




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__       (						_A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      ):







	'''simple docstring'''





	snake_case_						       =	{
	    "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),
	}
	snake_case_       , snake_case_						       =	input_paths_and_base_extractors[compression_format]
	if input_path is None:
		snake_case_						       =	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(a__				)
	assert base_extractor.is_extractable(a__				)
	snake_case_						       =	tmp_path / ("extracted" if is_archive else "extracted.txt")
	base_extractor.extract(a__	,      a__				)
	if is_archive:
		assert output_path.is_dir()
		for file_path in output_path.iterdir():
			assert file_path.name == text_file.name
			snake_case_						       =	file_path.read_text(encoding="utf-8"				)
	else:
		snake_case_						       =	output_path.read_text(encoding="utf-8"				)
	snake_case_						       =	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__       (						_A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      ):







	'''simple docstring'''





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


@pytest.fixture
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	import tarfile

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


@pytest.fixture
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	import tarfile

	snake_case_						       =	tmp_path / "data_sym_link"
	directory.mkdir()
	snake_case_						       =	directory / "tar_file_with_sym_link.tar"
	os.symlink(".."	,      directory / "subdir"	,      target_is_directory=a__				)
	with tarfile.TarFile(a__	,      "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__       (						_A	,      _A	,      _A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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






def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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



'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = data


 def __iter__(						self   :			Any     )    ->					Dict:


      """simple docstring"""




      for element in self.data:
           yield element







def   a__			(    a__=True		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Accelerator(even_batches=a__		)
     assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
     return accelerator







def   a__			(    a__ ,       a__ ,       a__ ,       a__ = False		):




     """simple docstring"""


     if iterable:
          __SCREAMING_SNAKE_CASE      = DummyIterableDataset(torch.as_tensor(range(a__		)		)		)
     else:
          __SCREAMING_SNAKE_CASE      = TensorDataset(torch.as_tensor(range(a__		)		)		)

     __SCREAMING_SNAKE_CASE      = DataLoader(a__ ,       batch_size=a__		)
     __SCREAMING_SNAKE_CASE      = accelerator.prepare(a__		)

     return dl







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = create_dataloader(accelerator=a__ ,       dataset_size=a__ ,       batch_size=a__		)

     __SCREAMING_SNAKE_CASE      = [len(batch[0]		) for batch in dl]

     if accelerator.process_index == 0:
          assert batch_sizes == process_0_expected_batch_sizes
     elif accelerator.process_index == 1:
          assert batch_sizes == process_1_expected_batch_sizes







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = create_accelerator()

     # without padding, we would expect a different number of batches
     verify_dataloader_batch_sizes(
         a__ ,       dataset_size=3 ,       batch_size=1 ,       process_0_expected_batch_sizes=[1, 1] ,       process_1_expected_batch_sizes=[1, 1] ,       )

     # without padding, we would expect the same number of batches, but different sizes
     verify_dataloader_batch_sizes(
         a__ ,       dataset_size=7 ,       batch_size=2 ,       process_0_expected_batch_sizes=[2, 2] ,       process_1_expected_batch_sizes=[2, 2] ,       )







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = create_accelerator(even_batches=a__		)

     verify_dataloader_batch_sizes(
         a__ ,       dataset_size=3 ,       batch_size=1 ,       process_0_expected_batch_sizes=[1, 1] ,       process_1_expected_batch_sizes=[1] ,       )

     verify_dataloader_batch_sizes(
         a__ ,       dataset_size=7 ,       batch_size=2 ,       process_0_expected_batch_sizes=[2, 2] ,       process_1_expected_batch_sizes=[2, 1] ,       )







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = create_accelerator(even_batches=a__		)

     __SCREAMING_SNAKE_CASE      = torch.nn.Linear(1 ,       1		)
     __SCREAMING_SNAKE_CASE      = accelerator.prepare(a__		)

     __SCREAMING_SNAKE_CASE      = create_dataloader(a__ ,       dataset_size=3 ,       batch_size=1		)

     __SCREAMING_SNAKE_CASE      = []
     with accelerator.join_uneven_inputs([ddp_model]		):
          for batch_idx, batch in enumerate(a__		):
               __SCREAMING_SNAKE_CASE      = ddp_model(batch[0].float()		)
               __SCREAMING_SNAKE_CASE      = output.sum()
               loss.backward()
               batch_idxs.append(a__		)

     accelerator.wait_for_everyone()

     if accelerator.process_index == 0:
          assert batch_idxs == [0, 1]
     elif accelerator.process_index == 1:
          assert batch_idxs == [0]







def   a__			(    a__		):




