"""simple docstring"""







import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   int					=	BertTokenizer
				UpperCAmelCase       :   Dict					=	BertTokenizerFast
				UpperCAmelCase       :   Tuple					=	True
				UpperCAmelCase       :   str					=	True
				UpperCAmelCase       :   List[str]					=	filter_non_english



				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					super().setUp()

					__snake_case    :							Optional[int]   	=				[
					    '[UNK]',
					    '[CLS]',
					    '[SEP]',
					    '[PAD]',
					    '[MASK]',
					    'want',
					    '##want',
					    '##ed',
					    'wa',
					    'un',
					    'runn',
					    '##ing',
					    ',',
					    'low',
					    'lowest',
					]
					__snake_case    :							List[str]   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file'])
					with open(self.vocab_file			,						'w'			,						encoding='utf-8') as vocab_writer:
						vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))



				def       _lowercase  (self       :   Tuple			,						_A       :   Union[str, Any])					->			List[Any]:
					__snake_case    :							str   	=				'UNwant\u00E9d,running'
					__snake_case    :							Optional[Any]   	=				'unwanted, running'
					return input_text, output_text



				def       _lowercase  (self       :   Tuple)					->			List[str]:
					__snake_case    :							str   	=				self.tokenizer_class(self.vocab_file)

					__snake_case    :							List[str]   	=				tokenizer.tokenize('UNwant\u00E9d,running')
					self.assertListEqual(_A			,						['un', '##want', '##ed', ',', 'runn', '##ing'])
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)			,						[9, 6, 7, 12, 10, 11])



				def       _lowercase  (self       :   Any)					->			str:
					if not self.test_rust_tokenizer:
						return

					__snake_case    :							List[Any]   	=				self.get_tokenizer()
					__snake_case    :							Union[str, Any]   	=				self.get_rust_tokenizer()

					__snake_case    :							List[Any]   	=				'UNwant\u00E9d,running'

					__snake_case    :							Union[str, Any]   	=				tokenizer.tokenize(_A)
					__snake_case    :							Dict   	=				rust_tokenizer.tokenize(_A)
					self.assertListEqual(_A			,						_A)

					__snake_case    :							Union[str, Any]   	=				tokenizer.encode(_A			,						add_special_tokens=_A)
					__snake_case    :							Any   	=				rust_tokenizer.encode(_A			,						add_special_tokens=_A)
					self.assertListEqual(_A			,						_A)

					__snake_case    :							Dict   	=				self.get_rust_tokenizer()
					__snake_case    :							Dict   	=				tokenizer.encode(_A)
					__snake_case    :							Dict   	=				rust_tokenizer.encode(_A)
					self.assertListEqual(_A			,						_A)

					# With lower casing
					__snake_case    :							int   	=				self.get_tokenizer(do_lower_case=_A)
					__snake_case    :							Optional[int]   	=				self.get_rust_tokenizer(do_lower_case=_A)

					__snake_case    :							List[Any]   	=				'UNwant\u00E9d,running'

					__snake_case    :							Tuple   	=				tokenizer.tokenize(_A)
					__snake_case    :							Any   	=				rust_tokenizer.tokenize(_A)
					self.assertListEqual(_A			,						_A)

					__snake_case    :							Optional[Any]   	=				tokenizer.encode(_A			,						add_special_tokens=_A)
					__snake_case    :							List[Any]   	=				rust_tokenizer.encode(_A			,						add_special_tokens=_A)
					self.assertListEqual(_A			,						_A)

