




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 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						List[Any]=sys.maxsize		):
       SCREAMING_SNAKE_CASE_      		=     'bilinear'
       SCREAMING_SNAKE_CASE_      		=     max_size
       SCREAMING_SNAKE_CASE_      		=     short_edge_length




 def __call__(				self    :						int							,							_lowerCAmelCase    :						Any		):
       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(_lowerCAmelCase							,							_lowerCAmelCase		)
             if h < w:
                   SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     size, scale * w
             else:
                   SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     scale * h, size
             if max(_lowerCAmelCase							,							_lowerCAmelCase		) > self.max_size:
                   SCREAMING_SNAKE_CASE_      		=     self.max_size * 1.0 / max(_lowerCAmelCase							,							_lowerCAmelCase		)
                   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(_lowerCAmelCase		)
                   SCREAMING_SNAKE_CASE_      		=     pil_image.resize((neww, newh)							,							PILImageResampling.BILINEAR		)
                   SCREAMING_SNAKE_CASE_      		=     np.asarray(_lowerCAmelCase		)
             else:
                   SCREAMING_SNAKE_CASE_      		=     img.permute(2							,							0							,							1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
                   SCREAMING_SNAKE_CASE_      		=     nn.functional.interpolate(
                       _lowerCAmelCase							,							(newh, neww)							,							mode=self.interp_method							,							align_corners=_lowerCAmelCase		).squeeze(0		)
             img_augs.append(_lowerCAmelCase		)

       return img_augs





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Optional[Any]		):
       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 _lowerCAmelCase		: (x - self.pixel_mean) / self.pixel_std



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     tuple(max(_lowerCAmelCase		) 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(
               _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    :						List[str]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						List[str]=False		):
       with torch.no_grad():
             if not isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
                   SCREAMING_SNAKE_CASE_      		=     [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
             SCREAMING_SNAKE_CASE_      		=     torch.tensor([im.shape[:2] for im in images]		)
             SCREAMING_SNAKE_CASE_      		=     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
             SCREAMING_SNAKE_CASE_      		=     [self.normalizer(_lowerCAmelCase		) for x in images]
             # now pad them to do the following operations
             SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.pad(_lowerCAmelCase		)
             # Normalize

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


def    UpperCAmelCase_				( __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      Any				)   ->					Union[str, Any]:
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes





def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      Tuple[int, int]				)   ->					Any:
      assert torch.isfinite(__UpperCAmelCase				).all(), "Box tensor contains infinite or NaN!"
      SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     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'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   ["image_processor", "tokenizer"]
     snake_case_						      =   "LayoutLMv2ImageProcessor"
     snake_case_						      =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



     def __init__(   self							:			List[Any]   ,       __snake_case							:			Union[str, Any]=None   ,       __snake_case							:			Optional[int]=None   ,       **__snake_case							:			Union[str, Any]						)->	str:
        if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,       __snake_case   ,       )
           snake_case 						=   kwargs.pop("""feature_extractor"""						)

        snake_case 						=   image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""						)
        if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""						)

        super().__init__(__snake_case   ,       __snake_case						)



     def __call__(   self							:			Optional[Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,       __snake_case							:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,       __snake_case							:			Union[List[List[int]], List[List[List[int]]]] = None   ,       __snake_case							:			Optional[Union[List[int], List[List[int]]]] = None   ,       __snake_case							:			bool = True   ,       __snake_case							:			Union[bool, str, PaddingStrategy] = False   ,       __snake_case							:			Union[bool, str, TruncationStrategy] = None   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			int = 0   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = True   ,       __snake_case							:			Optional[Union[str, TensorType]] = None   ,       **__snake_case							:			List[str]   ,       )->	BatchEncoding:
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               """You cannot provide bounding boxes """
               """if you initialized the image processor with apply_ocr set to True."""						)

        if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""						)

        if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""						)

        # first, apply the image processor
        snake_case 						=   self.image_processor(images=__snake_case   ,       return_tensors=__snake_case						)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(__snake_case   ,       __snake_case						):
              snake_case 						=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
           snake_case 						=   features["""words"""]

        snake_case 						=   self.tokenizer(
            text=text if text is not None else features["""words"""]   ,       text_pair=text_pair if text_pair is not None else None   ,       boxes=boxes if boxes is not None else features["""boxes"""]   ,       word_labels=__snake_case   ,       add_special_tokens=__snake_case   ,       padding=__snake_case   ,       truncation=__snake_case   ,       max_length=__snake_case   ,       stride=__snake_case   ,       pad_to_multiple_of=__snake_case   ,       return_token_type_ids=__snake_case   ,       return_attention_mask=__snake_case   ,       return_overflowing_tokens=__snake_case   ,       return_special_tokens_mask=__snake_case   ,       return_offsets_mapping=__snake_case   ,       return_length=__snake_case   ,       verbose=__snake_case   ,       return_tensors=__snake_case   ,       **__snake_case   ,       )

        # add pixel values
        snake_case 						=   features.pop("""pixel_values"""						)
        if return_overflowing_tokens is True:
           snake_case 						=   self.get_overflowing_images(__snake_case   ,       encoded_inputs["""overflow_to_sample_mapping"""]						)
        snake_case 						=   images

        return encoded_inputs



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			List[Any]   ,       __snake_case							:			str						)->	List[str]:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        snake_case 						=   []
        for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

        if len(__snake_case						) != len(__snake_case						):
           raise ValueError(
               """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
               f''' {len(__snake_case						)} and {len(__snake_case						)}'''						)

        return images_with_overflow



     def      lowerCAmelCase			(   self							:			int   ,       *__snake_case							:			Optional[int]   ,       **__snake_case							:			Tuple						)->	str:
        return self.tokenizer.batch_decode(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict   ,       *__snake_case							:			Tuple   ,       **__snake_case							:			Optional[int]						)->	List[Any]:
        return self.tokenizer.decode(*__snake_case   ,       **__snake_case						)



     @property
     def      lowerCAmelCase			(   self							:			str						)->	int:
        return ["input_ids", "bbox", "attention_mask", "image"]



     @property
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[str]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,       __snake_case   ,       )
        return self.image_processor_class



     @property
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Tuple:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,       __snake_case   ,       )
        return self.image_processor

