
import copy
from typing import Dict, Optional

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


A  :			List[Any]     				=				logging.get_logger(__name__)

A  :			int     				=				{
    '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     __A(				a    ):
				snake_case_		     =					'''oneformer'''
				snake_case_		     =					{'''hidden_size''': '''hidden_dim'''}




				def __init__(  self	,  _snake_case = None	,  _snake_case = 255	,  _snake_case = 150	,  _snake_case = 0.1	,  _snake_case = 2.0	,  _snake_case = 5.0	,  _snake_case = 5.0	,  _snake_case = 0.5	,  _snake_case = 0.07	,  _snake_case = 12_544	,  _snake_case = 3.0	,  _snake_case = 0.75	,  _snake_case = 0.02	,  _snake_case = 1.0	,  _snake_case = 1E-05	,  _snake_case = False	,  _snake_case = True	,  _snake_case = True	,  _snake_case = [4, 8, 16, 32]	,  _snake_case = 77	,  _snake_case = 256	,  _snake_case = 77	,  _snake_case = 6	,  _snake_case = 49_408	,  _snake_case = 2	,  _snake_case = 16	,  _snake_case = 256	,  _snake_case = 256	,  _snake_case = 256	,  _snake_case = 1_024	,  _snake_case = "GN"	,  _snake_case = 6	,  _snake_case = 10	,  _snake_case = True	,  _snake_case = 8	,  _snake_case = 0.1	,  _snake_case = 2_048	,  _snake_case = False	,  _snake_case = False	,  _snake_case = 2	,  _snake_case = 4	,  **_snake_case	,  )					->   Optional[int]:


								'''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        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict[str, any]:


								'''simple docstring'''




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

# 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     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

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

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

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

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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,  _snake_case	,  map_list=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_column(_snake_case       )
								__a    							=					self.python_features_decoder.decode_column(_snake_case	,  pa_table.column_names[0]       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								__a    							=					self._consolidate(_snake_case       )
								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_batch(_snake_case       )
								__a    							=					self.python_features_decoder.decode_batch(_snake_case       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								for column_name in batch:
												__a    							=					self._consolidate(batch[column_name]       )
								return batch