
import copy
from typing import Dict, List, Optional

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


_UpperCAmelCase      :				Dict          = {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_UpperCAmelCase      :				Dict          = logging.get_logger(__name__)


class        lowercase      (	_lowercase     ):
  __lowercase : List[Any]			  =       '''mask2former'''
  __lowercase : Dict			  =       ['''swin''']
  __lowercase : Union[str, Any]			  =       {'''hidden_size''': '''hidden_dim'''}



  def __init__(				self       ,   A_ = None       ,   A_ = 256       ,   A_ = 256       ,   A_ = 256       ,   A_ = 1_024       ,   A_ = "relu"       ,   A_ = 6       ,   A_ = 10       ,   A_ = 8       ,   A_ = 0.0       ,   A_ = 2_048       ,   A_ = False       ,   A_ = False       ,   A_ = 4       ,   A_ = 255       ,   A_ = 100       ,   A_ = 0.1       ,   A_ = 2.0       ,   A_ = 5.0       ,   A_ = 5.0       ,   A_ = 12_544       ,   A_ = 3.0       ,   A_ = 0.75       ,   A_ = 0.02       ,   A_ = 1.0       ,   A_ = True       ,   A_ = [4, 8, 16, 32]       ,   A_ = None       ,   **A_       ,   )						->      Optional[int]:
    """simple docstring"""
    if backbone_config is None:
      logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
      UpperCamelCase						=  CONFIG_MAPPING['swin'](
          image_size=224       ,   in_channels=3       ,   patch_size=4       ,   embed_dim=96       ,   depths=[2, 2, 18, 2]       ,   num_heads=[3, 6, 12, 24]       ,   window_size=7       ,   drop_path_rate=0.3       ,   use_absolute_embeddings=A_       ,   out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,   )

    if isinstance(A_       ,   A_ ):
      UpperCamelCase						=  backbone_config.pop('model_type' )
      UpperCamelCase						=  CONFIG_MAPPING[backbone_model_type]
      UpperCamelCase						=  config_class.from_dict(A_ )

    # 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 Mask2Former. '''
          F'''Supported model types: {','.join(self.backbones_supported )}''' )

    UpperCamelCase						=  backbone_config
    UpperCamelCase						=  feature_size
    UpperCamelCase						=  mask_feature_size
    UpperCamelCase						=  hidden_dim
    UpperCamelCase						=  encoder_feedforward_dim
    UpperCamelCase						=  activation_function
    UpperCamelCase						=  encoder_layers
    UpperCamelCase						=  decoder_layers
    UpperCamelCase						=  num_attention_heads
    UpperCamelCase						=  dropout
    UpperCamelCase						=  dim_feedforward
    UpperCamelCase						=  pre_norm
    UpperCamelCase						=  enforce_input_projection
    UpperCamelCase						=  common_stride
    UpperCamelCase						=  ignore_value
    UpperCamelCase						=  num_queries
    UpperCamelCase						=  no_object_weight
    UpperCamelCase						=  class_weight
    UpperCamelCase						=  mask_weight
    UpperCamelCase						=  dice_weight
    UpperCamelCase						=  train_num_points
    UpperCamelCase						=  oversample_ratio
    UpperCamelCase						=  importance_sample_ratio
    UpperCamelCase						=  init_std
    UpperCamelCase						=  init_xavier_std
    UpperCamelCase						=  use_auxiliary_loss
    UpperCamelCase						=  feature_strides
    UpperCamelCase						=  output_auxiliary_logits
    UpperCamelCase						=  decoder_layers

    super().__init__(**A_ )



  @classmethod
  def __UpperCamelCase				(				cls       ,   A_       ,   **A_ )						->      Union[str, Any]:
    """simple docstring"""
    return cls(
        backbone_config=A_       ,   **A_       ,   )



  def __UpperCamelCase				(				self )						->      Dict[str, any]:
    """simple docstring"""
    UpperCamelCase						=  copy.deepcopy(self.__dict__ )
    UpperCamelCase						=  self.backbone_config.to_dict()
    UpperCamelCase						=  self.__class__.model_type
    return output


import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset


@pytest.mark.parametrize('dataset_size'					, [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('input_in_memory_max_size'					, ['default', 0, 100 * 2**20, 900 * 2**20]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if input_in_memory_max_size != "default":
				monkeypatch.setattr(datasets.config					, 'IN_MEMORY_MAX_SIZE'					, lowercase				)
		UpperCamelCase						=  datasets.config.IN_MEMORY_MAX_SIZE
		if input_in_memory_max_size == "default":
				assert in_memory_max_size == 0
		else:
				assert in_memory_max_size == input_in_memory_max_size
		if dataset_size and in_memory_max_size:
				UpperCamelCase						=  dataset_size < in_memory_max_size
		else:
				UpperCamelCase						=  False
		UpperCamelCase						=  is_small_dataset(lowercase				)
		assert result == expected
