


"""simple docstring"""







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 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  assert "mock" in _fsspec_registry
  assert "bz2" in _fsspec_registry


def 					lowercase      (    )->				Tuple:
  '''simple docstring'''







  assert "mock" not in _fsspec_registry
  assert "bz2" in _fsspec_registry


def 					lowercase      (    )->				Tuple:
  '''simple docstring'''







  a						:		List[Any]											=      "mock-s3-bucket"
  a						:		Tuple											=      F'''s3://{mock_bucket}'''
  a						:		List[Any]											=      extract_path_from_uri(A_	)
  assert dataset_path.startswith("s3://"	) is False

  a						:		List[str]											=      "./local/path"
  a						:		Any											=      extract_path_from_uri(A_	)
  assert dataset_path == new_dataset_path


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  a						:		str											=      is_remote_filesystem(A_	)
  assert is_remote is True

  a						:		Optional[Any]											=      fsspec.filesystem("file"	)

  a						:		Union[str, Any]											=      is_remote_filesystem(A_	)
  assert is_remote is False


@pytest.mark.parametrize("compression_fs_class"  ,       A_	)
def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Dict											=      {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
  a						:		Optional[Any]											=      input_paths[compression_fs_class.protocol]
  if input_path is None:
    a						:		int											=      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(A_	)
  a						:		List[str]											=      fsspec.filesystem(compression_fs_class.protocol  ,       fo=A_	)
  assert isinstance(A_  ,       A_	)
  a						:		Tuple											=      os.path.basename(A_	)
  a						:		List[str]											=      expected_filename[: expected_filename.rindex("."	)]
  assert fs.glob("*"	) == [expected_filename]
  with fs.open(A_  ,       "r"  ,       encoding="utf-8"	) as f, open(A_  ,       encoding="utf-8"	) as expected_file:
    assert f.read() == expected_file.read()


@pytest.mark.parametrize("protocol"  ,       ["zip", "gzip"]	)
def 					lowercase      (    A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		int											=      {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
  a						:		int											=      compressed_file_paths[protocol]
  a						:		int											=      "dataset.jsonl"
  a						:		Any											=      F'''{protocol}://{member_file_path}::{compressed_file_path}'''
  a							,		*a						:		Optional[Any]											=      fsspec.get_fs_token_paths(A_	)
  assert fs.isfile(A_	)
  assert not fs.isfile("non_existing_" + member_file_path	)


@pytest.mark.integration
def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  a						:		List[Any]											=      hf_api.dataset_info(A_  ,       token=A_	)
  a						:		List[Any]											=      HfFileSystem(repo_info=A_  ,       token=A_	)
  assert sorted(hffs.glob("*"	)	) == [".gitattributes", "data"]
  assert hffs.isdir("data"	)
  assert hffs.isfile(".gitattributes"	) and hffs.isfile("data/text_data.txt"	)
  with open(A_	) as f:
    assert hffs.open("data/text_data.txt"  ,       "r"	).read() == f.read()







def 					lowercase      (    )->				Optional[Any]:
  '''simple docstring'''







  a						:		int											=      "bz2"

  # Import module
  import datasets.filesystems

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

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










"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






    @property
    def      __snake_case							( self    :   int):
      if self.task == "multiple-choice":
        a						:		List[Any]											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		int											=      {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("global_attention_mask", dynamic_axis),
          ])






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






