


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


snake_case_  :     str							  =							{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_  :     Tuple							  =							['''CpmTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_  :     Dict							  =							['''CpmTokenizerFast''']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_cpm import CpmTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_cpm_fast import CpmTokenizerFast

else:
							import sys

							snake_case_  :     int							  =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



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   : List[Any]			,      _a   : List[str]			,      _a   : Optional[Any]=sys.maxsize					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''bilinear'''
										_SCREAMING_SNAKE_CASE     					=max_size
										_SCREAMING_SNAKE_CASE     					=short_edge_length




						def __call__(    self   : Any			,      _a   : Tuple					)							->       List[str]:


										"""simple docstring"""





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

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


										"""simple docstring"""





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




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





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


										"""simple docstring"""





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

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







def   lowerCamelCase(				a__		,a__):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   lowerCamelCase(				a__		,a__):
				assert torch.isfinite(a__).all(), "Box tensor contains infinite or NaN!"
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=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__)