



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


snake_case     :    List[str]						=    logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case     :    Union[str, Any]						=    {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

snake_case     :    Optional[int]						=    {
    'RUCAIBox/mvp': 1_0_2_4,
}
@lru_cache()
def 						snake_case__							(       )		->		Union[str, Any]:







						"""simple docstring"""

						A__						:				Optional[Any]         =			(
						    list(range(ord("!"							)				,  ord("~"							) + 1							)							) + list(range(ord("¡"							)				,  ord("¬"							) + 1							)							) + list(range(ord("®"							)				,  ord("ÿ"							) + 1							)							)
						)
						A__						:				Union[str, Any]         =			bs[:]
						A__						:				Dict         =			0
						for b in range(2**8							):
												if b not in bs:
																		bs.append(__lowercase							)
																		cs.append(2**8 + n							)
																		n += 1
						A__						:				List[str]         =			[chr(__lowercase							) for n in cs]
						return dict(zip(__lowercase				,  __lowercase							)							)
def 						snake_case__							(       __lowercase							)		->		Optional[Any]:







						"""simple docstring"""

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




class 				lowerCAmelCase__	(						UpperCamelCase    ):
					__A  :     List[str]     =    VOCAB_FILES_NAMES
					__A  :     int     =    PRETRAINED_VOCAB_FILES_MAP
					__A  :     Tuple     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A  :     Union[str, Any]     =    ['input_ids', 'attention_mask']







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

											# Mask token behave like a normal word, i.e. include the space before it
											A__						:				Union[str, Any]         =			AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A) if isinstance(_A      ,		_A) else mask_token

											super().__init__(
											    errors=_A      ,		bos_token=_A      ,		eos_token=_A      ,		unk_token=_A      ,		sep_token=_A      ,		cls_token=_A      ,		pad_token=_A      ,		mask_token=_A      ,		add_prefix_space=_A      ,		**_A      ,		)
											with open(_A      ,		encoding="utf-8") as vocab_handle:
																	A__						:				List[Any]         =			json.load(_A)
											A__						:				int         =			{v: k for k, v in self.encoder.items()}
											A__						:				Optional[int]         =			errors  # how to handle errors in decoding
											A__						:				List[str]         =			bytes_to_unicode()
											A__						:				Dict         =			{v: k for k, v in self.byte_encoder.items()}
											with open(_A      ,		encoding="utf-8") as merges_handle:
																	A__						:				Union[str, Any]         =			merges_handle.read().split("\n")[1:-1]
											A__						:				Tuple         =			[tuple(merge.split()) for merge in bpe_merges]
											A__						:				Union[str, Any]         =			dict(zip(_A      ,		range(len(_A))))
											A__						:				Optional[int]         =			{}
											A__						:				List[Any]         =			add_prefix_space

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







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







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







					def 			_lowercase      (							self   :  Any      ,		_A   :  Optional[Any]):
											if token in self.cache:
																	return self.cache[token]
											A__						:				Tuple         =			tuple(_A)
											A__						:				Tuple         =			get_pairs(_A)

											if not pairs:
																	return token

											while True:
																	A__						:				str         =			min(_A      ,		key=lambda _A: self.bpe_ranks.get(_A      ,		float("inf")))
																	if bigram not in self.bpe_ranks:
																							break
																	A__     ,							A__						:				List[Any]         =			bigram
																	A__						:				List[str]         =			[]
																	A__						:				Dict         =			0
																	while i < len(_A):
																							try:
																													A__						:				int         =			word.index(_A      ,		_A)
																							except ValueError:
																													new_word.extend(word[i:])
																													break
																							else:
																													new_word.extend(word[i:j])
																													A__						:				str         =			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
																	A__						:				List[Any]         =			tuple(_A)
																	A__						:				Optional[Any]         =			new_word
																	if len(_A) == 1:
																							break
																	else:
																							A__						:				Tuple         =			get_pairs(_A)
											A__						:				str         =			" ".join(_A)
											A__						:				Tuple         =			word
											return word







					def 			_lowercase      (							self   :  List[Any]      ,		_A   :  Optional[int]):
											A__						:				List[str]         =			[]
											for token in re.findall(self.pat      ,		_A):
																	A__						:				List[str]         =			"".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(_A).split(" "))
											return bpe_tokens







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  int):
											return self.encoder.get(_A      ,		self.encoder.get(self.unk_token))







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







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Tuple):
											A__						:				Optional[Any]         =			"".join(_A)
											A__						:				int         =			bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"      ,		errors=self.errors)
											return text







