

"""simple docstring"""

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       _snake_case  :







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=sys.maxsize				)      ->    Optional[int]:
											__lowercase        =      '''bilinear'''
											__lowercase        =      max_size
											__lowercase        =      short_edge_length







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

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

											return img_augs



class       _snake_case  :







						def __init__(self  ,				UpperCAmelCase_				)      ->    Any:
											__lowercase        =      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,				cfg.INPUT.MAX_SIZE_TEST				)
											__lowercase        =      cfg.INPUT.FORMAT
											__lowercase        =      cfg.SIZE_DIVISIBILITY
											__lowercase        =      cfg.PAD_VALUE
											__lowercase        =      cfg.INPUT.MAX_SIZE_TEST
											__lowercase        =      cfg.MODEL.DEVICE
											__lowercase        =      torch.tensor(cfg.MODEL.PIXEL_STD				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)  ,				1  ,				1				)
											__lowercase        =      torch.tensor(cfg.MODEL.PIXEL_MEAN				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)  ,				1  ,				1				)
											__lowercase        =      lambda UpperCAmelCase_				: (x - self.pixel_mean) / self.pixel_std







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      tuple(max(UpperCAmelCase_				) for s in zip(*[img.shape for img in images]				)				)
											__lowercase        =      [im.shape[-2:] for im in images]
											__lowercase        =      [
											    nn.functional.pad(
											        UpperCAmelCase_  ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]  ,				value=self.pad_value  ,				)
											    for size, im in zip(UpperCAmelCase_  ,				UpperCAmelCase_				)
											]

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







						def __call__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=False				)      ->    List[str]:
											with torch.no_grad():
																if not isinstance(UpperCAmelCase_  ,				UpperCAmelCase_				):
																					__lowercase        =      [images]
																if single_image:
																					assert len(UpperCAmelCase_				) == 1
																for i in range(len(UpperCAmelCase_				)				):
																					if isinstance(images[i]  ,				torch.Tensor				):
																										images.insert(UpperCAmelCase_  ,				images.pop(UpperCAmelCase_				).to(self.device				).float()				)
																					elif not isinstance(images[i]  ,				torch.Tensor				):
																										images.insert(
																										    UpperCAmelCase_  ,				torch.as_tensor(img_tensorize(images.pop(UpperCAmelCase_				)  ,				input_format=self.input_format				)				)
																										    .to(self.device				)
																										    .float()  ,				)
            # resize smallest edge
																__lowercase        =      torch.tensor([im.shape[:2] for im in images]				)
																__lowercase        =      self.aug(UpperCAmelCase_				)
																# 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
																__lowercase        =      [self.normalizer(UpperCAmelCase_				) for x in images]
																# now pad them to do the following operations
																__lowercase    ,			__lowercase        =      self.pad(UpperCAmelCase_				)
																# Normalize

																if self.size_divisibility > 0:
																					raise NotImplementedError()
																# pad
																__lowercase        =      torch.true_divide(UpperCAmelCase_  ,				UpperCAmelCase_				)
																if single_image:
																					return images[0], sizes[0], scales_yx[0]
																else:
																					return images, sizes, scales_yx





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			Any      )					->      Dict:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			Tuple[int, int]      )					->      Any:




					"""simple docstring"""



					assert torch.isfinite(a__      ).all(), "Box tensor contains infinite or NaN!"
					__lowercase    ,			__lowercase        =      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__      )





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a__			:						Tuple							=			{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[str]							=			["""SegformerFeatureExtractor"""]
							a__			:						str							=			["""SegformerImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[Any]							=			[
							    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """SegformerDecodeHead""",
							    """SegformerForImageClassification""",
							    """SegformerForSemanticSegmentation""",
							    """SegformerLayer""",
							    """SegformerModel""",
							    """SegformerPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[Any]							=			[
							    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSegformerDecodeHead""",
							    """TFSegformerForImageClassification""",
							    """TFSegformerForSemanticSegmentation""",
							    """TFSegformerModel""",
							    """TFSegformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							a__			:						Any							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


