		:      Optional[int]					,   _snake_case		:      Optional[Dict] = None					,   _snake_case		:      int = 255					,   _snake_case		:      int = 150					,   _snake_case		:      int = 0.1					,   _snake_case		:      float = 2.0					,   _snake_case		:      float = 5.0					,   _snake_case		:      float = 5.0					,   _snake_case		:      float = 0.5					,   _snake_case		:      float = 0.07					,   _snake_case		:      int = 1_2544					,   _snake_case		:      float = 3.0					,   _snake_case		:      float = 0.75					,   _snake_case		:      float = 0.02					,   _snake_case		:      float = 1.0					,   _snake_case		:      float = 1E-05					,   _snake_case		:      bool = False					,   _snake_case		:      bool = True					,   _snake_case		:      bool = True					,   _snake_case		:      Optional[list] = [4, 8, 16, 32]					,   _snake_case		:      int = 77					,   _snake_case		:      int = 256					,   _snake_case		:      int = 77					,   _snake_case		:      int = 6					,   _snake_case		:      int = 4_9408					,   _snake_case		:      int = 2					,   _snake_case		:      int = 16					,   _snake_case		:      int = 256					,   _snake_case		:      int = 256					,   _snake_case		:      int = 256					,   _snake_case		:      int = 1024					,   _snake_case		:      str = "GN"					,   _snake_case		:      int = 6					,   _snake_case		:      int = 10					,   _snake_case		:      bool = True					,   _snake_case		:      int = 8					,   _snake_case		:      float = 0.1					,   _snake_case		:      int = 2048					,   _snake_case		:      bool = False					,   _snake_case		:      bool = False					,   _snake_case		:      int = 2					,   _snake_case		:      int = 4					,   **_snake_case		:      int					,   )				->			Union[str, Any]:




    '''simple docstring'''






    if backbone_config is None:
       logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'				)
       a__     				=		CONFIG_MAPPING['swin'](
           image_size=224					,   in_channels=3					,   patch_size=4					,   embed_dim=96					,   depths=[2, 2, 6, 2]					,   num_heads=[3, 6, 12, 24]					,   window_size=7					,   drop_path_rate=0.3					,   use_absolute_embeddings=_snake_case					,   out_features=['stage1', 'stage2', 'stage3', 'stage4']					,   )
    elif isinstance(_snake_case					,   _snake_case				):
       a__     				=		backbone_config.get('model_type'				)
       a__     				=		CONFIG_MAPPING[backbone_model_type]
       a__     				=		config_class.from_dict(_snake_case				)

    a__     				=		backbone_config

    a__     				=		ignore_value
    a__     				=		num_queries
    a__     				=		no_object_weight
    a__     				=		class_weight
    a__     				=		mask_weight
    a__     				=		dice_weight
    a__     				=		contrastive_weight
    a__     				=		contrastive_temperature
    a__     				=		train_num_points
    a__     				=		oversample_ratio
    a__     				=		importance_sample_ratio
    a__     				=		init_std
    a__     				=		init_xavier_std
    a__     				=		layer_norm_eps
    a__     				=		is_training
    a__     				=		use_auxiliary_loss
    a__     				=		output_auxiliary_logits
    a__     				=		strides
    a__     				=		task_seq_len
    a__     				=		text_encoder_width
    a__     				=		text_encoder_context_length
    a__     				=		text_encoder_num_layers
    a__     				=		text_encoder_vocab_size
    a__     				=		text_encoder_proj_layers
    a__     				=		text_encoder_n_ctx
    a__     				=		conv_dim
    a__     				=		mask_dim
    a__     				=		hidden_dim
    a__     				=		encoder_feedforward_dim
    a__     				=		norm
    a__     				=		encoder_layers
    a__     				=		decoder_layers
    a__     				=		use_task_norm
    a__     				=		num_attention_heads
    a__     				=		dropout
    a__     				=		dim_feedforward
    a__     				=		pre_norm
    a__     				=		enforce_input_proj
    a__     				=		query_dec_layers
    a__     				=		common_stride
    a__     				=		decoder_layers

    super().__init__(**_snake_case				)



 def        _lowerCAmelCase				(   self		:      int				)				->			Dict[str, any]:




    '''simple docstring'''






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



"""simple docstring"""



import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Any:


   '''simple docstring'''


   return EnvironmentCommand()




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      ArgumentParser				)				->			int:




    '''simple docstring'''






    a__     				=		parser.add_parser('env'					,   help='Print relevant system environment info.'				)
    download_parser.set_defaults(func=_snake_case				)


 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		{
        '`datasets` version': version,
        'Platform': platform.platform(),
        'Python version': platform.python_version(),
        'Huggingface_hub version': huggingface_hub.__version__,
        'PyArrow version': pyarrow.__version__,
        'Pandas version': pandas.__version__,
    }

    print('\nCopy-and-paste the text below in your GitHub issue.\n'				)
    print(self.format_dict(_snake_case				)				)

    return info



 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]				) + "\n"


