

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 						UpperCamelCase					(lowercase_: Dict					)     ->      Dict:
	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry

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

def 						UpperCamelCase					()     ->      Tuple:
	A__							:    Optional[Any]        =		"""mock-s3-bucket"""
	A__							:    Optional[Any]        =		f"""s3://{mock_bucket}"""
	A__							:    List[str]        =		extract_path_from_uri(lowercase_					)
	assert dataset_path.startswith("""s3://"""					) is False

	A__							:    str        =		"""./local/path"""
	A__							:    Any        =		extract_path_from_uri(lowercase_					)
	assert dataset_path == new_dataset_path

def 						UpperCamelCase					(lowercase_: Optional[Any]					)     ->      Tuple:
	A__							:    Tuple        =		is_remote_filesystem(lowercase_					)
	assert is_remote is True

	A__							:    int        =		fsspec.filesystem("""file"""					)

	A__							:    List[str]        =		is_remote_filesystem(lowercase_					)
	assert is_remote is False

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

@pytest.mark.parametrize("""protocol"""    ,      ["""zip""", """gzip"""]					)
def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: Union[str, Any]    ,      lowercase_: Any					)     ->      Union[str, Any]:
	A__							:    Optional[int]        =		{"""zip""": zip_jsonl_path, """gzip""": jsonl_gz_path}
	A__							:    Union[str, Any]        =		compressed_file_paths[protocol]
	A__							:    Optional[Any]        =		"""dataset.jsonl"""
	A__							:    Any        =		f"""{protocol}://{member_file_path}::{compressed_file_path}"""
	A__   ,   *A__							:    List[str]        =		fsspec.get_fs_token_paths(lowercase_					)
	assert fs.isfile(lowercase_					)
	assert not fs.isfile("""non_existing_""" + member_file_path					)

@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: int    ,      lowercase_: int    ,      lowercase_: Any					)     ->      Optional[Any]:
	A__							:    Union[str, Any]        =		hf_api.dataset_info(lowercase_    ,      token=lowercase_					)
	A__							:    Union[str, Any]        =		HfFileSystem(repo_info=lowercase_    ,      token=lowercase_					)
	assert sorted(hffs.glob("""*"""					)					) == [".gitattributes", "data"]
	assert hffs.isdir("""data"""					)
	assert hffs.isfile(""".gitattributes"""					) and hffs.isfile("""data/text_data.txt"""					)
	with open(lowercase_					) as f:
		assert hffs.open("""data/text_data.txt"""    ,      """r"""					).read() == f.read()


def 						UpperCamelCase					()     ->      int:
	A__							:    Tuple        =		"""bz2"""

	# Import module
	import datasets.filesystems

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

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






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



