_A								=   '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_A								=   [{'type': 'code', 'content': INSTALL_CONTENT}]
_A								=   {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}





'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_A								=   {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_A								=   logging.get_logger(__name__)


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    '''maskformer'''
   A							:       List[str]     =    {'''hidden_size''': '''mask_feature_size'''}
   A							:       Dict     =    ['''resnet''', '''swin''']
   A							:       Optional[Any]     =    ['''detr''']
   def __init__(self       ,				_a = 256       ,				_a = 256       ,				_a = 0.1       ,				_a = False       ,				_a = None       ,				_a = None       ,				_a = 0.02       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 20.0       ,				_a = None       ,				**_a       ,				)    ->     int:
        if backbone_config is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             lowercase_					:       List[Any]     				=					SwinConfig(
                 image_size=384       ,				in_channels=3       ,				patch_size=4       ,				embed_dim=128       ,				depths=[2, 2, 18, 2]       ,				num_heads=[4, 8, 16, 32]       ,				window_size=12       ,				drop_path_rate=0.3       ,				out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,				)

        if isinstance(_a       ,				_a				):
             lowercase_					:       Dict     				=					backbone_config.pop('model_type'				)
             lowercase_					:       Tuple     				=					CONFIG_MAPPING[backbone_model_type]
             lowercase_					:       Tuple     				=					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 MaskFormer. '''
                 f'''Supported model types: {",".join(self.backbones_supported				)}'''				)

        if decoder_config is None:
             # fall back to https://huggingface.co/facebook/detr-resnet-50
             lowercase_					:       str     				=					DetrConfig()
        else:
             # verify that the decoder is supported
             lowercase_					:       List[Any]     				=					(
                 decoder_config.pop('model_type'				) if isinstance(_a       ,				_a				) else decoder_config.model_type
             )
             if decoder_type not in self.decoders_supported:
                  raise ValueError(
                      f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                      f''' {",".join(self.decoders_supported				)}'''				)
             if isinstance(_a       ,				_a				):
                  lowercase_					:       List[Any]     				=					CONFIG_MAPPING[decoder_type]
                  lowercase_					:       Tuple     				=					config_class.from_dict(_a				)

        lowercase_					:       Optional[Any]     				=					backbone_config
        lowercase_					:       int     				=					decoder_config
        # main feature dimension for the model
        lowercase_					:       List[Any]     				=					fpn_feature_size
        lowercase_					:       Tuple     				=					mask_feature_size
        # initializer
        lowercase_					:       Optional[int]     				=					init_std
        lowercase_					:       List[str]     				=					init_xavier_std
        # Hungarian matcher && loss
        lowercase_					:       int     				=					cross_entropy_weight
        lowercase_					:       Optional[int]     				=					dice_weight
        lowercase_					:       Dict     				=					mask_weight
        lowercase_					:       Tuple     				=					use_auxiliary_loss
        lowercase_					:       int     				=					no_object_weight
        lowercase_					:       Union[str, Any]     				=					output_auxiliary_logits

        lowercase_					:       List[Any]     				=					self.decoder_config.encoder_attention_heads
        lowercase_					:       Tuple     				=					self.decoder_config.num_hidden_layers
        super().__init__(**_a				)
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				_a       ,				**_a				)    ->     Union[str, Any]:
        return cls(
            backbone_config=_a       ,				decoder_config=_a       ,				**_a       ,				)





   def 		_lowerCamelCase (self				)    ->     Dict[str, any]:
        lowercase_					:       Optional[int]     				=					copy.deepcopy(self.__dict__				)
        lowercase_					:       Union[str, Any]     				=					self.backbone_config.to_dict()
        lowercase_					:       Optional[Any]     				=					self.decoder_config.to_dict()
        lowercase_					:       Dict     				=					self.__class__.model_type
        return output




