
import copy
from typing import Dict, Optional

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


__lowerCamelCase							= logging.get_logger(__name__)

__lowerCamelCase							= {
    """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 						UpperCAmelCase	(		A_				):
   A__      :	List[str]		  =  "oneformer"
   A__      :	Any		  =  {"hidden_size": "hidden_dim"}



   def __init__(self	:      Any					,		snake_case__	:      Optional[Dict] = None					,		snake_case__	:      int = 2_55					,		snake_case__	:      int = 1_50					,		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_25_44					,		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 = 2_56					,		snake_case__	:      int = 77					,		snake_case__	:      int = 6					,		snake_case__	:      int = 4_94_08					,		snake_case__	:      int = 2					,		snake_case__	:      int = 16					,		snake_case__	:      int = 2_56					,		snake_case__	:      int = 2_56					,		snake_case__	:      int = 2_56					,		snake_case__	:      int = 10_24					,		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 = 20_48					,		snake_case__	:      bool = False					,		snake_case__	:      bool = False					,		snake_case__	:      int = 2					,		snake_case__	:      int = 4					,		**snake_case__	:      Optional[int]					,		)	->			Tuple:


          '''simple docstring'''


          if backbone_config is None:
                 logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."				)
                 snake_case :     str           =   CONFIG_MAPPING["swin"](
                     image_size=2_24					,		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__				):
                 snake_case :     int           =   backbone_config.get("model_type"				)
                 snake_case :     Dict           =   CONFIG_MAPPING[backbone_model_type]
                 snake_case :     List[str]           =   config_class.from_dict(snake_case__				)

          snake_case :     Optional[Any]           =   backbone_config

          snake_case :     int           =   ignore_value
          snake_case :     int           =   num_queries
          snake_case :     int           =   no_object_weight
          snake_case :     int           =   class_weight
          snake_case :     Optional[Any]           =   mask_weight
          snake_case :     Optional[int]           =   dice_weight
          snake_case :     Optional[Any]           =   contrastive_weight
          snake_case :     Union[str, Any]           =   contrastive_temperature
          snake_case :     Union[str, Any]           =   train_num_points
          snake_case :     str           =   oversample_ratio
          snake_case :     Optional[Any]           =   importance_sample_ratio
          snake_case :     Optional[int]           =   init_std
          snake_case :     str           =   init_xavier_std
          snake_case :     Dict           =   layer_norm_eps
          snake_case :     Dict           =   is_training
          snake_case :     Union[str, Any]           =   use_auxiliary_loss
          snake_case :     List[str]           =   output_auxiliary_logits
          snake_case :     Tuple           =   strides
          snake_case :     Any           =   task_seq_len
          snake_case :     Optional[Any]           =   text_encoder_width
          snake_case :     Optional[int]           =   text_encoder_context_length
          snake_case :     Union[str, Any]           =   text_encoder_num_layers
          snake_case :     Dict           =   text_encoder_vocab_size
          snake_case :     Union[str, Any]           =   text_encoder_proj_layers
          snake_case :     Dict           =   text_encoder_n_ctx
          snake_case :     Dict           =   conv_dim
          snake_case :     List[Any]           =   mask_dim
          snake_case :     Dict           =   hidden_dim
          snake_case :     str           =   encoder_feedforward_dim
          snake_case :     int           =   norm
          snake_case :     Union[str, Any]           =   encoder_layers
          snake_case :     List[Any]           =   decoder_layers
          snake_case :     Dict           =   use_task_norm
          snake_case :     int           =   num_attention_heads
          snake_case :     int           =   dropout
          snake_case :     Optional[Any]           =   dim_feedforward
          snake_case :     Optional[int]           =   pre_norm
          snake_case :     str           =   enforce_input_proj
          snake_case :     int           =   query_dec_layers
          snake_case :     Any           =   common_stride
          snake_case :     Optional[int]           =   decoder_layers

          super().__init__(**snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Dict[str, any]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   copy.deepcopy(self.__dict__				)
          snake_case :     Union[str, Any]           =   self.backbone_config.to_dict()
          snake_case :     Dict           =   self.__class__.model_type
          return output







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     UpperCamelCase      (					__lowerCamelCase  :				int					):
       return EnvironmentCommand()






class 						UpperCAmelCase	(		A_				):



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      ArgumentParser				)	->			Any:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   parser.add_parser("env"					,		help="Print relevant system environment info."				)
          download_parser.set_defaults(func=snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     List[str]           =   {
              "`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        _SCREAMING_SNAKE_CASE     (snake_case__	:      Tuple				)	->			Any:


          '''simple docstring'''


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





