
"""simple docstring"""







import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


lowerCamelCase__   					=		logging.get_logger(__name__)

lowerCamelCase__   					=		{
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

lowerCamelCase__   					=		{
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

lowerCamelCase__   					=		{
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						List[str]									=      set()
	__lowerCAmelCase			:						Any									=      word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)		)
		__lowerCAmelCase			:						int									=      char

	__lowerCAmelCase			:						List[str]									=      set(_snake_case		)
	return pairs




class  A__       (	_lowerCamelCase):
		A_						:       Union[str, Any]				   =	VOCAB_FILES_NAMES
		A_						:       Optional[Any]				   =	PRETRAINED_VOCAB_FILES_MAP
		A_						:       str				   =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE="<s>"       ,		_SCREAMING_SNAKE_CASE="</s>"       ,		_SCREAMING_SNAKE_CASE="</s>"       ,		_SCREAMING_SNAKE_CASE="<s>"       ,		_SCREAMING_SNAKE_CASE="<unk>"       ,		_SCREAMING_SNAKE_CASE="<pad>"       ,		_SCREAMING_SNAKE_CASE="<mask>"       ,		**_SCREAMING_SNAKE_CASE       ,		):
			super().__init__(
			    bos_token=_a       ,		eos_token=_a       ,		unk_token=_a       ,		sep_token=_a       ,		cls_token=_a       ,		pad_token=_a       ,		mask_token=_a       ,		**_a       ,		)

			__lowerCAmelCase			:						Dict									=      vocab_file
			__lowerCAmelCase			:						Tuple									=      merges_file

			__lowerCAmelCase			:						List[Any]									=      {}
			__lowerCAmelCase			:						List[Any]									=      0
			__lowerCAmelCase			:						Tuple									=      1
			__lowerCAmelCase			:						int									=      2
			__lowerCAmelCase			:						Union[str, Any]									=      3

			self.add_from_file(_a )

			__lowerCAmelCase			:						Optional[int]									=      {v: k for k, v in self.encoder.items()}

			with open(_a       ,		encoding='utf-8' ) as merges_handle:
				__lowerCAmelCase			:						List[str]									=      merges_handle.read().split('\n' )[:-1]
			__lowerCAmelCase			:						Union[str, Any]									=      [tuple(merge.split()[:-1] ) for merge in merges]
			__lowerCAmelCase			:						Union[str, Any]									=      dict(zip(_a       ,		range(len(_a ) ) ) )
			__lowerCAmelCase			:						Optional[int]									=      {}

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):

			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__lowerCAmelCase			:						Optional[Any]									=      [self.cls_token_id]
			__lowerCAmelCase			:						Union[str, Any]									=      [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = 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 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			__lowerCAmelCase			:						Optional[Any]									=      [self.sep_token_id]
			__lowerCAmelCase			:						Tuple									=      [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]

		@property
		def 							__lowerCamelCase							(      self ):
			return len(self.encoder )

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

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if token in self.cache:
				return self.cache[token]
			__lowerCAmelCase			:						List[Any]									=      tuple(_a )
			__lowerCAmelCase			:						List[Any]									=      tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
			__lowerCAmelCase			:						Any									=      get_pairs(_a )

			if not pairs:
				return token

			while True:
				__lowerCAmelCase			:						str									=      min(_a       ,		key=lambda _SCREAMING_SNAKE_CASE : self.bpe_ranks.get(_a       ,		float('inf' ) ) )
				if bigram not in self.bpe_ranks:
					break
				__lowerCAmelCase			:						List[str]									=      bigram
				__lowerCAmelCase			:						List[str]									=      []
				__lowerCAmelCase			:						List[str]									=      0
				while i < len(_a ):
					try:
						__lowerCAmelCase			:						Any									=      word.index(_a       ,		_a )
					except ValueError:
						new_word.extend(word[i:] )
						break
					else:
						new_word.extend(word[i:j] )
						__lowerCAmelCase			:						Tuple									=      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
				__lowerCAmelCase			:						Union[str, Any]									=      tuple(_a )
				__lowerCAmelCase			:						Optional[int]									=      new_word
				if len(_a ) == 1:
					break
				else:
					__lowerCAmelCase			:						List[Any]									=      get_pairs(_a )
			__lowerCAmelCase			:						Optional[int]									=      "@@ ".join(_a )
			__lowerCAmelCase			:						Tuple									=      word[:-4]
			__lowerCAmelCase			:						str									=      word
			return word

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[Any]									=      []

			__lowerCAmelCase			:						Dict									=      re.findall(R'\S+\n?'       ,		_a )

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

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			return self.encoder.get(_a       ,		self.encoder.get(self.unk_token ) )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			return self.decoder.get(_a       ,		self.unk_token )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Tuple									=      " ".join(_a ).replace('@@ '       ,		'' ).strip()
			return out_string

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			if not os.path.isdir(_a ):
				logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
				return
			__lowerCAmelCase			:						Optional[int]									=      os.path.join(
			    _a       ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
			__lowerCAmelCase			:						Union[str, Any]									=      os.path.join(
			    _a       ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

			if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
				copyfile(self.vocab_file       ,		_a )

			if os.path.abspath(self.merges_file ) != os.path.abspath(_a ):
				copyfile(self.merges_file       ,		_a )

			return out_vocab_file, out_merge_file







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if isinstance(_a       ,		_a ):
				try:
					with open(_a       ,		'r'       ,		encoding='utf-8' ) as fd:
						self.add_from_file(_a )
				except FileNotFoundError as fnfe:
					raise fnfe
				except UnicodeError:
					raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset" )
				return

			__lowerCAmelCase			:						List[Any]									=      f.readlines()
			for lineTmp in lines:
				__lowerCAmelCase			:						Optional[Any]									=      lineTmp.strip()
				__lowerCAmelCase			:						Union[str, Any]									=      line.rfind(' ' )
				if idx == -1:
					raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\'' )
				__lowerCAmelCase			:						Optional[int]									=      line[:idx]
				__lowerCAmelCase			:						Dict									=      len(self.encoder )

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
