




'''simple docstring'''





import copy

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


lowercase__								=						logging.get_logger(__name__)

lowercase__								=						{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}






class     lowercase  ( UpperCAmelCase_):






      def __init__(self			,	a__=2_5_6			,	a__=1_0_2_4			,	a__=1_6			,	a__=1_6			,	a__=4			,	a__="gelu"			,	a__=1E-6			,	**a__			,	)							-> List[str]:
         super().__init__(**a__       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			hidden_size
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_size
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			patch_size
         SCREAMING_SNAKE_CASE__      :					str							    =			image_size // patch_size
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			mask_input_channels
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			num_point_embeddings
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			hidden_act
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			layer_norm_eps






class     lowercase  ( UpperCAmelCase_):






      def __init__(self			,	a__=2_5_6			,	a__="relu"			,	a__=2_0_4_8			,	a__=2			,	a__=8			,	a__=2			,	a__=3			,	a__=3			,	a__=2_5_6			,	a__=1E-6			,	**a__			,	)							-> Union[str, Any]:
         super().__init__(**a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			hidden_size
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			hidden_act
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			mlp_dim
         SCREAMING_SNAKE_CASE__      :					Dict							    =			num_hidden_layers
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			num_attention_heads
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			attention_downsample_rate
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			num_multimask_outputs
         SCREAMING_SNAKE_CASE__      :					str							    =			iou_head_depth
         SCREAMING_SNAKE_CASE__      :					int							    =			iou_head_hidden_dim
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			layer_norm_eps






class     lowercase  ( UpperCAmelCase_):






      def __init__(self			,	a__=7_6_8			,	a__=2_5_6			,	a__=1_2			,	a__=1_2			,	a__=3			,	a__=1_0_2_4			,	a__=1_6			,	a__="gelu"			,	a__=1E-06			,	a__=0.0			,	a__=1E-10			,	a__=True			,	a__=4.0			,	a__=True			,	a__=True			,	a__=1_4			,	a__=[2, 5, 8, 1_1]			,	a__=1_2_8			,	a__=None			,	**a__			,	)							-> List[Any]:
         super().__init__(**a__       )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			hidden_size
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			output_channels
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			num_hidden_layers
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			num_attention_heads
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			num_channels
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_size
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			patch_size
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			hidden_act
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			layer_norm_eps
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			attention_dropout
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			initializer_range
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			qkv_bias
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			mlp_ratio
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			use_abs_pos
         SCREAMING_SNAKE_CASE__      :					int							    =			use_rel_pos
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			window_size
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			global_attn_indexes
         SCREAMING_SNAKE_CASE__      :					Any							    =			num_pos_feats
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			int(hidden_size * mlp_ratio       ) if mlp_dim is None else mlp_dim






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''sam'''
      __lowercase					=	True






      def __init__(self			,	a__=None			,	a__=None			,	a__=None			,	a__=0.02			,	**a__			,	)							-> List[str]:
         super().__init__(**a__       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			vision_config if vision_config is not None else {}
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			prompt_encoder_config if prompt_encoder_config is not None else {}
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			mask_decoder_config if mask_decoder_config is not None else {}

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			vision_config.to_dict()
         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Dict							    =			prompt_encoder_config.to_dict()
         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			mask_decoder_config.to_dict()

         SCREAMING_SNAKE_CASE__      :					Dict							    =			SamVisionConfig(**a__       )
         SCREAMING_SNAKE_CASE__      :					int							    =			SamPromptEncoderConfig(**a__       )
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			SamMaskDecoderConfig(**a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			initializer_range






      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			copy.deepcopy(self.__dict__       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.vision_config.to_dict()
         SCREAMING_SNAKE_CASE__      :					str							    =			self.prompt_encoder_config.to_dict()
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.mask_decoder_config.to_dict()
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.__class__.model_type
         return output







'''simple docstring'''





import warnings
from typing import List, Optional, Union

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






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''       )

         if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''       )

         # first, apply the image processor
         SCREAMING_SNAKE_CASE__      :					str							    =			self.image_processor(images=a__			,	return_tensors=a__       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(a__			,	a__       ):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(
             text=text if text is not None else features['''words''']			,	text_pair=text_pair if text_pair is not None else None			,	boxes=boxes if boxes is not None else features['''boxes''']			,	word_labels=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_token_type_ids=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)

         # add pixel values
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

         if len(a__       ) != len(a__       ):
            raise ValueError(
                '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                f""" {len(a__       )} and {len(a__       )}"""       )

         return images_with_overflow






      def  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,	a__			,	)
         return self.image_processor_class






      @property
      def  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