					__snake_case    :							List[str]   	=				self.get_rust_tokenizer()
					__snake_case    :							Optional[int]   	=				tokenizer.encode(_A)
					__snake_case    :							List[str]   	=				rust_tokenizer.encode(_A)
					self.assertListEqual(_A			,						_A)



				def       _lowercase  (self       :   List[Any])					->			Union[str, Any]:
					__snake_case    :							Any   	=				BasicTokenizer()

					self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz')			,						['ah', '\u535A', '\u63A8', 'zz'])



				def       _lowercase  (self       :   str)					->			List[str]:
					__snake_case    :							List[Any]   	=				BasicTokenizer(do_lower_case=_A)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')			,						['hello', '!', 'how', 'are', 'you', '?'])
					self.assertListEqual(tokenizer.tokenize('H\u00E9llo')			,						['hello'])



				def       _lowercase  (self       :   List[Any])					->			int:
					__snake_case    :							List[Any]   	=				BasicTokenizer(do_lower_case=_A			,						strip_accents=_A)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')			,						['hällo', '!', 'how', 'are', 'you', '?'])
					self.assertListEqual(tokenizer.tokenize('H\u00E9llo')			,						['h\u00E9llo'])



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							str   	=				BasicTokenizer(do_lower_case=_A			,						strip_accents=_A)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')			,						['hallo', '!', 'how', 'are', 'you', '?'])
					self.assertListEqual(tokenizer.tokenize('H\u00E9llo')			,						['hello'])



				def       _lowercase  (self       :   Union[str, Any])					->			List[Any]:
					__snake_case    :							Any   	=				BasicTokenizer(do_lower_case=_A)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')			,						['hallo', '!', 'how', 'are', 'you', '?'])
					self.assertListEqual(tokenizer.tokenize('H\u00E9llo')			,						['hello'])



				def       _lowercase  (self       :   Union[str, Any])					->			Optional[int]:
					__snake_case    :							int   	=				BasicTokenizer(do_lower_case=_A)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')			,						['HeLLo', '!', 'how', 'Are', 'yoU', '?'])



				def       _lowercase  (self       :   Tuple)					->			Dict:
					__snake_case    :							Optional[Any]   	=				BasicTokenizer(do_lower_case=_A			,						strip_accents=_A)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')			,						['HäLLo', '!', 'how', 'Are', 'yoU', '?'])



				def       _lowercase  (self       :   Tuple)					->			Optional[Any]:
					__snake_case    :							Dict   	=				BasicTokenizer(do_lower_case=_A			,						strip_accents=_A)

					self.assertListEqual(
					    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')			,						['HaLLo', '!', 'how', 'Are', 'yoU', '?'])



				def       _lowercase  (self       :   int)					->			str:
					__snake_case    :							List[Any]   	=				BasicTokenizer(do_lower_case=_A			,						never_split=['[UNK]'])

					self.assertListEqual(
					    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]')			,						['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'])



				def       _lowercase  (self       :   Any)					->			int:
					__snake_case    :							List[Any]   	=				BasicTokenizer()
					__snake_case    :							Optional[Any]   	=				'a\n\'ll !!to?\'d of, can\'t.'
					__snake_case    :							Dict   	=				['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
					self.assertListEqual(tokenizer.tokenize(_A)			,						_A)



				def       _lowercase  (self       :   Optional[Any])					->			Optional[int]:
					__snake_case    :							Union[str, Any]   	=				['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

					__snake_case    :							Any   	=				{}
					for i, token in enumerate(_A):
						__snake_case    :							Dict   	=				i
					__snake_case    :							List[Any]   	=				WordpieceTokenizer(vocab=_A			,						unk_token='[UNK]')

					self.assertListEqual(tokenizer.tokenize('')			,						[])

					self.assertListEqual(tokenizer.tokenize('unwanted running')			,						['un', '##want', '##ed', 'runn', '##ing'])

					self.assertListEqual(tokenizer.tokenize('unwantedX running')			,						['[UNK]', 'runn', '##ing'])



				def       _lowercase  (self       :   List[str])					->			List[Any]:
					self.assertTrue(_is_whitespace(' '))
					self.assertTrue(_is_whitespace('\t'))
					self.assertTrue(_is_whitespace('\r'))
					self.assertTrue(_is_whitespace('\n'))
					self.assertTrue(_is_whitespace('\u00A0'))

					self.assertFalse(_is_whitespace('A'))
					self.assertFalse(_is_whitespace('-'))



