






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      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Dict=sys.maxsize		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      'bilinear'
						A	 =      max_size
						A	 =      short_edge_length







		def __call__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   Any:


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

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

						return img_augs


class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str		)    ->   Dict:


						'''simple docstring'''
						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 _SCREAMING_SNAKE_CASE		: (x - self.pixel_mean) / self.pixel_std
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   Optional[int]:


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

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







		def __call__(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=False		)    ->   Optional[Any]:


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

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







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any]   ):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Tuple[int, int]   ):
				assert torch.isfinite(UpperCAmelCase   ).all(), "Box tensor contains infinite or NaN!"
				A ,		A	 =      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   )













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_albert import AlbertTokenizer
else:
					lowerCAmelCase_           = None

lowerCAmelCase_           = logging.get_logger(__name__)
lowerCAmelCase_           = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_           = {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_           = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

lowerCAmelCase_           = '▁'
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   AlbertTokenizer
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       int=None						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       List[str]=True						,_SCREAMING_SNAKE_CASE		:       str=True						,_SCREAMING_SNAKE_CASE		:       Optional[int]=False						,_SCREAMING_SNAKE_CASE		:       List[str]="[CLS]"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="[SEP]"						,_SCREAMING_SNAKE_CASE		:       Tuple="<unk>"						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]="[SEP]"						,_SCREAMING_SNAKE_CASE		:       Tuple="<pad>"						,_SCREAMING_SNAKE_CASE		:       Any="[CLS]"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="[MASK]"						,**_SCREAMING_SNAKE_CASE		:       Any						,)    ->   Tuple:


						'''simple docstring'''
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						A	 =      (
						    AddedToken(_SCREAMING_SNAKE_CASE						,lstrip=_SCREAMING_SNAKE_CASE						,rstrip=_SCREAMING_SNAKE_CASE						,normalized=_SCREAMING_SNAKE_CASE		)
						    if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						    else mask_token
						)

						super().__init__(
						    _SCREAMING_SNAKE_CASE						,tokenizer_file=_SCREAMING_SNAKE_CASE						,do_lower_case=_SCREAMING_SNAKE_CASE						,remove_space=_SCREAMING_SNAKE_CASE						,keep_accents=_SCREAMING_SNAKE_CASE						,bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,sep_token=_SCREAMING_SNAKE_CASE						,pad_token=_SCREAMING_SNAKE_CASE						,cls_token=_SCREAMING_SNAKE_CASE						,mask_token=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      do_lower_case
						A	 =      remove_space
						A	 =      keep_accents
						A	 =      vocab_file
						A	 =      False if not self.vocab_file else True
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       List[int]						,_SCREAMING_SNAKE_CASE		:       Optional[List[int]] = None		)    ->   List[int]:


						'''simple docstring'''
						A	 =      [self.sep_token_id]
						A	 =      [self.cls_token_id]
						if token_ids_a is None:
										return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[int]						,_SCREAMING_SNAKE_CASE		:       Optional[List[int]] = None		)    ->   List[int]:


						'''simple docstring'''
						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		) * [0] + len(token_ids_a + sep		) * [1]







		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						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(_SCREAMING_SNAKE_CASE		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
						A	 =      os.path.join(
						    _SCREAMING_SNAKE_CASE						,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

						return (out_vocab_file,)





