


'''simple docstring'''



import os
import re
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


__lowercase	:						Dict       =	logging.get_logger(__name__)

__lowercase	:						Any       =	{'vocab_file': 'spiece.model'}

__lowercase	:						List[Any]       =	{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

__lowercase	:						Tuple       =	{
    'google/bigbird-roberta-base': 40_96,
    'google/bigbird-roberta-large': 40_96,
    'google/bigbird-base-trivia-itc': 40_96,
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    VOCAB_FILES_NAMES
	A_				=    PRETRAINED_VOCAB_FILES_MAP
	A_				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_				=    ["input_ids", "attention_mask"]
	A_				=    []




	def __init__(   self						,				__a						,				__a="<unk>"						,				__a="<s>"						,				__a="</s>"						,				__a="<pad>"						,				__a="[SEP]"						,				__a="[MASK]"						,				__a="[CLS]"						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						__a			:       int					    =  AddedToken(__a						,				lstrip=__a						,				rstrip=__a							) if isinstance(__a						,				__a							) else bos_token
						__a			:       Any					    =  AddedToken(__a						,				lstrip=__a						,				rstrip=__a							) if isinstance(__a						,				__a							) else eos_token
						__a			:       Dict					    =  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
						__a			:       Optional[int]					    =  AddedToken(__a						,				lstrip=__a						,				rstrip=__a							) if isinstance(__a						,				__a							) else cls_token
						__a			:       List[Any]					    =  AddedToken(__a						,				lstrip=__a						,				rstrip=__a							) if isinstance(__a						,				__a							) else sep_token

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

						__a			:       List[Any]					    =  {} if sp_model_kwargs is None else sp_model_kwargs

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

						__a			:       Tuple					    =  vocab_file

						__a			:       Union[str, Any]					    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(__a							)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.sp_model.get_piece_size()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  {self.convert_ids_to_tokens(__a							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab




	def __getstate__(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.__dict__.copy()
						__a			:       Tuple					    =  None
						return state




	def __setstate__(   self						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  d

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

						__a			:       str					    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(self.vocab_file							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return self.sp_model.encode(__a						,				out_type=__a							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return self.sp_model.piece_to_id(__a							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  self.sp_model.IdToPiece(__a							)
						return token




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       List[str]					    =  []
						__a			:       Tuple					    =  ''
						__a			:       Any					    =  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
																__a			:       Any					    =  True
																__a			:       Optional[int]					    =  []
											else:
																current_sub_tokens.append(__a							)
																__a			:       Optional[int]					    =  False
						out_string += self.sp_model.decode(__a							)
						return out_string.strip()




	def       __UpperCAmelCase							(   self						,				__a						,				__a = False						,				__a = None						,				__a = True						,				**__a						,				):

						'''simple docstring'''





						__a			:       Tuple					    =  kwargs.pop('use_source_tokenizer'						,				__a							)

						__a			:       Optional[int]					    =  self.convert_ids_to_tokens(__a						,				skip_special_tokens=__a							)

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						__a			:       int					    =  []
						__a			:       Union[str, Any]					    =  []
						for token in filtered_tokens:
											if skip_special_tokens and token in self.all_special_ids:
																continue
											if token in self.added_tokens_encoder:
																if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(__a							)							)
																					__a			:       Union[str, Any]					    =  []
																sub_texts.append(__a							)
											else:
																current_sub_text.append(__a							)
						if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(__a							)							)

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
											__a			:       List[str]					    =  re.sub(r' (\[(MASK|SEP)\])'						,				r'\1'						,				' '.join(__a							)							)
						else:
											__a			:       str					    =  ''.join(__a							)

						__a			:       str					    =  (
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
											__a			:       Any					    =  self.clean_up_tokenization(__a							)
											return clean_text
						else:
											return text




	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						if not os.path.isdir(__a							):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
											return
						__a			:       List[Any]					    =  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:
																__a			:       Tuple					    =  self.sp_model.serialized_model_proto()
																fi.write(__a							)

						return (out_vocab_file,)




	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						__a			:       Optional[int]					    =  [self.cls_token_id]
						__a			:       Dict					    =  [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep




	def       __UpperCAmelCase							(   self						,				__a						,				__a = None						,				__a = False							):

						'''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] + ([0] * len(__a							)) + [1]





	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						__a			:       Any					    =  [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							) * [0] + len(token_ids_a + sep							) * [1]







'''simple docstring'''



import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					__a			:       Optional[Any]					    =  tmp_path / 'file.csv'
					__a			:       Union[str, Any]					    =  textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       str					    =  tmp_path / 'malformed_file.csv'
					__a			:       int					    =  textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20,\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  tmp_path / 'csv_with_image.csv'
					__a			:       Dict					    =  textwrap.dedent(
					    F"""\
        image
        {image_file}
        """   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Union[str, Any]					    =  tmp_path / 'csv_with_label.csv'
					__a			:       Any					    =  textwrap.dedent(
					    '\\n        label\n        good\n        bad\n        good\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Dict					    =  tmp_path / 'csv_with_int_list.csv'
					__a			:       Tuple					    =  textwrap.dedent(
					    '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       int					    =  Csv()
					__a			:       str					    =  csv._generate_tables([[csv_file, malformed_csv_file]]   )
					with pytest.raises(_SCREAMING_SNAKE_CASE						,      match='Error tokenizing data'   ):
										for _ in generator:
															pass
					assert any(
					    record.levelname == 'ERROR'
					    and 'Failed to read file' in record.message
					    and os.path.basename(_SCREAMING_SNAKE_CASE   ) in record.message
					    for record in caplog.records   )






@require_pil
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					with open(_SCREAMING_SNAKE_CASE						,      encoding='utf-8'   ) as f:
										__a			:       Tuple					    =  f.read().splitlines()[1]
					__a			:       Tuple					    =  Csv(encoding='utf-8'						,      features=Features({'image': Image()}   )   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_image]]   )
					__a			:       int					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('image'   ).type == Image()()
					__a			:       Any					    =  pa_table.to_pydict()['image']
					assert generated_content == [{"path": image_file, "bytes": None}]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					with open(_SCREAMING_SNAKE_CASE						,      encoding='utf-8'   ) as f:
										__a			:       Tuple					    =  f.read().splitlines()[1:]
					__a			:       Optional[int]					    =  Csv(encoding='utf-8'						,      features=Features({'label': ClassLabel(names=['good', 'bad']   )}   )   )
					__a			:       List[str]					    =  csv._generate_tables([[csv_file_with_label]]   )
					__a			:       Dict					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('label'   ).type == ClassLabel(names=['good', 'bad']   )()
					__a			:       int					    =  pa_table.to_pydict()['label']
					assert generated_content == [ClassLabel(names=['good', 'bad']   ).straint(_SCREAMING_SNAKE_CASE   ) for label in labels]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  Csv(encoding='utf-8'						,      sep=','						,      converters={'int_list': lambda _SCREAMING_SNAKE_CASE   : [int(_SCREAMING_SNAKE_CASE   ) for i in x.split()]}   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_int_list]]   )
					__a			:       Any					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa.types.is_list(pa_table.schema.field('int_list'   ).type   )
					__a			:       Tuple					    =  pa_table.to_pydict()['int_list']
					assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



