'''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     :						Optional[Any]							,							UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						List[Any]=sys.maxsize):


         """simple docstring"""







         a					:							int							= 'bilinear'
         a					:							str							= max_size
         a					:							Optional[int]							= short_edge_length







    def __call__(					self     :						Tuple							,							UpperCAmelCase_     :						Optional[Any]):


         """simple docstring"""







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

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

         return img_augs






class   UpperCamelCase					:







    """simple docstring"""





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						str):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						str):


         """simple docstring"""







         a					:							Optional[int]							= tuple(max(UpperCAmelCase_) for s in zip(*[img.shape for img in images]))
         a					:							int							= [im.shape[-2:] for im in images]
         a					:							List[Any]							= [
             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     :						int							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						str=False):


         """simple docstring"""







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

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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[str]				,	snake_case       :				List[str]  )   ->				List[str]:







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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				Tuple[int, int]  )   ->				Optional[Any]:







     """simple docstring"""
     assert torch.isfinite(snake_case  ).all(), "Box tensor contains infinite or NaN!"
     a							,			a					:							str							= box_size
     tensor[:, 0].clamp_(min=0				,	max=snake_case  )
     tensor[:, 1].clamp_(min=0				,	max=snake_case  )
     tensor[:, 2].clamp_(min=0				,	max=snake_case  )
     tensor[:, 3].clamp_(min=0				,	max=snake_case  )


'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Optional[int]			=    {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			"cpmant"





    def __init__(					self     :						Tuple							,							UpperCAmelCase_     :						int = 3_0_7_2_0							,							UpperCAmelCase_     :						int = 4_0_9_6							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 1_2_8							,							UpperCAmelCase_     :						int = 1_0_2_4_0							,							UpperCAmelCase_     :						int = 4_8							,							UpperCAmelCase_     :						int = 0.0							,							UpperCAmelCase_     :						int = 5_1_2							,							UpperCAmelCase_     :						int = 2_0_4_8							,							UpperCAmelCase_     :						int = 1e-6							,							UpperCAmelCase_     :						float = 1.0							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						bool = True							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         a					:							Union[str, Any]							= prompt_types
         a					:							List[str]							= prompt_length
         a					:							List[Any]							= segment_types
         a					:							Any							= hidden_size
         a					:							Optional[int]							= num_attention_heads
         a					:							Union[str, Any]							= dim_head
         a					:							int							= dim_ff
         a					:							Union[str, Any]							= num_hidden_layers
         a					:							List[Any]							= position_bias_num_buckets
         a					:							Optional[int]							= position_bias_max_distance
         a					:							str							= dropout_p
         a					:							List[str]							= eps
         a					:							Dict							= use_cache
         a					:							Tuple							= vocab_size
         a					:							Optional[Any]							= init_std

