"""simple docstring"""







import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a  :  Tuple=				logging.get_logger(__name__)

_a  :  str=				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_a  :  Optional[Any]=				{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_a  :  Optional[int]=				{
    "ctrl": 256,
}

_a  :  Any=				{
    "Pregnancy": 168_629,
    "Christianity": 7_675,
    "Explain": 106_423,
    "Fitness": 63_440,
    "Saving": 63_163,
    "Ask": 27_171,
    "Ass": 95_985,
    "Joke": 163_509,
    "Questions": 45_622,
    "Thoughts": 49_605,
    "Retail": 52_342,
    "Feminism": 164_338,
    "Writing": 11_992,
    "Atheism": 192_263,
    "Netflix": 48_616,
    "Computing": 39_639,
    "Opinion": 43_213,
    "Alone": 44_967,
    "Funny": 58_917,
    "Gaming": 40_358,
    "Human": 4_088,
    "India": 1_331,
    "Joker": 77_138,
    "Diet": 36_206,
    "Legal": 11_859,
    "Norman": 4_939,
    "Tip": 72_689,
    "Weight": 52_343,
    "Movies": 46_273,
    "Running": 23_425,
    "Science": 2_090,
    "Horror": 37_793,
    "Confession": 60_572,
    "Finance": 12_250,
    "Politics": 16_360,
    "Scary": 191_985,
    "Support": 12_654,
    "Technologies": 32_516,
    "Teenage": 66_160,
    "Event": 32_769,
    "Learned": 67_460,
    "Notion": 182_770,
    "Wikipedia": 37_583,
    "Books": 6_665,
    "Extract": 76_050,
    "Confessions": 102_701,
    "Conspiracy": 75_932,
    "Links": 63_674,
    "Narcissus": 150_425,
    "Relationship": 54_766,
    "Relationships": 134_796,
    "Reviews": 41_671,
    "News": 4_256,
    "Translation": 26_820,
    "multilingual": 128_406,
}
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							Any   	=				set()
	__snake_case    :							List[str]   	=				word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)						)
		__snake_case    :							List[str]   	=				char

	__snake_case    :							Any   	=				set(UpperCAmelCase_						)
	return pairs

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Optional[Any]					=	VOCAB_FILES_NAMES
				UpperCAmelCase       :   List[Any]					=	PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase       :   List[str]					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase       :   str					=	CONTROL_CODES



				def __init__(self       :   Optional[int]			,						_A       :   Tuple			,						_A       :   Optional[int]			,						_A       :   str="<unk>"			,						**_A       :   Union[str, Any])					->			Dict:
					super().__init__(unk_token=_A			,						**_A)

					with open(_A			,						encoding='utf-8') as vocab_handle:
						__snake_case    :							Optional[int]   	=				json.load(_A)
					__snake_case    :							Any   	=				{v: k for k, v in self.encoder.items()}
					with open(_A			,						encoding='utf-8') as merges_handle:
						__snake_case    :							Any   	=				merges_handle.read().split('\n')[1:-1]
					__snake_case    :							Dict   	=				[tuple(merge.split()) for merge in merges]
					__snake_case    :							str   	=				dict(zip(_A			,						range(len(_A))))
					__snake_case    :							List[str]   	=				{}



				@property
				def       _lowercase  (self       :   Optional[Any])					->			Optional[int]:
					return len(self.encoder)



				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					return dict(self.encoder			,						**self.added_tokens_encoder)



				def       _lowercase  (self       :   List[Any]			,						_A       :   Any)					->			str:
					if token in self.cache:
						return self.cache[token]
					__snake_case    :							Any   	=				tuple(_A)
					__snake_case    :							str   	=				tuple(list(word[:-1]) + [word[-1] + '</w>'])
					__snake_case    :							Tuple   	=				get_pairs(_A)

					if not pairs:
						return token

					while True:
						__snake_case    :							Optional[int]   	=				min(_A			,						key=lambda _A: self.bpe_ranks.get(_A			,						float('inf')))
						if bigram not in self.bpe_ranks:
							break
						__snake_case    :							List[str]   	=				bigram
						__snake_case    :							Optional[Any]   	=				[]
						__snake_case    :							Tuple   	=				0
						while i < len(_A):
							try:
								__snake_case    :							Optional[Any]   	=				word.index(_A			,						_A)
							except ValueError:
								new_word.extend(word[i:])
								break
							else:
								new_word.extend(word[i:j])
								__snake_case    :							Any   	=				j

							if word[i] == first and i < len(_A) - 1 and word[i + 1] == second:
								new_word.append(first + second)
								i += 2
							else:
								new_word.append(word[i])
								i += 1
						__snake_case    :							Optional[Any]   	=				tuple(_A)
						__snake_case    :							Any   	=				new_word
						if len(_A) == 1:
							break
						else:
							__snake_case    :							Union[str, Any]   	=				get_pairs(_A)
					__snake_case    :							Optional[Any]   	=				'@@ '.join(_A)
					__snake_case    :							Dict   	=				word[:-4]
					__snake_case    :							Union[str, Any]   	=				word
					return word



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

					__snake_case    :							int   	=				re.findall(r'\S+\n?'			,						_A)

					for token in words:
						split_tokens.extend(list(self.bpe(_A).split(' ')))
					return split_tokens



				def       _lowercase  (self       :   Any			,						_A       :   int)					->			List[str]:
					return self.encoder.get(_A			,						self.encoder.get(self.unk_token))



				def       _lowercase  (self       :   List[str]			,						_A       :   List[str])					->			List[Any]:
					return self.decoder.get(_A			,						self.unk_token)



				def       _lowercase  (self       :   List[str]			,						_A       :   int)					->			Optional[Any]:
					__snake_case    :							List[Any]   	=				' '.join(_A).replace('@@ '			,						'').strip()
					return out_string






				def       _lowercase  (self       :   Tuple			,						_A       :   str			,						_A       :   Optional[str] = None)					->			Tuple[str]:
					if not os.path.isdir(_A):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
					__snake_case    :							Optional[Any]   	=				os.path.join(
					    _A			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
					__snake_case    :							List[str]   	=				os.path.join(
					    _A			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

					with open(_A			,						'w'			,						encoding='utf-8') as f:
						f.write(json.dumps(self.encoder			,						indent=2			,						sort_keys=_A			,						ensure_ascii=_A) + '\n')

					__snake_case    :							List[str]   	=				0
					with open(_A			,						'w'			,						encoding='utf-8') as writer:
						writer.write('#version: 0.2\n')
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,						key=lambda _A: kv[1]):
							if index != token_index:
								logger.warning(
								    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
								    ' Please check that the tokenizer is not corrupted!')
								__snake_case    :							List[str]   	=				token_index
							writer.write(' '.join(_A) + '\n')
							index += 1

					return vocab_file, merge_file

				# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
				#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
				#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
				#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
				#     return ''.join(tokens_generated_so_far)



"""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_						)





