






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   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry


def   SCREAMING_SNAKE_CASE       (				):
	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry


def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   str     =						"mock-s3-bucket"
	snake_case__				:   List[str]     =						F'''s3://{mock_bucket}'''
	snake_case__				:   Dict     =						extract_path_from_uri(snake_case_     )
	assert dataset_path.startswith("s3://"     ) is False

	snake_case__				:   Optional[Any]     =						"./local/path"
	snake_case__				:   Tuple     =						extract_path_from_uri(snake_case_     )
	assert dataset_path == new_dataset_path


def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]     ):
	snake_case__				:   Union[str, Any]     =						is_remote_filesystem(snake_case_     )
	assert is_remote is True

	snake_case__				:   Optional[int]     =						fsspec.filesystem("file"     )

	snake_case__				:   List[str]     =						is_remote_filesystem(snake_case_     )
	assert is_remote is False


@pytest.mark.parametrize("compression_fs_class"  ,						snake_case_     )
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							Dict  ,						snake_case_      :							List[str]  ,						snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							str  ,						snake_case_      :							Any     ):
	snake_case__				:   str     =						{"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	snake_case__				:   str     =						input_paths[compression_fs_class.protocol]
	if input_path is None:
		snake_case__				:   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(snake_case_     )
	snake_case__				:   int     =						fsspec.filesystem(compression_fs_class.protocol  ,						fo=snake_case_     )
	assert isinstance(snake_case_  ,						snake_case_     )
	snake_case__				:   Tuple     =						os.path.basename(snake_case_     )
	snake_case__				:   Dict     =						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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]  ,						snake_case_      :							Tuple  ,						snake_case_      :							int     ):
	snake_case__				:   Optional[Any]     =						{"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	snake_case__				:   Tuple     =						compressed_file_paths[protocol]
	snake_case__				:   Optional[int]     =						"dataset.jsonl"
	snake_case__				:   Dict     =						F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	snake_case__,		*snake_case__				:   List[Any]     =						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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							int     ):
	snake_case__				:   str     =						hf_api.dataset_info(snake_case_  ,						token=snake_case_     )
	snake_case__				:   Tuple     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						"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.'''
	)











from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   Optional[int]     =						[]
	for part_id in partition_order:
		snake_case__				:   List[Any]     =						df.where(F'''SPARK_PARTITION_ID() = {part_id}'''     ).collect()
		for row_idx, row in enumerate(snake_case_     ):
			expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict())     )
	return expected_row_ids_and_row_dicts







@require_not_windows
@require_dill_gt_0_3_2
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Tuple     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Union[str, Any]     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Any     =						Spark(snake_case_     )
	# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
	# that each partition can hold 2 rows.
	spark_builder._repartition_df_if_needed(max_shard_size=16     )
	# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
	assert spark_builder.df.rdd.getNumPartitions() == 50







@require_not_windows
@require_dill_gt_0_3_2
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[Any]     =						spark.range(10     ).repartition(2     )
	snake_case__				:   Optional[Any]     =						[1, 0]
	snake_case__				:   Dict     =						_generate_iterable_examples(snake_case_  ,						snake_case_     )  # Reverse the partitions.
	snake_case__				:   Tuple     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						snake_case_     )

	for i, (row_id, row_dict) in enumerate(generate_fn()     ):
		snake_case__,		snake_case__				:   Tuple     =						expected_row_ids_and_row_dicts[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[int]     =						spark.range(10     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						SparkExamplesIterable(snake_case_     )
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		assert row_id == F'''0_{i}'''
		assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   str     =						spark.range(30     ).repartition(3     )
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("numpy.random.Generator"     ) as generator_mock:
		snake_case__				:   Union[str, Any]     =						lambda snake_case_     : x.reverse()
		snake_case__				:   Optional[int]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[2, 1, 0]     )

		snake_case__				:   List[Any]     =						SparkExamplesIterable(snake_case_     ).shuffle_data_sources(snake_case_     )
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(snake_case_     ):
			snake_case__,		snake_case__				:   Optional[Any]     =						expected_row_ids_and_row_dicts[i]
			assert row_id == expected_row_id
			assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Tuple     =						spark.range(20     ).repartition(4     )

	# Partitions 0 and 2
	snake_case__				:   List[Any]     =						SparkExamplesIterable(snake_case_     ).shard_data_sources(worker_id=0  ,						num_workers=2     )
	assert shard_it_a.n_shards == 2
	snake_case__				:   List[str]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[0, 2]     )
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		snake_case__,		snake_case__				:   Optional[int]     =						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

	# Partitions 1 and 3
	snake_case__				:   Any     =						SparkExamplesIterable(snake_case_     ).shard_data_sources(worker_id=1  ,						num_workers=2     )
	assert shard_it_a.n_shards == 2
	snake_case__				:   List[Any]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[1, 3]     )
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		snake_case__,		snake_case__				:   Optional[Any]     =						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Tuple     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						Spark(snake_case_     )
	# Choose a small max_shard_size for maximum partitioning.
	spark_builder._repartition_df_if_needed(max_shard_size=1     )
	# The new number of partitions should not be greater than the number of rows.
	assert spark_builder.df.rdd.getNumPartitions() == 100



