

import functools
import operator

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


SCREAMING_SNAKE_CASE		:Dict						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:int						=     {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:str										=      """sew-d"""






     def __init__(				self		,						_lowercase=32		,						_lowercase=768		,						_lowercase=12		,						_lowercase=12		,						_lowercase=3_072		,						_lowercase=2		,						_lowercase=512		,						_lowercase=256		,						_lowercase=True		,						_lowercase=True		,						_lowercase=("p2c", "c2p")		,						_lowercase="layer_norm"		,						_lowercase="gelu_python"		,						_lowercase=0.1		,						_lowercase=0.1		,						_lowercase=0.1		,						_lowercase=0.0		,						_lowercase=0.1		,						_lowercase=0.02		,						_lowercase=1e-7		,						_lowercase=1e-5		,						_lowercase="group"		,						_lowercase="gelu"		,						_lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,						_lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,						_lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,						_lowercase=False		,						_lowercase=128		,						_lowercase=16		,						_lowercase=True		,						_lowercase=0.05		,						_lowercase=10		,						_lowercase=2		,						_lowercase=0.0		,						_lowercase=10		,						_lowercase=0		,						_lowercase="mean"		,						_lowercase=False		,						_lowercase=False		,						_lowercase=256		,						_lowercase=0		,						_lowercase=1		,						_lowercase=2		,						**_lowercase		,						)->     Optional[int]:
         super().__init__(**_lowercase		,						pad_token_id=_lowercase		,						bos_token_id=_lowercase		,						eos_token_id=_lowercase	)
         UpperCamelCase_			  =						hidden_size
         UpperCamelCase_			  =						feat_extract_norm
         UpperCamelCase_			  =						feat_extract_activation
         UpperCamelCase_			  =						list(_lowercase	)
         UpperCamelCase_			  =						list(_lowercase	)
         UpperCamelCase_			  =						list(_lowercase	)
         UpperCamelCase_			  =						conv_bias
         UpperCamelCase_			  =						num_conv_pos_embeddings
         UpperCamelCase_			  =						num_conv_pos_embedding_groups
         UpperCamelCase_			  =						len(self.conv_dim	)
         UpperCamelCase_			  =						num_hidden_layers
         UpperCamelCase_			  =						intermediate_size
         UpperCamelCase_			  =						squeeze_factor
         UpperCamelCase_			  =						max_position_embeddings
         UpperCamelCase_			  =						position_buckets
         UpperCamelCase_			  =						share_att_key
         UpperCamelCase_			  =						relative_attention
         UpperCamelCase_			  =						norm_rel_ebd
         UpperCamelCase_			  =						list(_lowercase	)
         UpperCamelCase_			  =						hidden_act
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						hidden_dropout
         UpperCamelCase_			  =						attention_dropout
         UpperCamelCase_			  =						activation_dropout
         UpperCamelCase_			  =						feat_proj_dropout
         UpperCamelCase_			  =						final_dropout
         UpperCamelCase_			  =						layer_norm_eps
         UpperCamelCase_			  =						feature_layer_norm_eps
         UpperCamelCase_			  =						initializer_range
         UpperCamelCase_			  =						vocab_size

         if (
             (len(self.conv_stride	) != self.num_feat_extract_layers)
             or (len(self.conv_kernel	) != self.num_feat_extract_layers)
             or (len(self.conv_dim	) != self.num_feat_extract_layers)
         ):
             raise ValueError(
                 "Configuration for convolutional layers is incorrect."
                 "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                 F"but is `len(config.conv_dim) = {len(self.conv_dim	)}`, `len(config.conv_stride)"
                 F"= {len(self.conv_stride	)}`, `len(config.conv_kernel) = {len(self.conv_kernel	)}`."	)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         UpperCamelCase_			  =						apply_spec_augment
         UpperCamelCase_			  =						mask_time_prob
         UpperCamelCase_			  =						mask_time_length
         UpperCamelCase_			  =						mask_time_min_masks
         UpperCamelCase_			  =						mask_feature_prob
         UpperCamelCase_			  =						mask_feature_length
         UpperCamelCase_			  =						mask_feature_min_masks

         # ctc loss
         UpperCamelCase_			  =						ctc_loss_reduction
         UpperCamelCase_			  =						ctc_zero_infinity

         # sequence classification
         UpperCamelCase_			  =						use_weighted_layer_sum
         UpperCamelCase_			  =						classifier_proj_size






     @property
     def 	UpperCAmelCase_				(				self	)->     Tuple:
         return functools.reduce(operator.mul		,						self.conv_stride		,						1	)



import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """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
}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     logging.get_logger(__name__)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[int]										=      """mask2former"""
     UpperCamelCase_	:Dict										=      ["""swin"""]
     UpperCamelCase_	:List[Any]										=      {"""hidden_size""": """hidden_dim"""}






     def __init__(				self		,						_lowercase = None		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 1_024		,						_lowercase = "relu"		,						_lowercase = 6		,						_lowercase = 10		,						_lowercase = 8		,						_lowercase = 0.0		,						_lowercase = 2_048		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 4		,						_lowercase = 255		,						_lowercase = 100		,						_lowercase = 0.1		,						_lowercase = 2.0		,						_lowercase = 5.0		,						_lowercase = 5.0		,						_lowercase = 12_544		,						_lowercase = 3.0		,						_lowercase = 0.75		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = True		,						_lowercase = [4, 8, 16, 32]		,						_lowercase = None		,						**_lowercase		,						)->     Union[str, Any]:
         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=_lowercase		,						out_features=["stage1", "stage2", "stage3", "stage4"]		,						)

         if isinstance(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						config_class.from_dict(_lowercase	)

         # 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__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						**_lowercase	)->     Optional[int]:
         return cls(
             backbone_config=_lowercase		,						**_lowercase		,						)






     def 	UpperCAmelCase_				(				self	)->     Dict[str, any]:
         UpperCamelCase_			  =						copy.deepcopy(self.__dict__	)
         UpperCamelCase_			  =						self.backbone_config.to_dict()
         UpperCamelCase_			  =						self.__class__.model_type
         return output
