


'''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 			_lowercase :
   def __init__(						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=sys.maxsize      ) ->							Optional[Any]:
        __snake_case           =						'bilinear'
        __snake_case           =						max_size
        __snake_case           =						short_edge_length





   def __call__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      List[Any]      ) ->							List[str]:
        __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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
             if h < w:
                  __snake_case      ,       __snake_case           =						size, scale * w
             else:
                  __snake_case      ,       __snake_case           =						scale * h, size
             if max(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) > self.max_size:
                  __snake_case           =						self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
                  __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(SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						pil_image.resize((neww, newh)       ,   PILImageResampling.BILINEAR      )
                  __snake_case           =						np.asarray(SCREAMING_SNAKE_CASE_      )
             else:
                  __snake_case           =						img.permute(2       ,   0       ,   1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                  __snake_case           =						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 			_lowercase :
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Dict      ) ->							List[Any]:
        __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 SCREAMING_SNAKE_CASE_      : (x - self.pixel_mean) / self.pixel_std
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      int      ) ->							int:
        __snake_case           =						tuple(max(SCREAMING_SNAKE_CASE_      ) for s in zip(*[img.shape for img in images]      )      )
        __snake_case           =						[im.shape[-2:] for im in images]
        __snake_case           =						[
            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       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=False      ) ->							str:
        with torch.no_grad():
             if not isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ):
                  __snake_case           =						[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
             __snake_case           =						torch.tensor([im.shape[:2] for im in images]      )
             __snake_case           =						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
             __snake_case           =						[self.normalizer(SCREAMING_SNAKE_CASE_      ) for x in images]
             # now pad them to do the following operations
             __snake_case      ,       __snake_case           =						self.pad(SCREAMING_SNAKE_CASE_      )
             # Normalize

             if self.size_divisibility > 0:
                  raise NotImplementedError()
             # pad
             __snake_case           =						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   _a		(lowercase__			:   Tuple							,   lowercase__			:   str						)			->			Tuple:






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





def   _a		(lowercase__			:   Union[str, Any]							,   lowercase__			:   Tuple[int, int]						)			->			Any:






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











"""simple docstring"""



import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_					=     get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	AlbertTokenizer
 _lowerCamelCase								=	AlbertTokenizerFast
 _lowerCamelCase								=	True
 _lowerCamelCase								=	True
 _lowerCamelCase								=	True

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().setUp()

     # We have a SentencePiece fixture for testing
     __A       :		str							=	AlbertTokenizer(__lowerCamelCase    )
     tokenizer.save_pretrained(self.tmpdirname    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Tuple							=	'''this is a test'''
     __A       :		Union[str, Any]							=	'''this is a test'''
     return input_text, output_text

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	'''<pad>'''
     __A       :		Optional[int]							=	0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase    )   ,		__lowerCamelCase    )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase    )   ,		__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	list(self.get_tokenizer().get_vocab().keys()    )

     self.assertEqual(vocab_keys[0]   ,		'''<pad>'''    )
     self.assertEqual(vocab_keys[1]   ,		'''<unk>'''    )
     self.assertEqual(vocab_keys[-1]   ,		'''▁eloquent'''    )
     self.assertEqual(len(__lowerCamelCase    )   ,		3_0000    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size   ,		3_0000    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A       :		Dict							=	self.get_tokenizer()
     __A       :		List[Any]							=	self.get_rust_tokenizer()

     __A       :		int							=	'''I was born in 92000, and this is falsé.'''

     __A       :		Any							=	tokenizer.tokenize(__lowerCamelCase    )
     __A       :		Dict							=	rust_tokenizer.tokenize(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		List[Any]							=	tokenizer.encode(__lowerCamelCase   ,		add_special_tokens=__lowerCamelCase    )
     __A       :		List[str]							=	rust_tokenizer.encode(__lowerCamelCase   ,		add_special_tokens=__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		List[Any]							=	self.get_rust_tokenizer()
     __A       :		Union[str, Any]							=	tokenizer.encode(__lowerCamelCase    )
     __A       :		List[Any]							=	rust_tokenizer.encode(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	AlbertTokenizer(__lowerCamelCase   ,		keep_accents=__lowerCamelCase    )

     __A       :		Optional[int]							=	tokenizer.tokenize('''This is a test'''    )
     self.assertListEqual(__lowerCamelCase   ,		['''▁this''', '''▁is''', '''▁a''', '''▁test''']    )

     self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase    )   ,		[48, 25, 21, 1289]    )

     __A       :		Tuple							=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
     self.assertListEqual(
         __lowerCamelCase   ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']    )
     __A       :		Optional[Any]							=	tokenizer.convert_tokens_to_ids(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

     __A       :		Optional[Any]							=	tokenizer.convert_ids_to_tokens(__lowerCamelCase    )
     self.assertListEqual(
         __lowerCamelCase   ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']   ,		)

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	AlbertTokenizer(__lowerCamelCase    )

     __A       :		List[Any]							=	tokenizer.encode('''sequence builders'''    )
     __A       :		str							=	tokenizer.encode('''multi-sequence build'''    )

     __A       :		Union[str, Any]							=	tokenizer.build_inputs_with_special_tokens(__lowerCamelCase    )
     __A       :		Optional[int]							=	tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   ,		__lowerCamelCase    )

     assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
     assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
         tokenizer.sep_token_id
     ]







 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	{'''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''input_ids''': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__lowerCamelCase   ,		model_name='''albert-base-v2'''   ,		revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'''   ,		)




