


import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class       _a							:




	def __init__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	List[str]=sys.maxsize	)-> Tuple:
			lowerCAmelCase__     :    List[str]					  =      '''bilinear'''
			lowerCAmelCase__     :    str					  =      max_size
			lowerCAmelCase__     :    List[str]					  =      short_edge_length




	def __call__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	str	)-> Tuple:
			lowerCAmelCase__     :    int					  =      []
			for img in imgs:
					lowerCAmelCase__  ,			lowerCAmelCase__     :    int					  =      img.shape[:2]
					# later: provide list and randomly choose index for resize
					lowerCAmelCase__     :    Tuple					  =      np.random.randint(self.short_edge_length[0]	,      self.short_edge_length[1] + 1	)
					if size == 0:
							return img
					lowerCAmelCase__     :    Optional[int]					  =      size * 1.0 / min(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
					if h < w:
							lowerCAmelCase__  ,			lowerCAmelCase__     :    List[Any]					  =      size, scale * w
					else:
							lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      scale * h, size
					if max(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	) > self.max_size:
							lowerCAmelCase__     :    Optional[int]					  =      self.max_size * 1.0 / max(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    List[str]					  =      newh * scale
							lowerCAmelCase__     :    Optional[int]					  =      neww * scale
					lowerCAmelCase__     :    Optional[Any]					  =      int(neww + 0.5	)
					lowerCAmelCase__     :    List[Any]					  =      int(newh + 0.5	)

					if img.dtype == np.uinta:
							lowerCAmelCase__     :    Optional[int]					  =      Image.fromarray(_SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    Tuple					  =      pil_image.resize((neww, newh)	,      PILImageResampling.BILINEAR	)
							lowerCAmelCase__     :    int					  =      np.asarray(_SCREAMING_SNAKE_CASE	)
					else:
							lowerCAmelCase__     :    Optional[Any]					  =      img.permute(2	,      0	,      1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
							lowerCAmelCase__     :    Tuple					  =      nn.functional.interpolate(
							    _SCREAMING_SNAKE_CASE	,      (newh, neww)	,      mode=self.interp_method	,      align_corners=_SCREAMING_SNAKE_CASE	).squeeze(0	)
					img_augs.append(_SCREAMING_SNAKE_CASE	)

			return img_augs





class       _a							:




	def __init__(      self		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	Any	)-> Optional[int]:
			lowerCAmelCase__     :    Tuple					  =      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,      cfg.INPUT.MAX_SIZE_TEST	)
			lowerCAmelCase__     :    Optional[int]					  =      cfg.INPUT.FORMAT
			lowerCAmelCase__     :    Any					  =      cfg.SIZE_DIVISIBILITY
			lowerCAmelCase__     :    Union[str, Any]					  =      cfg.PAD_VALUE
			lowerCAmelCase__     :    List[Any]					  =      cfg.INPUT.MAX_SIZE_TEST
			lowerCAmelCase__     :    str					  =      cfg.MODEL.DEVICE
			lowerCAmelCase__     :    Optional[Any]					  =      torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)	,      1	,      1	)
			lowerCAmelCase__     :    List[Any]					  =      torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)	,      1	,      1	)
			lowerCAmelCase__     :    int					  =      lambda _SCREAMING_SNAKE_CASE	: (x - self.pixel_mean) / self.pixel_std




