


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_								=    logging.get_logger(__name__)

lowercase_								=    {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowercase_								=    {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

lowercase_								=    {
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}


@lru_cache()
def 							SCREAMING_SNAKE_CASE					(  )    ->  int:
						_a     =			(
						    list(range(ord('!'				)							,     ord('~'				) + 1				)				) + list(range(ord('¡'				)							,     ord('¬'				) + 1				)				) + list(range(ord('®'				)							,     ord('ÿ'				) + 1				)				)
						)
						_a     =			bs[:]
						_a     =			0
						for b in range(2**8				):
												if b not in bs:
																		bs.append(_UpperCAmelCase				)
																		cs.append(2**8 + n				)
																		n += 1
						_a     =			[chr(_UpperCAmelCase				) for n in cs]
						return dict(zip(_UpperCAmelCase							,     _UpperCAmelCase				)				)


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  int:
						_a     =			set()
						_a     =			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)				)
												_a     =			char
						return pairs






class 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''
							_A  				=  VOCAB_FILES_NAMES
							_A  				=  PRETRAINED_VOCAB_FILES_MAP
							_A  				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_A  				=  ["input_ids", "attention_mask"]







							def __init__(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Any="replace"		,				SCREAMING_SNAKE_CASE_  :				Tuple="<s>"		,				SCREAMING_SNAKE_CASE_  :				List[Any]="</s>"		,				SCREAMING_SNAKE_CASE_  :				Any="</s>"		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]="<s>"		,				SCREAMING_SNAKE_CASE_  :				Tuple="<unk>"		,				SCREAMING_SNAKE_CASE_  :				str="<pad>"		,				SCREAMING_SNAKE_CASE_  :				List[str]="<mask>"		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=False		,				**SCREAMING_SNAKE_CASE_  :				str		,				):
													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else bos_token
													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else eos_token
													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else sep_token
													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else cls_token
													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else unk_token
													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else mask_token

													super().__init__(
													    errors=SCREAMING_SNAKE_CASE_		,				bos_token=SCREAMING_SNAKE_CASE_		,				eos_token=SCREAMING_SNAKE_CASE_		,				unk_token=SCREAMING_SNAKE_CASE_		,				sep_token=SCREAMING_SNAKE_CASE_		,				cls_token=SCREAMING_SNAKE_CASE_		,				pad_token=SCREAMING_SNAKE_CASE_		,				mask_token=SCREAMING_SNAKE_CASE_		,				add_prefix_space=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		,				)

													with open(SCREAMING_SNAKE_CASE_		,				encoding='utf-8'		) as vocab_handle:
																			_a     =			json.load(SCREAMING_SNAKE_CASE_		)
													_a     =			{v: k for k, v in self.encoder.items()}
													_a     =			errors  # how to handle errors in decoding
													_a     =			bytes_to_unicode()
													_a     =			{v: k for k, v in self.byte_encoder.items()}
													with open(SCREAMING_SNAKE_CASE_		,				encoding='utf-8'		) as merges_handle:
																			_a     =			merges_handle.read().split('\n'		)[1:-1]
													_a     =			[tuple(merge.split()		) for merge in bpe_merges]
													_a     =			dict(zip(SCREAMING_SNAKE_CASE_		,				range(len(SCREAMING_SNAKE_CASE_		)		)		)		)
													_a     =			{}
													_a     =			add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													_a     =			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'		)







							@property
							def 		_UpperCAmelCase	(		self  :				Optional[Any]		):
													return len(self.encoder		)







							def 		_UpperCAmelCase	(		self  :				str		):
													return dict(self.encoder		,				**self.added_tokens_encoder		)







