
"""simple docstring"""




import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard



@pytest.mark.parametrize(
    "compression_format, is_archive"       ,    [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]       ,    )
def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       int       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[Any] 							=						{
         "7z": (seven_zip_file, SevenZipExtractor),
         "bz2": (bza_file, BzipaExtractor),
         "gzip": (gz_file, GzipExtractor),
         "lz4": (lza_file, LzaExtractor),
         "tar": (tar_file, TarExtractor),
         "xz": (xz_file, XzExtractor),
         "zip": (zip_file, ZipExtractor),
         "zstd": (zstd_file, ZstdExtractor),
     }
     UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						input_paths_and_base_extractors[compression_format]
     if input_path is None:
          UpperCAmelCase__							: int 							=						F"for '{compression_format}' compression_format, "
          if compression_format == "7z":
               reason += require_pyazr.kwargs["reason"]
          elif compression_format == "lz4":
               reason += require_lza.kwargs["reason"]
          elif compression_format == "zstd":
               reason += require_zstandard.kwargs["reason"]
          pytest.skip(lowerCAmelCase		)
     assert base_extractor.is_extractable(lowerCAmelCase		)
     UpperCAmelCase__							: int 							=						tmp_path / ("extracted" if is_archive else "extracted.txt")
     base_extractor.extract(lowerCAmelCase       ,    lowerCAmelCase		)
     if is_archive:
          assert output_path.is_dir()
          for file_path in output_path.iterdir():
               assert file_path.name == text_file.name
               UpperCAmelCase__							: Dict 							=						file_path.read_text(encoding="utf-8"		)
     else:
          UpperCAmelCase__							: Any 							=						output_path.read_text(encoding="utf-8"		)
     UpperCAmelCase__							: str 							=						text_file.read_text(encoding="utf-8"		)
     assert extracted_file_content == expected_file_content



@pytest.mark.parametrize(
    "compression_format, is_archive"       ,    [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]       ,    )
def 							a__ (				lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       str       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: List[str] 							=						{
         "7z": seven_zip_file,
         "bz2": bza_file,
         "gzip": gz_file,
         "lz4": lza_file,
         "tar": tar_file,
         "xz": xz_file,
         "zip": zip_file,
         "zstd": zstd_file,
     }
     UpperCAmelCase__							: Any 							=						input_paths[compression_format]
     if input_path is None:
          UpperCAmelCase__							: List[Any] 							=						F"for '{compression_format}' compression_format, "
          if compression_format == "7z":
               reason += require_pyazr.kwargs["reason"]
          elif compression_format == "lz4":
               reason += require_lza.kwargs["reason"]
          elif compression_format == "zstd":
               reason += require_zstandard.kwargs["reason"]
          pytest.skip(lowerCAmelCase		)
     UpperCAmelCase__							: List[Any] 							=						Extractor.infer_extractor_format(lowerCAmelCase		)
     assert extractor_format is not None
     UpperCAmelCase__							: Any 							=						tmp_path / ("extracted" if is_archive else "extracted.txt")
     Extractor.extract(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
     if is_archive:
          assert output_path.is_dir()
          for file_path in output_path.iterdir():
               assert file_path.name == text_file.name
               UpperCAmelCase__							: Optional[Any] 							=						file_path.read_text(encoding="utf-8"		)
     else:
          UpperCAmelCase__							: Tuple 							=						output_path.read_text(encoding="utf-8"		)
     UpperCAmelCase__							: Union[str, Any] 							=						text_file.read_text(encoding="utf-8"		)
     assert extracted_file_content == expected_file_content



@pytest.fixture
def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     import tarfile

     UpperCAmelCase__							: str 							=						tmp_path / "data_dot_dot"
     directory.mkdir()
     UpperCAmelCase__							: int 							=						directory / "tar_file_with_dot_dot.tar"
     with tarfile.TarFile(lowerCAmelCase       ,    "w"		) as f:
          f.add(lowerCAmelCase       ,    arcname=os.path.join(".."       ,    text_file.name		)		)
     return path



@pytest.fixture
def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     import tarfile

     UpperCAmelCase__							: int 							=						tmp_path / "data_sym_link"
     directory.mkdir()
     UpperCAmelCase__							: Dict 							=						directory / "tar_file_with_sym_link.tar"
     os.symlink(".."       ,    directory / "subdir"       ,    target_is_directory=lowerCAmelCase		)
     with tarfile.TarFile(lowerCAmelCase       ,    "w"		) as f:
          f.add(str(directory / "subdir"		)       ,    arcname="subdir"		)  # str required by os.readlink on Windows and Python < 3.8
     return path



@pytest.mark.parametrize(
    "insecure_tar_file, error_log"       ,    [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")]       ,    )
def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						{
         "tar_file_with_dot_dot": tar_file_with_dot_dot,
         "tar_file_with_sym_link": tar_file_with_sym_link,
     }
     UpperCAmelCase__							: Optional[int] 							=						insecure_tar_files[insecure_tar_file]
     UpperCAmelCase__							: Tuple 							=						tmp_path / "extracted"
     TarExtractor.extract(lowerCAmelCase       ,    lowerCAmelCase		)
     assert caplog.text
     for record in caplog.records:
          assert record.levelname == "ERROR"
          assert error_log in record.msg



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     # We should have less false positives than zipfile.is_zipfile
     # We do that by checking only the magic number
     UpperCAmelCase__							: Optional[int] 							=						tmpdir / "not_a_zip_file"
     # From: https://github.com/python/cpython/pull/5053
     UpperCAmelCase__							: Optional[Any] 							=						(
         b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
         b"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
         b"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
         b"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
     )
     with not_a_zip_file.open("wb"		) as f:
          f.write(lowerCAmelCase		)
     assert zipfile.is_zipfile(str(lowerCAmelCase		)		)  # is a false positive for `zipfile`
     assert not ZipExtractor.is_extractable(lowerCAmelCase		)  # but we're right





"""simple docstring"""




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 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     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
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						self.tokenizer.pad(
              __UpperCamelCase ,			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

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

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

          return batch