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	int	)-> Optional[Any]:
			lowerCAmelCase__     :    Dict					  =      tuple(max(_SCREAMING_SNAKE_CASE	) for s in zip(*[img.shape for img in images]	)	)
			lowerCAmelCase__     :    List[str]					  =      [im.shape[-2:] for im in images]
			lowerCAmelCase__     :    Optional[Any]					  =      [
			    nn.functional.pad(
			        _SCREAMING_SNAKE_CASE	,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]	,      value=self.pad_value	,      )
			    for size, im in zip(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			]

			return torch.stack(_SCREAMING_SNAKE_CASE	), torch.tensor(_SCREAMING_SNAKE_CASE	)




	def __call__(      self		:	Dict	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=False	)-> List[str]:
			with torch.no_grad():
					if not isinstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	):
							lowerCAmelCase__     :    str					  =      [images]
					if single_image:
							assert len(_SCREAMING_SNAKE_CASE	) == 1
					for i in range(len(_SCREAMING_SNAKE_CASE	)	):
							if isinstance(images[i]	,      torch.Tensor	):
									images.insert(_SCREAMING_SNAKE_CASE	,      images.pop(_SCREAMING_SNAKE_CASE	).to(self.device	).float()	)
							elif not isinstance(images[i]	,      torch.Tensor	):
									images.insert(
									    _SCREAMING_SNAKE_CASE	,      torch.as_tensor(img_tensorize(images.pop(_SCREAMING_SNAKE_CASE	)	,      input_format=self.input_format	)	)
									    .to(self.device	)
									    .float()	,      )
            # resize smallest edge
					lowerCAmelCase__     :    Union[str, Any]					  =      torch.tensor([im.shape[:2] for im in images]	)
					lowerCAmelCase__     :    Optional[Any]					  =      self.aug(_SCREAMING_SNAKE_CASE	)
					# transpose images and convert to torch tensors
					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
					# now normalize before pad to avoid useless arithmetic
					lowerCAmelCase__     :    Any					  =      [self.normalizer(_SCREAMING_SNAKE_CASE	) for x in images]
					# now pad them to do the following operations
					lowerCAmelCase__  ,			lowerCAmelCase__     :    List[str]					  =      self.pad(_SCREAMING_SNAKE_CASE	)
					# Normalize

					if self.size_divisibility > 0:
							raise NotImplementedError()
					# pad
					lowerCAmelCase__     :    Union[str, Any]					  =      torch.true_divide(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
					if single_image:
							return images[0], sizes[0], scales_yx[0]
					else:
							return images, sizes, scales_yx






def   lowerCamelCase_					( _a			,     _a ):




		"""simple docstring"""






		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def   lowerCamelCase_					( _a			,     _a ):




		"""simple docstring"""






		assert torch.isfinite(_a ).all(), "Box tensor contains infinite or NaN!"
		lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      box_size
		tensor[:, 0].clamp_(min=0			,     max=_a )
		tensor[:, 1].clamp_(min=0			,     max=_a )
		tensor[:, 2].clamp_(min=0			,     max=_a )
		tensor[:, 3].clamp_(min=0			,     max=_a )








import copy

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


lowerCamelCase		=	logging.get_logger(__name__)

lowerCamelCase		=	{
    '''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       _a							(       _lowercase):




	def __init__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Tuple=256	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=1024	,      _SCREAMING_SNAKE_CASE		:	List[Any]=16	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=16	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=4	,      _SCREAMING_SNAKE_CASE		:	Any="gelu"	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=1E-6	,      **_SCREAMING_SNAKE_CASE		:	List[Any]	,      )-> Tuple:
			super().__init__(**_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      hidden_size
			lowerCAmelCase__     :    Tuple					  =      image_size
			lowerCAmelCase__     :    List[Any]					  =      patch_size
			lowerCAmelCase__     :    Any					  =      image_size // patch_size
			lowerCAmelCase__     :    Dict					  =      mask_input_channels
			lowerCAmelCase__     :    Dict					  =      num_point_embeddings
			lowerCAmelCase__     :    str					  =      hidden_act
			lowerCAmelCase__     :    List[Any]					  =      layer_norm_eps





class       _a							(       _lowercase):




