




'''simple docstring'''



import copy
from typing import Dict, Optional

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


UpperCAmelCase_				:     int     			=						logging.get_logger(__name__)

UpperCAmelCase_				:     Optional[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 			lowerCAmelCase      (       _snake_case):
       __lowercase     :					Any       				=	"oneformer"
       __lowercase     :					int       				=	{"hidden_size": "hidden_dim"}


       def __init__(      self			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = 255			,     __SCREAMING_SNAKE_CASE = 150			,     __SCREAMING_SNAKE_CASE = 0.1			,     __SCREAMING_SNAKE_CASE = 2.0			,     __SCREAMING_SNAKE_CASE = 5.0			,     __SCREAMING_SNAKE_CASE = 5.0			,     __SCREAMING_SNAKE_CASE = 0.5			,     __SCREAMING_SNAKE_CASE = 0.07			,     __SCREAMING_SNAKE_CASE = 1_2544			,     __SCREAMING_SNAKE_CASE = 3.0			,     __SCREAMING_SNAKE_CASE = 0.75			,     __SCREAMING_SNAKE_CASE = 0.02			,     __SCREAMING_SNAKE_CASE = 1.0			,     __SCREAMING_SNAKE_CASE = 1E-05			,     __SCREAMING_SNAKE_CASE = False			,     __SCREAMING_SNAKE_CASE = True			,     __SCREAMING_SNAKE_CASE = True			,     __SCREAMING_SNAKE_CASE = [4, 8, 16, 32]			,     __SCREAMING_SNAKE_CASE = 77			,     __SCREAMING_SNAKE_CASE = 256			,     __SCREAMING_SNAKE_CASE = 77			,     __SCREAMING_SNAKE_CASE = 6			,     __SCREAMING_SNAKE_CASE = 4_9408			,     __SCREAMING_SNAKE_CASE = 2			,     __SCREAMING_SNAKE_CASE = 16			,     __SCREAMING_SNAKE_CASE = 256			,     __SCREAMING_SNAKE_CASE = 256			,     __SCREAMING_SNAKE_CASE = 256			,     __SCREAMING_SNAKE_CASE = 1024			,     __SCREAMING_SNAKE_CASE = "GN"			,     __SCREAMING_SNAKE_CASE = 6			,     __SCREAMING_SNAKE_CASE = 10			,     __SCREAMING_SNAKE_CASE = True			,     __SCREAMING_SNAKE_CASE = 8			,     __SCREAMING_SNAKE_CASE = 0.1			,     __SCREAMING_SNAKE_CASE = 2048			,     __SCREAMING_SNAKE_CASE = False			,     __SCREAMING_SNAKE_CASE = False			,     __SCREAMING_SNAKE_CASE = 2			,     __SCREAMING_SNAKE_CASE = 4			,     **__SCREAMING_SNAKE_CASE			,     )					->     str:



            '''simple docstring'''




            if backbone_config is None:
                 logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''     )
                 __snake_case			    =     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=_lowercase			,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			,     )
            elif isinstance(_lowercase			,     _lowercase     ):
                 __snake_case			    =     backbone_config.get('''model_type'''     )
                 __snake_case			    =     CONFIG_MAPPING[backbone_model_type]
                 __snake_case			    =     config_class.from_dict(_lowercase     )

            __snake_case			    =     backbone_config

            __snake_case			    =     ignore_value
            __snake_case			    =     num_queries
            __snake_case			    =     no_object_weight
            __snake_case			    =     class_weight
            __snake_case			    =     mask_weight
            __snake_case			    =     dice_weight
            __snake_case			    =     contrastive_weight
            __snake_case			    =     contrastive_temperature
            __snake_case			    =     train_num_points
            __snake_case			    =     oversample_ratio
            __snake_case			    =     importance_sample_ratio
            __snake_case			    =     init_std
            __snake_case			    =     init_xavier_std
            __snake_case			    =     layer_norm_eps
            __snake_case			    =     is_training
            __snake_case			    =     use_auxiliary_loss
            __snake_case			    =     output_auxiliary_logits
            __snake_case			    =     strides
            __snake_case			    =     task_seq_len
            __snake_case			    =     text_encoder_width
            __snake_case			    =     text_encoder_context_length
            __snake_case			    =     text_encoder_num_layers
            __snake_case			    =     text_encoder_vocab_size
            __snake_case			    =     text_encoder_proj_layers
            __snake_case			    =     text_encoder_n_ctx
            __snake_case			    =     conv_dim
            __snake_case			    =     mask_dim
            __snake_case			    =     hidden_dim
            __snake_case			    =     encoder_feedforward_dim
            __snake_case			    =     norm
            __snake_case			    =     encoder_layers
            __snake_case			    =     decoder_layers
            __snake_case			    =     use_task_norm
            __snake_case			    =     num_attention_heads
            __snake_case			    =     dropout
            __snake_case			    =     dim_feedforward
            __snake_case			    =     pre_norm
            __snake_case			    =     enforce_input_proj
            __snake_case			    =     query_dec_layers
            __snake_case			    =     common_stride
            __snake_case			    =     decoder_layers

            super().__init__(**_lowercase     )




       def 					lowerCAmelCase					(      self     )					->     Dict[str, any]:



            '''simple docstring'''




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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EsmForMaskedLM",
							    "EsmForSequenceClassification",
							    "EsmForTokenClassification",
							    "EsmModel",
							    "EsmPreTrainedModel",
							]
							UpperCamelCase_	       =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEsmForMaskedLM",
							    "TFEsmForSequenceClassification",
							    "TFEsmForTokenClassification",
							    "TFEsmModel",
							    "TFEsmPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
							from .tokenization_esm import EsmTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_esm import (
														    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EsmForMaskedLM,
														    EsmForSequenceClassification,
														    EsmForTokenClassification,
														    EsmModel,
														    EsmPreTrainedModel,
														)
														from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_esm import (
														    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEsmForMaskedLM,
														    TFEsmForSequenceClassification,
														    TFEsmForTokenClassification,
														    TFEsmModel,
														    TFEsmPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)





