






"""simple docstring"""

import copy
from typing import Dict, List, Optional

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


lowercase    			=       {
    '''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
}

lowercase    			=       logging.get_logger(__name__)

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''mask2former'''
				UpperCAmelCase__        =				['''swin''']
				UpperCAmelCase__        =				{'''hidden_size''': '''hidden_dim'''}







				def __init__(							self							:						Optional[int]						,    __lowerCamelCase							:						Optional[Dict] = None						,    __lowerCamelCase							:						int = 2_5_6						,    __lowerCamelCase							:						int = 2_5_6						,    __lowerCamelCase							:						int = 2_5_6						,    __lowerCamelCase							:						int = 1_0_2_4						,    __lowerCamelCase							:						str = "relu"						,    __lowerCamelCase							:						int = 6						,    __lowerCamelCase							:						int = 1_0						,    __lowerCamelCase							:						int = 8						,    __lowerCamelCase							:						float = 0.0						,    __lowerCamelCase							:						int = 2_0_4_8						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						int = 4						,    __lowerCamelCase							:						int = 2_5_5						,    __lowerCamelCase							:						int = 1_0_0						,    __lowerCamelCase							:						float = 0.1						,    __lowerCamelCase							:						float = 2.0						,    __lowerCamelCase							:						float = 5.0						,    __lowerCamelCase							:						float = 5.0						,    __lowerCamelCase							:						int = 1_2_5_4_4						,    __lowerCamelCase							:						float = 3.0						,    __lowerCamelCase							:						float = 0.75						,    __lowerCamelCase							:						float = 0.02						,    __lowerCamelCase							:						float = 1.0						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						List[int] = [4, 8, 1_6, 3_2]						,    __lowerCamelCase							:						bool = None						,    **__lowerCamelCase							:						Tuple						,    )				->					str:
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
																__magic_name__            =     CONFIG_MAPPING["swin"](
																    image_size=2_2_4						,    in_channels=3						,    patch_size=4						,    embed_dim=9_6						,    depths=[2, 2, 1_8, 2]						,    num_heads=[3, 6, 1_2, 2_4]						,    window_size=7						,    drop_path_rate=0.3						,    use_absolute_embeddings=__lowerCamelCase						,    out_features=["stage1", "stage2", "stage3", "stage4"]						,    )

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

										# 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	)}'''	)

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

										super().__init__(**__lowerCamelCase	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[int]						,    __lowerCamelCase							:						PretrainedConfig						,    **__lowerCamelCase							:						Optional[int]	)				->					Dict:
										return cls(
										    backbone_config=__lowerCamelCase						,    **__lowerCamelCase						,    )







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











"""simple docstring"""

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										if images is None and text is None:
																raise ValueError("You have to specify either images or text."	)

										# Get only text
										if images is None:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

										if text is not None:
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
										else:
																__magic_name__            =     None

										if text_encoding is not None:
																encoding_image_processor.update(__lowerCamelCase	)

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