	def __init__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=256	,      _SCREAMING_SNAKE_CASE		:	Dict="relu"	,      _SCREAMING_SNAKE_CASE		:	str=2048	,      _SCREAMING_SNAKE_CASE		:	List[Any]=2	,      _SCREAMING_SNAKE_CASE		:	Dict=8	,      _SCREAMING_SNAKE_CASE		:	str=2	,      _SCREAMING_SNAKE_CASE		:	Tuple=3	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=3	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=256	,      _SCREAMING_SNAKE_CASE		:	Dict=1E-6	,      **_SCREAMING_SNAKE_CASE		:	Any	,      )-> List[Any]:
			super().__init__(**_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[str]					  =      hidden_size
			lowerCAmelCase__     :    Union[str, Any]					  =      hidden_act
			lowerCAmelCase__     :    str					  =      mlp_dim
			lowerCAmelCase__     :    Optional[int]					  =      num_hidden_layers
			lowerCAmelCase__     :    int					  =      num_attention_heads
			lowerCAmelCase__     :    List[str]					  =      attention_downsample_rate
			lowerCAmelCase__     :    str					  =      num_multimask_outputs
			lowerCAmelCase__     :    Optional[int]					  =      iou_head_depth
			lowerCAmelCase__     :    Union[str, Any]					  =      iou_head_hidden_dim
			lowerCAmelCase__     :    Tuple					  =      layer_norm_eps





class       _a							(       _lowercase):




	def __init__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	int=768	,      _SCREAMING_SNAKE_CASE		:	str=256	,      _SCREAMING_SNAKE_CASE		:	int=12	,      _SCREAMING_SNAKE_CASE		:	Tuple=12	,      _SCREAMING_SNAKE_CASE		:	Dict=3	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=1024	,      _SCREAMING_SNAKE_CASE		:	Tuple=16	,      _SCREAMING_SNAKE_CASE		:	List[Any]="gelu"	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=1E-06	,      _SCREAMING_SNAKE_CASE		:	int=0.0	,      _SCREAMING_SNAKE_CASE		:	int=1E-10	,      _SCREAMING_SNAKE_CASE		:	List[Any]=True	,      _SCREAMING_SNAKE_CASE		:	Any=4.0	,      _SCREAMING_SNAKE_CASE		:	List[str]=True	,      _SCREAMING_SNAKE_CASE		:	int=True	,      _SCREAMING_SNAKE_CASE		:	List[str]=14	,      _SCREAMING_SNAKE_CASE		:	Dict=[2, 5, 8, 11]	,      _SCREAMING_SNAKE_CASE		:	List[Any]=128	,      _SCREAMING_SNAKE_CASE		:	Dict=None	,      **_SCREAMING_SNAKE_CASE		:	str	,      )-> Any:
			super().__init__(**_SCREAMING_SNAKE_CASE	)

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





class       _a							(       _lowercase):
	_a						:						Tuple   					=							'''sam'''
	_a						:						Optional[Any]   					=							True




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

			if isinstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	):
					lowerCAmelCase__     :    Any					  =      vision_config.to_dict()
			if isinstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	):
					lowerCAmelCase__     :    Any					  =      prompt_encoder_config.to_dict()
			if isinstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	):
					lowerCAmelCase__     :    List[Any]					  =      mask_decoder_config.to_dict()

			lowerCAmelCase__     :    Any					  =      SamVisionConfig(**_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[Any]					  =      SamPromptEncoderConfig(**_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Dict					  =      SamMaskDecoderConfig(**_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[Any]					  =      initializer_range




	def 							UpperCAmelCase__(      self		:	Any	)-> int:
			lowerCAmelCase__     :    Optional[int]					  =      copy.deepcopy(self.__dict__	)
			lowerCAmelCase__     :    Any					  =      self.vision_config.to_dict()
			lowerCAmelCase__     :    Optional[Any]					  =      self.prompt_encoder_config.to_dict()
			lowerCAmelCase__     :    str					  =      self.mask_decoder_config.to_dict()
			lowerCAmelCase__     :    Any					  =      self.__class__.model_type
			return output




