



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 SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=sys.maxsize		):
        A			:	List[str]										=       """bilinear"""
        A			:	Optional[Any]										=       max_size
        A			:	Dict										=       short_edge_length






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

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

        return img_augs






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






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






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Any										=       tuple(max(lowerCamelCase__		) for s in zip(*[img.shape for img in images]		)		)
        A			:	List[Any]										=       [im.shape[-2:] for im in images]
        A			:	Dict										=       [
            nn.functional.pad(
                lowerCamelCase__,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],		value=self.pad_value,		)
            for size, im in zip(lowerCamelCase__,		lowerCamelCase__		)
        ]

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






    def __call__(	self,		lowerCamelCase__,		lowerCamelCase__=False		):
        with torch.no_grad():
            if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
                A			:	List[Any]										=       [images]
            if single_image:
                assert len(lowerCamelCase__		) == 1
            for i in range(len(lowerCamelCase__		)		):
                if isinstance(images[i],		torch.Tensor		):
                    images.insert(lowerCamelCase__,		images.pop(lowerCamelCase__		).to(self.device		).float()		)
                elif not isinstance(images[i],		torch.Tensor		):
                    images.insert(
                        lowerCamelCase__,		torch.as_tensor(img_tensorize(images.pop(lowerCamelCase__		),		input_format=self.input_format		)		)
                        .to(self.device		)
                        .float(),		)
            # resize smallest edge
            A			:	List[str]										=       torch.tensor([im.shape[:2] for im in images]		)
            A			:	Optional[Any]										=       self.aug(lowerCamelCase__		)
            # 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			:	Union[str, Any]										=       [self.normalizer(lowerCamelCase__		) for x in images]
            # now pad them to do the following operations
            A					,							A			:	str										=       self.pad(lowerCamelCase__		)
            # Normalize

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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    assert torch.isfinite(_lowerCAmelCase		).all(), "Box tensor contains infinite or NaN!"
    A					,							A			:	int										=       box_size
    tensor[:, 0].clamp_(min=0				,					max=_lowerCAmelCase		)
    tensor[:, 1].clamp_(min=0				,					max=_lowerCAmelCase		)
    tensor[:, 2].clamp_(min=0				,					max=_lowerCAmelCase		)
    tensor[:, 3].clamp_(min=0				,					max=_lowerCAmelCase		)







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				"roberta"






    def __init__(	self,		lowerCamelCase__=5_0265,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A			:	List[str]										=       vocab_size
        A			:	List[Any]										=       hidden_size
        A			:	List[Any]										=       num_hidden_layers
        A			:	Optional[Any]										=       num_attention_heads
        A			:	Union[str, Any]										=       hidden_act
        A			:	Union[str, Any]										=       intermediate_size
        A			:	Optional[int]										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	int										=       max_position_embeddings
        A			:	List[str]										=       type_vocab_size
        A			:	List[str]										=       initializer_range
        A			:	Dict										=       layer_norm_eps
        A			:	Optional[int]										=       position_embedding_type
        A			:	Any										=       use_cache
        A			:	Optional[int]										=       classifier_dropout






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Optional[Any]										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	int										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