				def       _lowercase  (self       :   List[Any])					->			Dict:
					self.assertTrue(_is_control('\u0005'))

					self.assertFalse(_is_control('A'))
					self.assertFalse(_is_control(' '))
					self.assertFalse(_is_control('\t'))
					self.assertFalse(_is_control('\r'))



				def       _lowercase  (self       :   int)					->			str:
					self.assertTrue(_is_punctuation('-'))
					self.assertTrue(_is_punctuation('$'))
					self.assertTrue(_is_punctuation('`'))
					self.assertTrue(_is_punctuation('.'))

					self.assertFalse(_is_punctuation('A'))
					self.assertFalse(_is_punctuation(' '))



				def       _lowercase  (self       :   Dict)					->			Union[str, Any]:
					__snake_case    :							Any   	=				self.get_tokenizer()
					__snake_case    :							Union[str, Any]   	=				self.get_rust_tokenizer()

					# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
					self.assertListEqual([tokenizer.tokenize(_A) for t in ['Test', '\xad', 'test']]			,						[['[UNK]'], [], ['[UNK]']])

					self.assertListEqual(
					    [rust_tokenizer.tokenize(_A) for t in ['Test', '\xad', 'test']]			,						[['[UNK]'], [], ['[UNK]']])



				@slow
				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				self.tokenizer_class.from_pretrained('bert-base-uncased')

					__snake_case    :							List[str]   	=				tokenizer.encode('sequence builders'			,						add_special_tokens=_A)
					__snake_case    :							List[Any]   	=				tokenizer.encode('multi-sequence build'			,						add_special_tokens=_A)

					__snake_case    :							Union[str, Any]   	=				tokenizer.build_inputs_with_special_tokens(_A)
					__snake_case    :							Tuple   	=				tokenizer.build_inputs_with_special_tokens(_A			,						_A)

					assert encoded_sentence == [1_01] + text + [1_02]
					assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]



				def       _lowercase  (self       :   Dict)					->			Any:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case    :							Optional[Any]   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)

							__snake_case    :							List[Any]   	=				f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
							__snake_case    :							List[Any]   	=				tokenizer_r.encode_plus(
							    _A			,						return_attention_mask=_A			,						return_token_type_ids=_A			,						return_offsets_mapping=_A			,						add_special_tokens=_A			,						)

							__snake_case    :							Any   	=				tokenizer_r.do_lower_case if hasattr(_A			,						'do_lower_case') else False
							__snake_case    :							Dict   	=				(
							    [
							        ((0, 0), tokenizer_r.cls_token),
							        ((0, 1), 'A'),
							        ((1, 2), ','),
							        ((3, 5), 'na'),
							        ((5, 6), '##ï'),
							        ((6, 8), '##ve'),
							        ((9, 15), tokenizer_r.mask_token),
							        ((16, 21), 'Allen'),
							        ((21, 23), '##NL'),
							        ((23, 24), '##P'),
							        ((25, 33), 'sentence'),
							        ((33, 34), '.'),
							        ((0, 0), tokenizer_r.sep_token),
							    ]
							    if not do_lower_case
							    else [
							        ((0, 0), tokenizer_r.cls_token),
							        ((0, 1), 'a'),
							        ((1, 2), ','),
							        ((3, 8), 'naive'),
							        ((9, 15), tokenizer_r.mask_token),
							        ((16, 21), 'allen'),
							        ((21, 23), '##nl'),
							        ((23, 24), '##p'),
							        ((25, 33), 'sentence'),
							        ((33, 34), '.'),
							        ((0, 0), tokenizer_r.sep_token),
							    ]
							)

							self.assertEqual(
							    [e[1] for e in expected_results]			,						tokenizer_r.convert_ids_to_tokens(tokens['input_ids']))
							self.assertEqual([e[0] for e in expected_results]			,						tokens['offset_mapping'])






