

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	'''▁'''

lowerCAmelCase_       	=	{'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase_       	=	{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

lowerCAmelCase_       	=	{
    '''facebook/xglm-564M''': 20_48,
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Tuple									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   str									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   Any									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				["input_ids", "attention_mask"]







			def __init__(     self :				Dict							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				int="<s>"							,   _UpperCamelCase :				int="</s>"							,   _UpperCamelCase :				Optional[int]="</s>"							,   _UpperCamelCase :				Tuple="<s>"							,   _UpperCamelCase :				Dict="<unk>"							,   _UpperCamelCase :				List[str]="<pad>"							,   _UpperCamelCase :				Optional[Dict[str, Any]] = None							,   **_UpperCamelCase :				Optional[Any]							,   )      ->None:
									snake_case_   =	{} if sp_model_kwargs is None else sp_model_kwargs

									# Compatibility with the original tokenizer
									snake_case_   =	7
									snake_case_   =	[f'''<madeupword{i}>''' for i in range(self.num_madeup_words							)]

									snake_case_   =	kwargs.get('''additional_special_tokens'''							,   []							)
									kwargs["additional_special_tokens"] += [
									    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    bos_token=_UpperCamelCase							,   eos_token=_UpperCamelCase							,   unk_token=_UpperCamelCase							,   sep_token=_UpperCamelCase							,   cls_token=_UpperCamelCase							,   pad_token=_UpperCamelCase							,   sp_model_kwargs=self.sp_model_kwargs							,   **_UpperCamelCase							,   )

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(str(_UpperCamelCase							)							)
									snake_case_   =	vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									snake_case_   =	1

									# Mimic fairseq token-to-id alignment for the first 4 token
									snake_case_   =	{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									snake_case_   =	len(self.sp_model							)
									snake_case_   =	{f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
									self.fairseq_tokens_to_ids.update(_UpperCamelCase							)

									snake_case_   =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def __getstate__(     self :				Union[str, Any]							)      ->str:
									snake_case_   =	self.__dict__.copy()
									snake_case_   =	None
									snake_case_   =	self.sp_model.serialized_model_proto()
									return state







			def __setstate__(     self :				Any							,   _UpperCamelCase :				Union[str, Any]							)      ->Optional[Any]:
									snake_case_   =	d

									# for backward compatibility
									if not hasattr(self							,   '''sp_model_kwargs'''							):
															snake_case_   =	{}

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:

									if token_ids_a is None:
															return [self.sep_token_id] + token_ids_a
									snake_case_   =	[self.sep_token_id]
									return sep + token_ids_a + sep + sep + token_ids_a







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							,   _UpperCamelCase :				bool = False							)      ->List[int]:

									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCamelCase							,   token_ids_a=_UpperCamelCase							,   already_has_special_tokens=_UpperCamelCase							)

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







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]

									if token_ids_a is None:
															return len(sep + token_ids_a							) * [0]
									return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]







			@property
			def 				snake_case__(     self :				int							)      ->Union[str, Any]:
									return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words







			def 				snake_case__(     self :				Any							)      ->int:
									snake_case_   =	{self.convert_ids_to_tokens(_UpperCamelCase							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				str							)      ->List[str]:
									return self.sp_model.encode(_UpperCamelCase							,   out_type=_UpperCamelCase							)







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[Any]							)      ->Optional[int]:
									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									snake_case_   =	self.sp_model.PieceToId(_UpperCamelCase							)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[str]							)      ->int:
									if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				int							)      ->str:
									snake_case_   =	''''''.join(_UpperCamelCase							).replace(_UpperCamelCase							,   ''' '''							).strip()
									return out_string







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[str] = None							)      ->Tuple[str]:
									if not os.path.isdir(_UpperCamelCase							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_   =	os.path.join(
									    _UpperCamelCase							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

									if os.path.abspath(self.vocab_file							) != os.path.abspath(_UpperCamelCase							) and os.path.isfile(self.vocab_file							):
															copyfile(self.vocab_file							,   _UpperCamelCase							)
									elif not os.path.isfile(self.vocab_file							):
															with open(_UpperCamelCase							,   '''wb'''							) as fi:
																					snake_case_   =	self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase							)

									return (out_vocab_file,)



"""simple docstring"""







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       LayoutLMTokenizer
    UpperCAmelCase       :							int	=       LayoutLMTokenizerFast
    UpperCAmelCase       :							Union[str, Any]	=       True
    UpperCAmelCase       :							Optional[Any]	=       True






    def 	__snake_case							(					self				:    Optional[int]):
      super().setUp()

      a						:		Tuple      							=			[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      a						:		Optional[Any]      							=			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 	__snake_case							(					self				:    Optional[int]				, **__UpperCAmelCase				:    Tuple):
      return LayoutLMTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    str):
      a						:		Tuple      							=			"UNwant\u00E9d,running"
      a						:		Dict      							=			"unwanted, running"
      return input_text, output_text






    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			self.tokenizer_class(self.vocab_file)

      a						:		str      							=			tokenizer.tokenize("UNwant\u00E9d,running")
      self.assertListEqual(__UpperCAmelCase				, ["un", "##want", "##ed", ",", "runn", "##ing"])
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase)				, [7, 4, 5, 10, 8, 9])






    def 	__snake_case							(					self				:    Dict):
      pass






