
"""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 		a__						:
   def __init__(			self	,    _a	,    _a=sys.maxsize				):
          lowercase    :				List[str]       		=							'bilinear'
          lowercase    :				List[Any]       		=							max_size
          lowercase    :				Dict       		=							short_edge_length

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

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

          return img_augs


class 		a__						:
   def __init__(			self	,    _a				):
          lowercase    :				Dict       		=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,    cfg.INPUT.MAX_SIZE_TEST				)
          lowercase    :				Any       		=							cfg.INPUT.FORMAT
          lowercase    :				List[Any]       		=							cfg.SIZE_DIVISIBILITY
          lowercase    :				str       		=							cfg.PAD_VALUE
          lowercase    :				List[str]       		=							cfg.INPUT.MAX_SIZE_TEST
          lowercase    :				Union[str, Any]       		=							cfg.MODEL.DEVICE
          lowercase    :				Optional[Any]       		=							torch.tensor(cfg.MODEL.PIXEL_STD				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)	,    1	,    1				)
          lowercase    :				Optional[int]       		=							torch.tensor(cfg.MODEL.PIXEL_MEAN				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)	,    1	,    1				)
          lowercase    :				Optional[Any]       		=							lambda _a				: (x - self.pixel_mean) / self.pixel_std
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							tuple(max(_a				) for s in zip(*[img.shape for img in images]				)				)
          lowercase    :				List[Any]       		=							[im.shape[-2:] for im in images]
          lowercase    :				Dict       		=							[
              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				):






          with torch.no_grad():
                 if not isinstance(_a	,    _a				):
                        lowercase    :				Optional[Any]       		=							[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    :				List[Any]       		=							torch.tensor([im.shape[:2] for im in images]				)
                 lowercase    :				Any       		=							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    :				Any       		=							[self.normalizer(_a				) for x in images]
                 # now pad them to do the following operations
                 lowercase    :				Any       		=							self.pad(_a				)
                 # Normalize

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


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       List[Any]  ) ->				Any:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       Tuple[int, int]  ) ->				List[str]:
       assert torch.isfinite(__a  ).all(), "Box tensor contains infinite or NaN!"
       lowercase    :				List[str]       		=							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'''







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__lowerCAmelCase         =    HUGGINGFACE_HUB_CACHE


__lowerCAmelCase         =    """config.json"""
__lowerCAmelCase         =    """diffusion_pytorch_model.bin"""
__lowerCAmelCase         =    """diffusion_flax_model.msgpack"""
__lowerCAmelCase         =    """model.onnx"""
__lowerCAmelCase         =    """diffusion_pytorch_model.safetensors"""
__lowerCAmelCase         =    """weights.pb"""
__lowerCAmelCase         =    """https://huggingface.co"""
__lowerCAmelCase         =    default_cache_path
__lowerCAmelCase         =    """diffusers_modules"""
__lowerCAmelCase         =    os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
__lowerCAmelCase         =    ["""fp16""", """non-ema"""]
__lowerCAmelCase         =    """.self_attn"""