     """simple docstring"""


     with warnings.catch_warnings(record=a__		) as w:
          with accelerator.join_uneven_inputs([Mock()]		):
               pass

          assert issubclass(w[-1].category ,       a__		)
          assert "only supported for multi-GPU" in str(w[-1].message		)







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = True
     __SCREAMING_SNAKE_CASE      = False
     __SCREAMING_SNAKE_CASE      = create_accelerator(even_batches=a__		)
     __SCREAMING_SNAKE_CASE      = torch.nn.Linear(1 ,       1		)
     __SCREAMING_SNAKE_CASE      = accelerator.prepare(a__		)
     __SCREAMING_SNAKE_CASE      = create_dataloader(a__ ,       dataset_size=3 ,       batch_size=1		)
     __SCREAMING_SNAKE_CASE      = create_dataloader(a__ ,       dataset_size=3 ,       batch_size=1		)

     with accelerator.join_uneven_inputs([ddp_model] ,       even_batches=a__		):
          __SCREAMING_SNAKE_CASE      = train_dl.batch_sampler.even_batches
          __SCREAMING_SNAKE_CASE      = valid_dl.batch_sampler.even_batches

     assert train_dl_overridden_value == overridden_even_batches
     assert valid_dl_overridden_value == overridden_even_batches
     assert train_dl.batch_sampler.even_batches == default_even_batches
     assert valid_dl.batch_sampler.even_batches == default_even_batches







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = True
     __SCREAMING_SNAKE_CASE      = False
     __SCREAMING_SNAKE_CASE      = create_accelerator(even_batches=a__		)
     __SCREAMING_SNAKE_CASE      = torch.nn.Linear(1 ,       1		)
     __SCREAMING_SNAKE_CASE      = accelerator.prepare(a__		)
     create_dataloader(a__ ,       dataset_size=3 ,       batch_size=1 ,       iterable=a__		)
     __SCREAMING_SNAKE_CASE      = create_dataloader(a__ ,       dataset_size=3 ,       batch_size=1		)

     with warnings.catch_warnings():
          warnings.filterwarnings("""ignore"""		)
          try:
               with accelerator.join_uneven_inputs([ddp_model] ,       even_batches=a__		):
                    __SCREAMING_SNAKE_CASE      = batch_dl.batch_sampler.even_batches
          except AttributeError:
               # ensure attribute error is not raised when processing iterable dl
               raise AssertionError

     assert batch_dl_overridden_value == overridden_even_batches
     assert batch_dl.batch_sampler.even_batches == default_even_batches







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = create_accelerator()
     __SCREAMING_SNAKE_CASE      = torch.nn.Linear(1 ,       1		)
     __SCREAMING_SNAKE_CASE      = accelerator.prepare(a__		)
     create_dataloader(a__ ,       dataset_size=3 ,       batch_size=1 ,       iterable=a__		)

     with warnings.catch_warnings(record=a__		) as w:
          with accelerator.join_uneven_inputs([ddp_model] ,       even_batches=a__		):
               pass

          assert issubclass(w[-1].category ,       a__		)
          assert "only supported for map-style datasets" in str(w[-1].message		)







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = create_accelerator()

     accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""		)
     test_default_ensures_even_batch_sizes()

     accelerator.print("""Run tests with even_batches disabled"""		)
     test_can_disable_even_batches()

     accelerator.print("""Test joining uneven inputs"""		)
     test_can_join_uneven_inputs()

     accelerator.print("""Test overriding even_batches when joining uneven inputs"""		)
     test_join_can_override_even_batches()

     accelerator.print("""Test overriding even_batches for mixed dataloader types"""		)
     test_join_can_override_for_mixed_type_dataloaders()

     accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""		)
     test_join_raises_warning_for_iterable_when_overriding_even_batches()

     accelerator.print("""Test join with non DDP distributed raises warning"""		)
     __SCREAMING_SNAKE_CASE      = accelerator.state.distributed_type
     __SCREAMING_SNAKE_CASE      = DistributedType.FSDP
     test_join_raises_warning_for_non_ddp_distributed(a__		)
     __SCREAMING_SNAKE_CASE      = original_state


if __name__ == "__main__":
  main()






