

'''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 lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=sys.maxsize )    ->							List[str]:
					snake_case							=					'bilinear'
					snake_case							=					max_size
					snake_case							=					short_edge_length






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

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

					return img_augs







class lowerCamelCase   :

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

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

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

def      __magic_name__							(			A       , A				)      ->		str:
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes


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













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:
	UpperCAmelCase_					:		str           = None


UpperCAmelCase_					:		int           = logging.get_logger(__name__)

UpperCAmelCase_					:		List[str]           = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_					:		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'''
        ),
    },
}

UpperCAmelCase_					:		Any           = {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Dict	      =    VOCAB_FILES_NAMES
	snake_case__				:	Optional[int]	      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:	str	      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:	int	      =    ["""input_ids""", """attention_mask"""]
	snake_case__				:	Dict	      =    XLMRobertaTokenizer





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

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

		UpperCamelCase	:Optional[int]		=      vocab_file
		UpperCamelCase	:List[Any]		=      False if not self.vocab_file else True





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):

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





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):
		UpperCamelCase	:str		=      [self.sep_token_id]
		UpperCamelCase	:int		=      [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 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[str] = None      ):
		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(__lowerCamelCase      ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
			return
		UpperCamelCase	:Union[str, Any]		=      os.path.join(
		    __lowerCamelCase					,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

		return (out_vocab_file,)
