'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A								=   logging.get_logger(__name__)

_A								=   {
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Union[str, Any]     =    '''nllb-moe'''
   A							:       Dict     =    ['''past_key_values''']
   A							:       Tuple     =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(self       ,				_a=128_112       ,				_a=1_024       ,				_a=12       ,				_a=4_096       ,				_a=16       ,				_a=12       ,				_a=4_096       ,				_a=16       ,				_a=0.05       ,				_a=0.05       ,				_a=True       ,				_a=True       ,				_a="relu"       ,				_a=1_024       ,				_a=0.1       ,				_a=0.1       ,				_a=0.0       ,				_a=0.02       ,				_a=2       ,				_a=True       ,				_a=False       ,				_a="float32"       ,				_a=False       ,				_a=128       ,				_a=64       ,				_a=4       ,				_a=4       ,				_a=0.0_01       ,				_a=0.0_01       ,				_a="all"       ,				_a=False       ,				_a=False       ,				_a=1.0       ,				_a=0.2       ,				_a=1       ,				_a=0       ,				_a=2       ,				_a=False       ,				**_a       ,				)    ->     str:
        lowercase_					:       Any     				=					vocab_size
        lowercase_					:       List[Any]     				=					max_position_embeddings
        lowercase_					:       str     				=					d_model
        lowercase_					:       int     				=					encoder_ffn_dim
        lowercase_					:       List[str]     				=					encoder_layers
        lowercase_					:       List[Any]     				=					encoder_attention_heads
        lowercase_					:       List[Any]     				=					decoder_ffn_dim
        lowercase_					:       List[Any]     				=					decoder_layers
        lowercase_					:       int     				=					decoder_attention_heads
        lowercase_					:       List[Any]     				=					dropout
        lowercase_					:       str     				=					attention_dropout
        lowercase_					:       str     				=					activation_dropout
        lowercase_					:       Any     				=					activation_function
        lowercase_					:       Dict     				=					init_std
        lowercase_					:       str     				=					encoder_layerdrop
        lowercase_					:       Optional[Any]     				=					decoder_layerdrop
        lowercase_					:       Any     				=					use_cache
        lowercase_					:       str     				=					encoder_layers
        lowercase_					:       int     				=					scale_embedding  # scale factor will be sqrt(d_model) if True
        lowercase_					:       Optional[Any]     				=					router_z_loss_coef
        lowercase_					:       int     				=					router_aux_loss_coef
        lowercase_					:       Union[str, Any]     				=					decoder_sparse_step
        lowercase_					:       Optional[int]     				=					encoder_sparse_step
        lowercase_					:       int     				=					num_experts
        lowercase_					:       Tuple     				=					expert_capacity
        lowercase_					:       Any     				=					router_bias
        if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''				)
        lowercase_					:       Dict     				=					router_dtype

        lowercase_					:       str     				=					router_ignore_padding_tokens
        lowercase_					:       Dict     				=					batch_prioritized_routing
        lowercase_					:       str     				=					second_expert_policy
        lowercase_					:       Optional[int]     				=					normalize_router_prob_before_dropping
        lowercase_					:       int     				=					moe_eval_capacity_token_fraction
        lowercase_					:       List[Any]     				=					moe_token_dropout
        lowercase_					:       Optional[int]     				=					output_router_logits
        super().__init__(
            pad_token_id=_a       ,				bos_token_id=_a       ,				eos_token_id=_a       ,				is_encoder_decoder=_a       ,				decoder_start_token_id=_a       ,				**_a       ,				)





'''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




