


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      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=sys.maxsize						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      'bilinear'
									_UpperCAmelCase					=      max_size
									_UpperCAmelCase					=      short_edge_length






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





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

																if img.dtype == np.uinta:
																							_UpperCAmelCase					=      Image.fromarray(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      pil_image.resize((neww, newh)      ,   PILImageResampling.BILINEAR						)
																							_UpperCAmelCase					=      np.asarray(_SCREAMING_SNAKE_CASE						)
																else:
																							_UpperCAmelCase					=      img.permute(2      ,   0      ,   1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																							_UpperCAmelCase					=      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      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





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




									"""simple docstring"""





									_UpperCAmelCase					=      tuple(max(_SCREAMING_SNAKE_CASE						) for s in zip(*[img.shape for img in images]						)						)
									_UpperCAmelCase					=      [im.shape[-2:] for im in images]
									_UpperCAmelCase					=      [
									    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      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	List[Any]:




									"""simple docstring"""





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

																if self.size_divisibility > 0:
																							raise NotImplementedError()
																# pad
																_UpperCAmelCase					=      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__:		Any      ,  a__:		Union[str, Any]	)  ->		Optional[Any]:


							'''simple docstring'''





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







def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		Tuple[int, int]	)  ->		Any:


							'''simple docstring'''





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




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

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available

@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  Union[List[np.ndarray], torch.FloatTensor]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_text_to_video_synth import TextToVideoSDPipeline
		from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
		from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