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

											A__						:				Optional[Any]         =			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!")
																													A__						:				Union[str, Any]         =			token_index
																							writer.write(" ".join(_A) + "\n")
																							index += 1

											return vocab_file, merge_file







					def 			_lowercase      (							self   :  int      ,		_A   :  List[int]      ,		_A   :  Optional[List[int]] = None):
											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											A__						:				List[str]         =			[self.cls_token_id]
											A__						:				List[Any]         =			[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  List[int]      ,		_A   :  Optional[List[int]] = None      ,		_A   :  bool = False):
											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_A      ,		token_ids_a=_A      ,		already_has_special_tokens=_A)

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







					def 			_lowercase      (							self   :  List[str]      ,		_A   :  List[int]      ,		_A   :  Optional[List[int]] = None):
											A__						:				str         =			[self.sep_token_id]
											A__						:				Dict         =			[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 			_lowercase      (							self   :  Tuple      ,		_A   :  Any      ,		_A   :  List[Any]=False      ,		**_A   :  str):
											A__						:				Any         =			kwargs.pop("add_prefix_space"      ,		self.add_prefix_space)
											if (is_split_into_words or add_prefix_space) and (len(_A) > 0 and not text[0].isspace()):
																	A__						:				Optional[Any]         =			" " + text
											return (text, kwargs)




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Any     =    BartTokenizer
					__A  :     str     =    BartTokenizerFast
					__A  :     Optional[int]     =    True
					__A  :     Optional[int]     =    filter_roberta_detectors







					def 			_lowercase      (							self   :  Dict):
											super().setUp()
											A__						:				Union[str, Any]         =			[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											A__						:				List[str]         =			dict(zip(_A      ,		range(len(_A))))
											A__						:				List[str]         =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											A__						:				str         =			{"unk_token": "<unk>"}

											A__						:				Dict         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"])
											A__						:				Optional[Any]         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["merges_file"])
											with open(self.vocab_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write(json.dumps(_A) + "\n")
											with open(self.merges_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write("\n".join(_A))







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  str):
											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  List[Any]):
											kwargs.update(self.special_tokens_map)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Union[str, Any]):
											return "lower newer", "lower newer"







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizer.from_pretrained("facebook/bart-large")







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizerFast.from_pretrained("facebook/bart-large")







					@require_torch
					def 			_lowercase      (							self   :  Dict):
											A__						:				Optional[int]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											A__						:				List[Any]         =			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		max_length=len(_A)      ,		padding=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)

																	self.assertEqual((2, 9)      ,		batch.input_ids.shape)
																	self.assertEqual((2, 9)      ,		batch.attention_mask.shape)
																	A__						:				Dict         =			batch.input_ids.tolist()[0]
																	self.assertListEqual(_A      ,		_A)
																	# Test that special tokens are reset







					@require_torch
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Optional[Any]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		padding=_A      ,		return_tensors="pt")
																	# check if input_ids are returned and no labels
																	self.assertIn("input_ids"      ,		_A)
																	self.assertIn("attention_mask"      ,		_A)
																	self.assertNotIn("labels"      ,		_A)
																	self.assertNotIn("decoder_attention_mask"      ,		_A)







					@require_torch
					def 			_lowercase      (							self   :  int):
											A__						:				Optional[Any]         =			[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				int         =			tokenizer(text_target=_A      ,		max_length=32      ,		padding="max_length"      ,		return_tensors="pt")
																	self.assertEqual(32      ,		targets["input_ids"].shape[1])







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				List[str]         =			tokenizer(
																	    ["I am a small frog" * 1024, "I am a small frog"]      ,		padding=_A      ,		truncation=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)
																	self.assertEqual(batch.input_ids.shape      ,		(2, 1024))







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											A__						:				int         =			["A long paragraph for summarization."]
											A__						:				List[str]         =			[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Optional[Any]         =			tokenizer(_A      ,		return_tensors="pt")
																	A__						:				Union[str, Any]         =			tokenizer(text_target=_A      ,		return_tensors="pt")
																	A__						:				Tuple         =			inputs["input_ids"]
																	A__						:				str         =			targets["input_ids"]
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







					def 			_lowercase      (							self   :  Optional[int]):
											pass







					def 			_lowercase      (							self   :  Any):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																							A__						:				Union[str, Any]         =			self.rust_tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				int         =			self.tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				Union[str, Any]         =			"A, <mask> AllenNLP sentence."
																							A__						:				Dict         =			tokenizer_r.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)
																							A__						:				Optional[Any]         =			tokenizer_p.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)

																							# token_type_ids should put 0 everywhere
																							self.assertEqual(sum(tokens_r["token_type_ids"])      ,		sum(tokens_p["token_type_ids"]))

																							# attention_mask should put 1 everywhere, so sum over length should be 1
																							self.assertEqual(
																							    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])      ,		sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])      ,		)

																							A__						:				Optional[int]         =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																							A__						:				Dict         =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

																							# Rust correctly handles the space before the mask while python doesnt
																							self.assertSequenceEqual(tokens_p["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																							self.assertSequenceEqual(tokens_r["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])