






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							, lowercase__							, lowercase__=sys.maxsize						)				->				str:
				__A    =							"bilinear"
				__A    =							max_size
				__A    =							short_edge_length






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

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

				return img_augs





class        a__      :






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				__A    =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							, cfg.INPUT.MAX_SIZE_TEST						)
				__A    =							cfg.INPUT.FORMAT
				__A    =							cfg.SIZE_DIVISIBILITY
				__A    =							cfg.PAD_VALUE
				__A    =							cfg.INPUT.MAX_SIZE_TEST
				__A    =							cfg.MODEL.DEVICE
				__A    =							torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							lambda lowercase__						: (x - self.pixel_mean) / self.pixel_std






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[int]:
				__A    =							tuple(max(lowercase__						) for s in zip(*[img.shape for img in images]						)						)
				__A    =							[im.shape[-2:] for im in images]
				__A    =							[
				    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							, lowercase__							, lowercase__=False						)				->				int:
				with torch.no_grad():
						if not isinstance(lowercase__							, lowercase__						):
								__A    =							[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
						__A    =							torch.tensor([im.shape[:2] for im in images]						)
						__A    =							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
						__A    =							[self.normalizer(lowercase__						) for x in images]
						# now pad them to do the following operations
						__A ,   __A    =							self.pad(lowercase__						)
						# Normalize

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		assert torch.isfinite(lowerCAmelCase__	).all(), "Box tensor contains infinite or NaN!"
		__A ,   __A    =							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__	)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
					snake_case_   :	Optional[int]							      =None


snake_case_   :	List[str]							      =logging.get_logger(__name__)

snake_case_   :	Optional[int]							      ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_   :	Union[str, Any]							      ={
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case_   :	int							      ={
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       int 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       str 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Union[str, Any] 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       int 					= ['input_ids', 'attention_mask']
		UpperCAmelCase_							:       Optional[int] 					= XLMRobertaTokenizer






		def __init__(      self							, lowercase__=None							, lowercase__=None							, lowercase__="<s>"							, lowercase__="</s>"							, lowercase__="</s>"							, lowercase__="<s>"							, lowercase__="<unk>"							, lowercase__="<pad>"							, lowercase__="<mask>"							, **lowercase__							, )				->				List[str]:
				# Mask token behave like a normal word, i.e. include the space before it
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else mask_token

				super().__init__(
				    lowercase__							, tokenizer_file=lowercase__							, bos_token=lowercase__							, eos_token=lowercase__							, sep_token=lowercase__							, cls_token=lowercase__							, unk_token=lowercase__							, pad_token=lowercase__							, mask_token=lowercase__							, **lowercase__							, )

				__A    =							vocab_file
				__A    =							False if not self.vocab_file else True






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:

				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__A    =							[self.cls_token_id]
				__A    =							[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__A    =							[self.sep_token_id]
				__A    =							[self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep						) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				Tuple[str]:
				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
						    "tokenizer."						)

				if not os.path.isdir(lowercase__						):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
						return
				__A    =							os.path.join(
				    lowercase__							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase__						):
						copyfile(self.vocab_file							, lowercase__						)

				return (out_vocab_file,)

