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

import sentencepiece as spm

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


_UpperCAmelCase :			Union[str, Any]              =	logging.get_logger(__name__)

_UpperCAmelCase :			Dict              =	{"vocab_file": "sentencepiece.bpe.model"}

_UpperCAmelCase :			List[str]              =	{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

_UpperCAmelCase :			List[Any]              =	{
    "camembert-base": 512,
}

_UpperCAmelCase :			List[str]              =	"▁"


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
  __lowercase	:	Tuple     					=    VOCAB_FILES_NAMES
  __lowercase	:	int     					=    PRETRAINED_VOCAB_FILES_MAP
  __lowercase	:	Tuple     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase	:	str     					=    ["input_ids", "attention_mask"]



  def __init__(			self    ,				A_    ,				A_="<s>"    ,				A_="</s>"    ,				A_="</s>"    ,				A_="<s>"    ,				A_="<unk>"    ,				A_="<pad>"    ,				A_="<mask>"    ,				A_=["<s>NOTUSED", "</s>NOTUSED"]    ,				A_ = None    ,				**A_    ,				)  -> None:
    """simple docstring"""
    UpperCamelCase          =					AddedToken(A_    ,				lstrip=A_    ,				rstrip=A_	) if isinstance(A_    ,				A_	) else mask_token

    UpperCamelCase          =					{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=A_    ,				eos_token=A_    ,				unk_token=A_    ,				sep_token=A_    ,				cls_token=A_    ,				pad_token=A_    ,				mask_token=A_    ,				additional_special_tokens=A_    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**A_    ,				)
    UpperCamelCase          =					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
    self.sp_model.Load(str(A_	)	)
    UpperCamelCase          =					vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    UpperCamelCase          =					{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
    UpperCamelCase          =					len(self.fairseq_tokens_to_ids	)
    UpperCamelCase          =					len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
    UpperCamelCase          =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}



  def __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> List[int]:
    """simple docstring"""

    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCamelCase          =					[self.cls_token_id]
    UpperCamelCase          =					[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep



  def __UpperCamelCase			(			self    ,				A_    ,				A_ = None    ,				A_ = False	)  -> List[int]:
    """simple docstring"""
    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 __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> List[int]:
    """simple docstring"""
    UpperCamelCase          =					[self.sep_token_id]
    UpperCamelCase          =					[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 __UpperCamelCase			(			self	)  -> str:
    """simple docstring"""
    return len(self.fairseq_tokens_to_ids	) + len(self.sp_model	)



  def __UpperCamelCase			(			self	)  -> Any:
    """simple docstring"""
    UpperCamelCase          =					{self.convert_ids_to_tokens(A_	): i for i in range(self.vocab_size	)}
    vocab.update(self.added_tokens_encoder	)
    return vocab



  def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
    """simple docstring"""
    return self.sp_model.encode(A_    ,				out_type=A_	)



  def __UpperCamelCase			(			self    ,				A_	)  -> Union[str, Any]:
    """simple docstring"""
    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(A_	) == 0:
      # Convert sentence piece unk token to fairseq unk token index
      return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(A_	)



  def __UpperCamelCase			(			self    ,				A_	)  -> Optional[int]:
    """simple docstring"""
    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 __UpperCamelCase			(			self    ,				A_	)  -> int:
    """simple docstring"""
    UpperCamelCase          =					[]
    UpperCamelCase          =					''
    UpperCamelCase          =					False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        if not prev_is_special:
          out_string += " "
        out_string += self.sp_model.decode(A_	) + token
        UpperCamelCase          =					True
        UpperCamelCase          =					[]
      else:
        current_sub_tokens.append(A_	)
        UpperCamelCase          =					False
    out_string += self.sp_model.decode(A_	)
    return out_string.strip()



  def __getstate__(			self	)  -> Any:
    """simple docstring"""
    UpperCamelCase          =					self.__dict__.copy()
    UpperCamelCase          =					None
    return state



  def __setstate__(			self    ,				A_	)  -> int:
    """simple docstring"""
    UpperCamelCase          =					d

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

    UpperCamelCase          =					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
    self.sp_model.Load(self.vocab_file	)



  def __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> Tuple[str]:
    """simple docstring"""
    if not os.path.isdir(A_	):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
      return
    UpperCamelCase          =					os.path.join(
        A_    ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

    return (out_vocab_file,)


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase :			Union[str, Any]              =	get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")


@require_sentencepiece
@require_tokenizers
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    GPTSwaTokenizer
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Union[str, Any]     					=    True
		__lowercase	:	Tuple     					=    False



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCamelCase          =					GPTSwaTokenizer(A_    ,				eos_token='<unk>'    ,				bos_token='<unk>'    ,				pad_token='<unk>'	)

				tokenizer.save_pretrained(self.tmpdirname	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'This is a test'
				UpperCamelCase          =					'This is a test'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					'<s>'
				UpperCamelCase          =					1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_	)    ,				A_	)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_	)    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					list(self.get_tokenizer().get_vocab().keys()	)

				self.assertEqual(vocab_keys[0]    ,				'<unk>'	)
				self.assertEqual(vocab_keys[1]    ,				'<s>'	)
				self.assertEqual(vocab_keys[-1]    ,				'j'	)
				self.assertEqual(len(A_	)    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)

				UpperCamelCase          =					tokenizer.tokenize('This is a test'	)
				self.assertListEqual(A_    ,				['▁This', '▁is', '▁a', '▁t', 'est']	)

				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				[465, 287, 265, 631, 842]	)

				UpperCamelCase          =					tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']    ,				)
				# fmt: on

				UpperCamelCase          =					tokenizer.convert_tokens_to_ids(A_	)
				self.assertListEqual(
				    A_    ,				[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]    ,				)

				UpperCamelCase          =					tokenizer.convert_ids_to_tokens(A_	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']	)
				# fmt: on



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)
				UpperCamelCase          =					['This is a test', 'I was born in 92000, and this is falsé.']
				UpperCamelCase          =					[
				    [465, 287, 265, 631, 842],
				    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
				]

				# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
				for text, expected_ids in zip(A_    ,				A_	):
						self.assertListEqual(tokenizer.encode_fast(A_	)    ,				A_	)

				# Test that decode_fast returns the input text
				for text, token_ids in zip(A_    ,				A_	):
						self.assertEqual(tokenizer.decode_fast(A_	)    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[
				    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
				    'Hey there, how are you doing this fine day?',
				    'This is a text with a trailing spaces followed by a dot     .',
				    'Häj sväjs lillebrör! =)',
				    'Det är inget fel på Mr. Cool',
				]

				# fmt: off
				UpperCamelCase          =					{'input_ids': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
				# fmt: on
				self.tokenizer_integration_test_util(
				    expected_encoding=A_    ,				model_name='AI-Sweden/gpt-sw3-126m'    ,				sequences=A_    ,				)
