





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_ (       __a		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =tmp_path / "file.csv"
  lowerCamelCase__:      Optional[int]			  =textwrap.dedent(
      "\\n        header1,header2\n        1,2\n        10,20\n        "		)
  with open(__a	,    "w"		) as f:
    f.write(__a		)
  return str(__a		)





@pytest.fixture
def      lowerCAmelCase_ (       __a		)     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      str			  =tmp_path / "malformed_file.csv"
  lowerCamelCase__:      List[Any]			  =textwrap.dedent(
      "\\n        header1,header2\n        1,2\n        10,20,\n        "		)
  with open(__a	,    "w"		) as f:
    f.write(__a		)
  return str(__a		)





@pytest.fixture
def      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =tmp_path / "csv_with_image.csv"
  lowerCamelCase__:      str			  =textwrap.dedent(
      F"""\
        image
        {image_file}
        """		)
  with open(__a	,    "w"		) as f:
    f.write(__a		)
  return str(__a		)





@pytest.fixture
def      lowerCAmelCase_ (       __a		)     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =tmp_path / "csv_with_label.csv"
  lowerCamelCase__:      Optional[Any]			  =textwrap.dedent(
      "\\n        label\n        good\n        bad\n        good\n        "		)
  with open(__a	,    "w"		) as f:
    f.write(__a		)
  return str(__a		)





@pytest.fixture
def      lowerCAmelCase_ (       __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Dict			  =tmp_path / "csv_with_int_list.csv"
  lowerCamelCase__:      str			  =textwrap.dedent(
      "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "		)
  with open(__a	,    "w"		) as f:
    f.write(__a		)
  return str(__a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		str:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =Csv()
  lowerCamelCase__:      Union[str, Any]			  =csv._generate_tables([[csv_file, malformed_csv_file]]		)
  with pytest.raises(__a	,    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(__a		) in record.message
      for record in caplog.records		)





@require_pil
def      lowerCAmelCase_ (       __a		)     ->		Dict:







  """simple docstring"""





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





def      lowerCAmelCase_ (       __a		)     ->		str:







  """simple docstring"""





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






def      lowerCAmelCase_ (       __a		)     ->		List[str]:







  """simple docstring"""





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








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_       , tokenizer_file=UpperCAmelCase_       , do_lower_case=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , tokenize_chinese_chars=UpperCAmelCase_       , strip_accents=UpperCAmelCase_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      Optional[int]			  =[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]







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

