



'''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 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    Any  ,_snake_case	:    List[str]  ,_snake_case	:    Dict  ,_snake_case	:    List[Any]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    List[str]  ,_snake_case	:    Dict  ,_snake_case	:    List[str]  ,_snake_case	:    Any  ,_snake_case	:    int  ,_snake_case	:    Optional[int]  ,):





    '''simple docstring'''


    lowercase__									=    {
        "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),
    }
    lowercase__							,							lowercase__									=    input_paths_and_base_extractors[compression_format]
    if input_path is None:
        lowercase__									=    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(_snake_case							)
    assert base_extractor.is_extractable(_snake_case							)
    lowercase__									=    tmp_path / ("extracted" if is_archive else "extracted.txt")
    base_extractor.extract(_snake_case  ,_snake_case							)
    if is_archive:
        assert output_path.is_dir()
        for file_path in output_path.iterdir():
            assert file_path.name == text_file.name
            lowercase__									=    file_path.read_text(encoding="utf-8"							)
    else:
        lowercase__									=    output_path.read_text(encoding="utf-8"							)
    lowercase__									=    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 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    Dict  ,_snake_case	:    List[Any]  ,_snake_case	:    Optional[Any]  ,_snake_case	:    Tuple  ,_snake_case	:    str  ,_snake_case	:    Any  ,_snake_case	:    Dict  ,_snake_case	:    Optional[int]  ,_snake_case	:    Optional[Any]  ,_snake_case	:    List[str]  ,_snake_case	:    Optional[Any]  ,):





    '''simple docstring'''


    lowercase__									=    {
        "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,
    }
    lowercase__									=    input_paths[compression_format]
    if input_path is None:
        lowercase__									=    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(_snake_case							)
    lowercase__									=    Extractor.infer_extractor_format(_snake_case							)
    assert extractor_format is not None
    lowercase__									=    tmp_path / ("extracted" if is_archive else "extracted.txt")
    Extractor.extract(_snake_case  ,_snake_case  ,_snake_case							)
    if is_archive:
        assert output_path.is_dir()
        for file_path in output_path.iterdir():
            assert file_path.name == text_file.name
            lowercase__									=    file_path.read_text(encoding="utf-8"							)
    else:
        lowercase__									=    output_path.read_text(encoding="utf-8"							)
    lowercase__									=    text_file.read_text(encoding="utf-8"							)
    assert extracted_file_content == expected_file_content




@pytest.fixture
def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    Dict							):





    '''simple docstring'''


    import tarfile

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




@pytest.fixture
def 							lowerCamelCase	(					_snake_case	:    List[Any]							):





    '''simple docstring'''


    import tarfile

    lowercase__									=    tmp_path / "data_sym_link"
    directory.mkdir()
    lowercase__									=    directory / "tar_file_with_sym_link.tar"
    os.symlink(".."  ,directory / "subdir"  ,target_is_directory=_snake_case							)
    with tarfile.TarFile(_snake_case  ,"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 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    Optional[int]  ,_snake_case	:    List[str]  ,_snake_case	:    str  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Tuple							):





    '''simple docstring'''


    lowercase__									=    {
        "tar_file_with_dot_dot": tar_file_with_dot_dot,
        "tar_file_with_sym_link": tar_file_with_sym_link,
    }
    lowercase__									=    insecure_tar_files[insecure_tar_file]
    lowercase__									=    tmp_path / "extracted"
    TarExtractor.extract(_snake_case  ,_snake_case							)
    assert caplog.text
    for record in caplog.records:
        assert record.levelname == "ERROR"
        assert error_log in record.msg




def 							lowerCamelCase	(					_snake_case	:    List[Any]							):





    '''simple docstring'''


    lowercase__									=    tmpdir / "not_a_zip_file"
    # From: https://github.com/python/cpython/pull/5053
    lowercase__									=    (
        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(_snake_case							)
    assert zipfile.is_zipfile(str(_snake_case							)							)  # is a false positive for `zipfile`
    assert not ZipExtractor.is_extractable(_snake_case							)  # but we're right









'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE__          =  {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 					snake_case			(nn.Module ):



       def __init__(					self  ,UpperCAmelCase_   )	->				Dict:
           super().__init__()
           lowercase__									=    torchvision.models.resnetaaa(pretrained=UpperCAmelCase_   )
           lowercase__									=    list(model.children()   )[:-2]
           lowercase__									=    nn.Sequential(*UpperCAmelCase_   )
           lowercase__									=    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )







       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
           lowercase__									=    self.pool(self.model(UpperCAmelCase_   )   )
           lowercase__									=    torch.flatten(UpperCAmelCase_  ,start_dim=2   )
           lowercase__									=    out.transpose(1  ,2   ).contiguous()
           return out  # BxNx2048





class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    [json.loads(UpperCAmelCase_   ) for l in open(UpperCAmelCase_   )]
           lowercase__									=    os.path.dirname(UpperCAmelCase_   )
           lowercase__									=    tokenizer
           lowercase__									=    labels
           lowercase__									=    len(UpperCAmelCase_   )
           lowercase__									=    max_seq_length

           lowercase__									=    transforms



       def __len__(					self   )	->				Optional[Any]:
           return len(self.data   )



       def __getitem__(					self  ,UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,add_special_tokens=UpperCAmelCase_   )   )
           lowercase__							,							lowercase__							,							lowercase__									=    sentence[0], sentence[1:-1], sentence[-1]
           lowercase__									=    sentence[: self.max_seq_length]

           lowercase__									=    torch.zeros(self.n_classes   )
           lowercase__									=    1

           lowercase__									=    Image.open(os.path.join(self.data_dir  ,self.data[index]["img"]   )   ).convert("RGB"   )
           lowercase__									=    self.transforms(UpperCAmelCase_   )

           return {
               "image_start_token": start_token,
               "image_end_token": end_token,
               "sentence": sentence,
               "image": image,
               "label": label,
           }







       def 				_a   (					self   )	->				str:
           lowercase__									=    Counter()
           for row in self.data:
               label_freqs.update(row["label"]   )
           return label_freqs





def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [len(row["sentence"]							) for row in batch]
    lowercase__							,							lowercase__									=    len(_snake_case							), max(_snake_case							)

    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)
    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)

    for i_batch, (input_row, length) in enumerate(zip(_snake_case  ,_snake_case							)							):
        lowercase__									=    input_row["sentence"]
        lowercase__									=    1

    lowercase__									=    torch.stack([row["image"] for row in batch]							)
    lowercase__									=    torch.stack([row["label"] for row in batch]							)
    lowercase__									=    torch.stack([row["image_start_token"] for row in batch]							)
    lowercase__									=    torch.stack([row["image_end_token"] for row in batch]							)

    return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def 							lowerCamelCase	(					):





    '''simple docstring'''


    return [
        "Crime",
        "Drama",
        "Thriller",
        "Action",
        "Comedy",
        "Romance",
        "Documentary",
        "Short",
        "Mystery",
        "History",
        "Family",
        "Adventure",
        "Fantasy",
        "Sci-Fi",
        "Western",
        "Horror",
        "Sport",
        "War",
        "Music",
        "Musical",
        "Animation",
        "Biography",
        "Film-Noir",
    ]




def 							lowerCamelCase	(					):





    '''simple docstring'''


    return transforms.Compose(
        [
            transforms.Resize(256							),
            transforms.CenterCrop(224							),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]  ,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]  ,),
        ]							)