				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							List[Any]   	=				['的', '人', '有']
					__snake_case    :							str   	=				''.join(_A)
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case    :							Optional[Any]   	=				True
							__snake_case    :							Dict   	=				self.tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							str   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)

							__snake_case    :							List[str]   	=				tokenizer_p.encode(_A			,						add_special_tokens=_A)
							__snake_case    :							Tuple   	=				tokenizer_r.encode(_A			,						add_special_tokens=_A)

							__snake_case    :							List[Any]   	=				tokenizer_r.convert_ids_to_tokens(_A)
							__snake_case    :							Tuple   	=				tokenizer_p.convert_ids_to_tokens(_A)

							# it is expected that each Chinese character is not preceded by "##"
							self.assertListEqual(_A			,						_A)
							self.assertListEqual(_A			,						_A)

							__snake_case    :							List[Any]   	=				False
							__snake_case    :							Union[str, Any]   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							str   	=				self.tokenizer_class.from_pretrained(_A			,						**_A)

							__snake_case    :							List[Any]   	=				tokenizer_r.encode(_A			,						add_special_tokens=_A)
							__snake_case    :							Dict   	=				tokenizer_p.encode(_A			,						add_special_tokens=_A)

							__snake_case    :							Dict   	=				tokenizer_r.convert_ids_to_tokens(_A)
							__snake_case    :							Any   	=				tokenizer_p.convert_ids_to_tokens(_A)

							# it is expected that only the first Chinese character is not preceded by "##".
							__snake_case    :							Dict   	=				[
							    f"##{token}" if idx != 0 else token for idx, token in enumerate(_A)
							]
							self.assertListEqual(_A			,						_A)
							self.assertListEqual(_A			,						_A)






"""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 	UpperCamelCase    :



				def __init__(self       :   Tuple			,						_A       :   List[Any]			,						_A       :   int=sys.maxsize)					->			int:
					__snake_case    :							Dict   	=				'bilinear'
					__snake_case    :							int   	=				max_size
					__snake_case    :							Any   	=				short_edge_length






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

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

					return img_augs







class 	UpperCamelCase    :



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



				def       _lowercase  (self       :   Tuple			,						_A       :   str)					->			int:
					__snake_case    :							str   	=				tuple(max(_A) for s in zip(*[img.shape for img in images]))
					__snake_case    :							int   	=				[im.shape[-2:] for im in images]
					__snake_case    :							int   	=				[
					    nn.functional.pad(
					        _A			,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,						value=self.pad_value			,						)
					    for size, im in zip(_A			,						_A)
					]

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






				def __call__(self       :   Optional[Any]			,						_A       :   Dict			,						_A       :   Union[str, Any]=False)					->			Optional[int]:
					with torch.no_grad():
						if not isinstance(_A			,						_A):
							__snake_case    :							str   	=				[images]
						if single_image:
							assert len(_A) == 1
						for i in range(len(_A)):
							if isinstance(images[i]			,						torch.Tensor):
								images.insert(_A			,						images.pop(_A).to(self.device).float())
							elif not isinstance(images[i]			,						torch.Tensor):
								images.insert(
								    _A			,						torch.as_tensor(img_tensorize(images.pop(_A)			,						input_format=self.input_format))
								    .to(self.device)
								    .float()			,						)
            # resize smallest edge
						__snake_case    :							Any   	=				torch.tensor([im.shape[:2] for im in images])
						__snake_case    :							Tuple   	=				self.aug(_A)
						# 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    :							List[str]   	=				[self.normalizer(_A) for x in images]
						# now pad them to do the following operations
						__snake_case	,       __snake_case    :							List[Any]   	=				self.pad(_A)
						# Normalize

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







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       Any						)							->    int:

	'''simple docstring'''





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

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple[int, int]						)							->    Union[str, Any]:

	'''simple docstring'''





	assert torch.isfinite(UpperCAmelCase_						).all(), "Box tensor contains infinite or NaN!"
	__snake_case	,       __snake_case    :							Optional[int]   	=				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_						)