							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Dict		):
													if token in self.cache:
																			return self.cache[token]
													_a     =			tuple(SCREAMING_SNAKE_CASE_		)
													_a     =			get_pairs(SCREAMING_SNAKE_CASE_		)

													if not pairs:
																			return token

													while True:
																			_a     =			min(SCREAMING_SNAKE_CASE_		,				key=lambda SCREAMING_SNAKE_CASE_		: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_		,				float('inf'		)		)		)
																			if bigram not in self.bpe_ranks:
																									break
																			_a ,	_a     =			bigram
																			_a     =			[]
																			_a     =			0
																			while i < len(SCREAMING_SNAKE_CASE_		):
																									try:
																															_a     =			word.index(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
																									except ValueError:
																															new_word.extend(word[i:]		)
																															break
																									else:
																															new_word.extend(word[i:j]		)
																															_a     =			j

																									if word[i] == first and i < len(SCREAMING_SNAKE_CASE_		) - 1 and word[i + 1] == second:
																															new_word.append(first + second		)
																															i += 2
																									else:
																															new_word.append(word[i]		)
																															i += 1
																			_a     =			tuple(SCREAMING_SNAKE_CASE_		)
																			_a     =			new_word
																			if len(SCREAMING_SNAKE_CASE_		) == 1:
																									break
																			else:
																									_a     =			get_pairs(SCREAMING_SNAKE_CASE_		)
													_a     =			' '.join(SCREAMING_SNAKE_CASE_		)
													_a     =			word
													return word







							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
													_a     =			[]
													for token in re.findall(self.pat		,				SCREAMING_SNAKE_CASE_		):
																			_a     =			''.join(
																			    self.byte_encoder[b] for b in token.encode('utf-8'		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_		).split(' '		)		)
													return bpe_tokens







							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Dict		):
													return self.encoder.get(SCREAMING_SNAKE_CASE_		,				self.encoder.get(self.unk_token		)		)







							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				str		):
													return self.decoder.get(SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		):
													_a     =			''.join(SCREAMING_SNAKE_CASE_		)
													_a     =			bytearray([self.byte_decoder[c] for c in text]		).decode('utf-8'		,				errors=self.errors		)
													return text







							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Optional[str] = None		):
													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']		)
													_a     =			os.path.join(
													    SCREAMING_SNAKE_CASE_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']		)

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

													_a     =			0
													with open(SCREAMING_SNAKE_CASE_		,				'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 SCREAMING_SNAKE_CASE_		: 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!'		)
																															_a     =			token_index
																									writer.write(' '.join(SCREAMING_SNAKE_CASE_		) + '\n'		)
																									index += 1

													return vocab_file, merge_file







							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):
													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													_a     =			[self.cls_token_id]
													_a     =			[self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep







							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = False		):
													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=SCREAMING_SNAKE_CASE_		,				token_ids_a=SCREAMING_SNAKE_CASE_		,				already_has_special_tokens=SCREAMING_SNAKE_CASE_		)

													if token_ids_a is None:
																			return [1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1]
													return [1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1]







							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):
													_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 + sep + token_ids_a + sep		) * [0]







							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Any=False		,				**SCREAMING_SNAKE_CASE_  :				List[Any]		):
													_a     =			kwargs.pop('add_prefix_space'		,				self.add_prefix_space		)
													if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_		) > 0 and not text[0].isspace()):
																			_a     =			' ' + text
													return (text, kwargs)






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
						_a     =			min(img.size				)

						if s < target_image_size:
												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)

						_a     =			target_image_size / s
						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
						return img


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
						_a     =			2.0 * x - 1.0
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
						_a     =			x.detach().cpu()
						if process:
												_a     =			post_process_tensor(_UpperCAmelCase				)
						_a     =			x.numpy()
						if process:
												_a     =			(255 * x).astype(np.uinta				)
						_a     =			Image.fromarray(_UpperCAmelCase				)
						if not x.mode == mode:
												_a     =			x.convert(_UpperCAmelCase				)
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
						_a     =			(x + 1.0) / 2.0
						_a     =			x.permute(1							,     2							,     0				)
						return x






def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
						_a     =			post_process_tensor(x.squeeze()				)
						return x.permute(2							,     0							,     1				).unsqueeze(0				)


