



"""simple docstring"""
import copy
from typing import Dict, Optional

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


_lowerCAmelCase					:List[str]  =      logging.get_logger(__name__)

_lowerCAmelCase					:Union[str, Any]  =      {
    '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_   ):





     '''simple docstring'''



     a__             ='oneformer'
     a__             ={'hidden_size': 'hidden_dim'}





     def __init__(      self ,			A = None ,			A = 2_5_5 ,			A = 1_5_0 ,			A = 0.1 ,			A = 2.0 ,			A = 5.0 ,			A = 5.0 ,			A = 0.5 ,			A = 0.07 ,			A = 1_2_5_4_4 ,			A = 3.0 ,			A = 0.75 ,			A = 0.02 ,			A = 1.0 ,			A = 1E-05 ,			A = False ,			A = True ,			A = True ,			A = [4, 8, 1_6, 3_2] ,			A = 7_7 ,			A = 2_5_6 ,			A = 7_7 ,			A = 6 ,			A = 4_9_4_0_8 ,			A = 2 ,			A = 1_6 ,			A = 2_5_6 ,			A = 2_5_6 ,			A = 2_5_6 ,			A = 1_0_2_4 ,			A = "GN" ,			A = 6 ,			A = 1_0 ,			A = True ,			A = 8 ,			A = 0.1 ,			A = 2_0_4_8 ,			A = False ,			A = False ,			A = 2 ,			A = 4 ,			**A ,			)							->      List[Any]:
         if backbone_config is None:
             logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''	)
             _UpperCAmelCase							:						str       							=					CONFIG_MAPPING['swin'](
                 image_size=2_2_4 ,			in_channels=3 ,			patch_size=4 ,			embed_dim=9_6 ,			depths=[2, 2, 6, 2] ,			num_heads=[3, 6, 1_2, 2_4] ,			window_size=7 ,			drop_path_rate=0.3 ,			use_absolute_embeddings=A ,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,			)
         elif isinstance(A ,			A	):
             _UpperCAmelCase							:						Any       							=					backbone_config.get('''model_type'''	)
             _UpperCAmelCase							:						Union[str, Any]       							=					CONFIG_MAPPING[backbone_model_type]
             _UpperCAmelCase							:						Optional[Any]       							=					config_class.from_dict(A	)

         _UpperCAmelCase							:						Union[str, Any]       							=					backbone_config

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

         super().__init__(**A	)





     def 				__lowerCAmelCase			(      self	)							->      Dict[str, any]:
         _UpperCAmelCase							:						List[str]       							=					copy.deepcopy(self.__dict__	)
         _UpperCAmelCase							:						Dict       							=					self.backbone_config.to_dict()
         _UpperCAmelCase							:						Any       							=					self.__class__.model_type
         return output







"""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 tensorflow as tf
class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):





     '''simple docstring'''



     def __init__(      self ,			A=None ,			**A	)							->      List[str]:
         super().__init__(features=A	)
         _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
         import tensorflow as tf  # noqa: import tf at initialization





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
         import tensorflow as tf

         if isinstance(A ,			A	) and column:
             if all(
                 isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                 return tf.stack(A	)
             elif all(
                 isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                 for x in column	):
                 # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                 return tf.ragged.stack(A	)

         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      int:
         import tensorflow as tf

         if value is None:
             return value

         _UpperCAmelCase							:						Any       							=					{}

         if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
             _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
         elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
             _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(A ,			PIL.Image.Image	):
                 _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)

         return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
         import tensorflow as tf

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

             if isinstance(A ,			torch.Tensor	):
                 return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
         if hasattr(A ,			'''__array__'''	) and not isinstance(A ,			tf.Tensor	):
             _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(A ,			np.ndarray	):
             if data_struct.dtype == object:  # tf 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 ,			A	)							->      Optional[int]:
         return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
         _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
         return self.recursive_tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
         _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
         _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
         _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
         for column_name in batch:
             _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
         return batch


