"""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      _lowercase						:

				def __init__(       self    :      str    ,      _lowercase    :      List[Any]    ,      _lowercase    :      int=sys.maxsize       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""bilinear"""
										UpperCAmelCase__   		=						max_size
										UpperCAmelCase__   		=						short_edge_length

				def __call__(       self    :      Any    ,      _lowercase    :      str       ):


										'''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(_lowercase    ,      _lowercase       )
																if h < w:
																						UpperCAmelCase__		, UpperCAmelCase__   		=						size, scale * w
																else:
																						UpperCAmelCase__		, UpperCAmelCase__   		=						scale * h, size
																if max(_lowercase    ,      _lowercase       ) > self.max_size:
																						UpperCAmelCase__   		=						self.max_size * 1.0 / max(_lowercase    ,      _lowercase       )
																						UpperCAmelCase__   		=						newh * scale
																						UpperCAmelCase__   		=						neww * scale
																UpperCAmelCase__   		=						int(neww + 0.5       )
																UpperCAmelCase__   		=						int(newh + 0.5       )

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

										return img_augs


class      _lowercase						:

				def __init__(       self    :      Optional[int]    ,      _lowercase    :      List[Any]       ):


										'''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 _lowercase       : (x - self.pixel_mean) / self.pixel_std

				def     A	(       self    :      str    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						tuple(max(_lowercase       ) for s in zip(*[img.shape for img in images]       )       )
										UpperCAmelCase__   		=						[im.shape[-2:] for im in images]
										UpperCAmelCase__   		=						[
										    nn.functional.pad(
										        _lowercase    ,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,      value=self.pad_value    ,      )
										    for size, im in zip(_lowercase    ,      _lowercase       )
										]

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

				def __call__(       self    :      Optional[int]    ,      _lowercase    :      Dict    ,      _lowercase    :      List[str]=False       ):


										'''simple docstring'''
										with torch.no_grad():
																if not isinstance(_lowercase    ,      _lowercase       ):
																						UpperCAmelCase__   		=						[images]
																if single_image:
																						assert len(_lowercase       ) == 1
																for i in range(len(_lowercase       )       ):
																						if isinstance(images[i]    ,      torch.Tensor       ):
																												images.insert(_lowercase    ,      images.pop(_lowercase       ).to(self.device       ).float()       )
																						elif not isinstance(images[i]    ,      torch.Tensor       ):
																												images.insert(
																												    _lowercase    ,      torch.as_tensor(img_tensorize(images.pop(_lowercase       )    ,      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(_lowercase       )
																# 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(_lowercase       ) for x in images]
																# now pad them to do the following operations
																UpperCAmelCase__		, UpperCAmelCase__   		=						self.pad(_lowercase       )
																# Normalize

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


def 					a__ (						UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     Optional[int]     )->		Optional[Any]:
						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes







def 					a__ (						UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     Tuple[int, int]     )->		Dict:
						assert torch.isfinite(UpperCAmelCase__     ).all(), "Box tensor contains infinite or NaN!"
						UpperCAmelCase__		, UpperCAmelCase__   		=						box_size
						tensor[:, 0].clamp_(min=0						,     max=UpperCAmelCase__     )
						tensor[:, 1].clamp_(min=0						,     max=UpperCAmelCase__     )
						tensor[:, 2].clamp_(min=0						,     max=UpperCAmelCase__     )
						tensor[:, 3].clamp_(min=0						,     max=UpperCAmelCase__     )


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_    :    Optional[Any]											=						logging.get_logger(__name__)

UpperCamelCase_    :    Optional[int]											=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    'rembert'

				def __init__(       self    :      List[Any]    ,      _lowercase    :      Tuple=2_5_0_3_0_0    ,      _lowercase    :      Tuple=1_1_5_2    ,      _lowercase    :      List[Any]=3_2    ,      _lowercase    :      Dict=1_8    ,      _lowercase    :      str=2_5_6    ,      _lowercase    :      Optional[Any]=1_6_6_4    ,      _lowercase    :      Union[str, Any]=4_6_0_8    ,      _lowercase    :      Optional[Any]="gelu"    ,      _lowercase    :      Optional[Any]=0.0    ,      _lowercase    :      Tuple=0.0    ,      _lowercase    :      Dict=0.1    ,      _lowercase    :      str=5_1_2    ,      _lowercase    :      Tuple=2    ,      _lowercase    :      int=0.02    ,      _lowercase    :      Tuple=1e-12    ,      _lowercase    :      Union[str, Any]=True    ,      _lowercase    :      Optional[int]=0    ,      _lowercase    :      Any=3_1_2    ,      _lowercase    :      Optional[Any]=3_1_3    ,      **_lowercase    :      str    ,      ):


										'''simple docstring'''
										super().__init__(pad_token_id=_lowercase    ,      bos_token_id=_lowercase    ,      eos_token_id=_lowercase    ,      **_lowercase       )

										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						input_embedding_size
										UpperCAmelCase__   		=						output_embedding_size
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						hidden_size
										UpperCAmelCase__   		=						num_hidden_layers
										UpperCAmelCase__   		=						num_attention_heads
										UpperCAmelCase__   		=						intermediate_size
										UpperCAmelCase__   		=						hidden_act
										UpperCAmelCase__   		=						hidden_dropout_prob
										UpperCAmelCase__   		=						attention_probs_dropout_prob
										UpperCAmelCase__   		=						classifier_dropout_prob
										UpperCAmelCase__   		=						initializer_range
										UpperCAmelCase__   		=						type_vocab_size
										UpperCAmelCase__   		=						layer_norm_eps
										UpperCAmelCase__   		=						use_cache
										UpperCAmelCase__   		=						False


class      _lowercase						(				__lowerCamelCase					):

				@property
				def     A	(       self    :      str       ):


										'''simple docstring'''
										if self.task == "multiple-choice":
																UpperCAmelCase__   		=						{0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																UpperCAmelCase__   		=						{0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""token_type_ids""", dynamic_axis),
										    ]       )

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return 1e-4

