'''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    UpperCAmelCase__	:






   """simple docstring"""
   def __init__(self							,					_a							,					_a=sys.maxsize					)     ->  List[str]:
        lowercase_       :							Optional[Any]  					=       'bilinear'
        lowercase_       :							Dict  					=       max_size
        lowercase_       :							int  					=       short_edge_length





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

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

        return img_augs



class    UpperCAmelCase__	:






   """simple docstring"""
   def __init__(self							,					_a					)     ->  str:
        lowercase_       :							Any  					=       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							,					cfg.INPUT.MAX_SIZE_TEST					)
        lowercase_       :							Union[str, Any]  					=       cfg.INPUT.FORMAT
        lowercase_       :							int  					=       cfg.SIZE_DIVISIBILITY
        lowercase_       :							Union[str, Any]  					=       cfg.PAD_VALUE
        lowercase_       :							Optional[Any]  					=       cfg.INPUT.MAX_SIZE_TEST
        lowercase_       :							Optional[int]  					=       cfg.MODEL.DEVICE
        lowercase_       :							Tuple  					=       torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)							,					1							,					1					)
        lowercase_       :							str  					=       torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)							,					1							,					1					)
        lowercase_       :							Union[str, Any]  					=       lambda _a					: (x - self.pixel_mean) / self.pixel_std
   def 		_lowerCamelCase			(self							,					_a					)     ->  Optional[int]:
        lowercase_       :							Optional[Any]  					=       tuple(max(_a					) for s in zip(*[img.shape for img in images]					)					)
        lowercase_       :							Dict  					=       [im.shape[-2:] for im in images]
        lowercase_       :							List[Any]  					=       [
            nn.functional.pad(
                _a							,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							,					value=self.pad_value							,					)
            for size, im in zip(_a							,					_a					)
        ]

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





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

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



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     assert torch.isfinite(SCREAMING_SNAKE_CASE_				).all(), "Box tensor contains infinite or NaN!"
     lowercase_					,lowercase_       :							Optional[int]  					=       box_size
     tensor[:, 0].clamp_(min=0	,							max=SCREAMING_SNAKE_CASE_				)
     tensor[:, 1].clamp_(min=0	,							max=SCREAMING_SNAKE_CASE_				)
     tensor[:, 2].clamp_(min=0	,							max=SCREAMING_SNAKE_CASE_				)
     tensor[:, 3].clamp_(min=0	,							max=SCREAMING_SNAKE_CASE_				)





'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




