





import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    assert "mock" in _fsspec_registry
    assert "bz2" in _fsspec_registry


def 							__snake_case							(    )				->						Union[str, Any]:
    assert "mock" not in _fsspec_registry
    assert "bz2" in _fsspec_registry


def 							__snake_case							(    )				->						Dict:
    SCREAMING_SNAKE_CASE__											=			"""mock-s3-bucket"""
    SCREAMING_SNAKE_CASE__											=			f'''s3://{mock_bucket}'''
    SCREAMING_SNAKE_CASE__											=			extract_path_from_uri(snake_case__	)
    assert dataset_path.startswith('''s3://'''	) is False

    SCREAMING_SNAKE_CASE__											=			"""./local/path"""
    SCREAMING_SNAKE_CASE__											=			extract_path_from_uri(snake_case__	)
    assert dataset_path == new_dataset_path


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			is_remote_filesystem(snake_case__	)
    assert is_remote is True

    SCREAMING_SNAKE_CASE__											=			fsspec.filesystem('''file'''	)

    SCREAMING_SNAKE_CASE__											=			is_remote_filesystem(snake_case__	)
    assert is_remote is False


@pytest.mark.parametrize('''compression_fs_class''' ,							snake_case__	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_file, """bz2""": bza_file, """lz4""": lza_file}
    SCREAMING_SNAKE_CASE__											=			input_paths[compression_fs_class.protocol]
    if input_path is None:
        SCREAMING_SNAKE_CASE__											=			f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
        if compression_fs_class.protocol == "lz4":
            reason += require_lza.kwargs["reason"]
        elif compression_fs_class.protocol == "zstd":
            reason += require_zstandard.kwargs["reason"]
        pytest.skip(snake_case__	)
    SCREAMING_SNAKE_CASE__											=			fsspec.filesystem(compression_fs_class.protocol ,							fo=snake_case__	)
    assert isinstance(snake_case__ ,							snake_case__	)
    SCREAMING_SNAKE_CASE__											=			os.path.basename(snake_case__	)
    SCREAMING_SNAKE_CASE__											=			expected_filename[: expected_filename.rindex('''.'''	)]
    assert fs.glob('''*'''	) == [expected_filename]
    with fs.open(snake_case__ ,							'''r''' ,							encoding='''utf-8'''	) as f, open(snake_case__ ,							encoding='''utf-8'''	) as expected_file:
        assert f.read() == expected_file.read()


@pytest.mark.parametrize('''protocol''' ,							['''zip''', '''gzip''']	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			{"""zip""": zip_jsonl_path, """gzip""": jsonl_gz_path}
    SCREAMING_SNAKE_CASE__											=			compressed_file_paths[protocol]
    SCREAMING_SNAKE_CASE__											=			"""dataset.jsonl"""
    SCREAMING_SNAKE_CASE__											=			f'''{protocol}://{member_file_path}::{compressed_file_path}'''
    SCREAMING_SNAKE_CASE__											=			fsspec.get_fs_token_paths(snake_case__	)
    assert fs.isfile(snake_case__	)
    assert not fs.isfile('''non_existing_''' + member_file_path	)


@pytest.mark.integration
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			hf_api.dataset_info(snake_case__ ,							token=snake_case__	)
    SCREAMING_SNAKE_CASE__											=			HfFileSystem(repo_info=snake_case__ ,							token=snake_case__	)
    assert sorted(hffs.glob('''*'''	)	) == [".gitattributes", "data"]
    assert hffs.isdir('''data'''	)
    assert hffs.isfile('''.gitattributes'''	) and hffs.isfile('''data/text_data.txt'''	)
    with open(snake_case__	) as f:
        assert hffs.open('''data/text_data.txt''' ,							'''r'''	).read() == f.read()



def 							__snake_case							(    )				->						str:
    SCREAMING_SNAKE_CASE__											=			"""bz2"""

    # Import module
    import datasets.filesystems

    # Overwrite protocol and reload
    register_implementation(snake_case__ ,							snake_case__ ,							clobber=snake_case__	)
    with pytest.warns(snake_case__	) as warning_info:
        importlib.reload(datasets.filesystems	)

    assert len(snake_case__	) == 1
    assert (
        str(warning_info[0].message	)
        == f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
    )








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import torch




class       snake_case__ (				TensorFormatter[Mapping, "torch.Tensor", Mapping]):

       '''simple docstring'''



       def __init__(				self      ,    a__=None      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(features=a__     )
            __snake_case      :int 							=    torch_tensor_kwargs
            import torch  # noqa import torch at initialization



       def        __lowercase    (				self      ,    a__     )			->   List[str]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    a__     ) and column:
                 if all(
                     isinstance(a__      ,    torch.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                     for x in column     ):
                      return torch.stack(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    (str, bytes, type(a__     ))     ):
                 return value
            elif isinstance(a__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
                 return value.tolist()

            __snake_case      :Optional[int] 							=    {}

            if isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.integer     ):
                 __snake_case      :List[Any] 							=    {"""dtype""": torch.intaa}
            elif isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.floating     ):
                 __snake_case      :Tuple 							=    {"""dtype""": torch.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(a__      ,    PIL.Image.Image     ):
                      __snake_case      :Union[str, Any] 							=    np.asarray(a__     )
            return torch.tensor(a__      ,    **{**default_dtype, **self.torch_tensor_kwargs}     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            import torch

            # support for torch, tf, jax etc.
            if hasattr(a__      ,    """__array__"""     ) and not isinstance(a__      ,    torch.Tensor     ):
                 __snake_case      :Optional[Any] 							=    data_struct.__array__()
            # support for nested types like struct of list of struct
            if isinstance(a__      ,    np.ndarray     ):
                 if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                      return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            elif isinstance(a__      ,    (list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            return self._tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            return map_nested(self._recursive_tensorize      ,    a__      ,    map_list=a__     )



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.numpy_arrow_extractor().extract_row(a__     )
            __snake_case      :Any 							=    self.python_features_decoder.decode_row(a__     )
            return self.recursive_tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   "torch.Tensor":






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.numpy_arrow_extractor().extract_column(a__     )
            __snake_case      :List[Any] 							=    self.python_features_decoder.decode_column(a__      ,    pa_table.column_names[0]     )
            __snake_case      :Optional[Any] 							=    self.recursive_tensorize(a__     )
            __snake_case      :Any 							=    self._consolidate(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.numpy_arrow_extractor().extract_batch(a__     )
            __snake_case      :Tuple 							=    self.python_features_decoder.decode_batch(a__     )
            __snake_case      :Tuple 							=    self.recursive_tensorize(a__     )
            for column_name in batch:
                 __snake_case      :str 							=    self._consolidate(batch[column_name]     )
            return batch


