






"""simple docstring"""







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      UpperCamelCase       ( _A  ,				_A )     ->       Dict:
				lowercase    :      Dict       = []
				for part_id in partition_order:
								lowercase    :      Optional[int]       = df.where(F"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
								for row_idx, row in enumerate(_A ):
												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      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      Optional[int]       = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
				lowercase    :      Optional[Any]       = spark.range(100 ).repartition(1 )
				lowercase    :      int       = Spark(_A )
				# 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      UpperCamelCase       ( )     ->       Optional[int]:
				lowercase    :      Any       = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
				lowercase    :      Tuple       = spark.range(10 ).repartition(2 )
				lowercase    :      str       = [1, 0]
				lowercase    :      int       = _generate_iterable_examples(_A  ,				_A )  # Reverse the partitions.
				lowercase    :      Optional[Any]       = _get_expected_row_ids_and_row_dicts_for_partition_order(_A  ,				_A )

				for i, (row_id, row_dict) in enumerate(generate_fn() ):
								lowercase      ,      lowercase    :      List[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      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      Optional[Any]       = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
				lowercase    :      List[str]       = spark.range(10 ).repartition(1 )
				lowercase    :      Union[str, Any]       = SparkExamplesIterable(_A )
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(_A ):
								assert row_id == F"""0_{i}"""
								assert row_dict == {"id": i}


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

								lowercase    :      Tuple       = SparkExamplesIterable(_A ).shuffle_data_sources(_A )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(_A ):
												lowercase      ,      lowercase    :      int       = 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      UpperCamelCase       ( )     ->       int:
				lowercase    :      int       = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
				lowercase    :      Tuple       = spark.range(20 ).repartition(4 )

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

				# Partitions 1 and 3
				lowercase    :      Any       = SparkExamplesIterable(_A ).shard_data_sources(worker_id=1  ,				num_workers=2 )
				assert shard_it_a.n_shards == 2
				lowercase    :      str       = _get_expected_row_ids_and_row_dicts_for_partition_order(_A  ,				[1, 3] )
				for i, (row_id, row_dict) in enumerate(_A ):
								lowercase      ,      lowercase    :      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      UpperCamelCase       ( )     ->       Dict:
				lowercase    :      str       = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
				lowercase    :      List[Any]       = spark.range(100 ).repartition(1 )
				lowercase    :      int       = Spark(_A )
				# 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








"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_fnet import FNetTokenizer
else:
				_lowerCAmelCase            =	None

_lowerCAmelCase            =	logging.get_logger(__name__)
_lowerCAmelCase            =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase            =	{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

_lowerCAmelCase            =	'▁'


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_ids""", """token_type_ids"""]
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		FNetTokenizer






						def __init__(		self  :Optional[Any]			, __magic_name__  :Union[str, Any]=None			, __magic_name__  :Any=None			, __magic_name__  :List[str]=False			, __magic_name__  :Any=True			, __magic_name__  :Any=True			, __magic_name__  :Union[str, Any]="<unk>"			, __magic_name__  :List[Any]="[SEP]"			, __magic_name__  :Union[str, Any]="<pad>"			, __magic_name__  :Tuple="[CLS]"			, __magic_name__  :Optional[int]="[MASK]"			, **__magic_name__  :Optional[int]			, )      ->Tuple:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase    :      Union[str, Any]       = (
										    AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__			, normalized=__magic_name__				)
										    if isinstance(__magic_name__			, __magic_name__				)
										    else mask_token
										)

										super().__init__(
										    __magic_name__			, tokenizer_file=__magic_name__			, do_lower_case=__magic_name__			, remove_space=__magic_name__			, keep_accents=__magic_name__			, unk_token=__magic_name__			, sep_token=__magic_name__			, pad_token=__magic_name__			, cls_token=__magic_name__			, mask_token=__magic_name__			, **__magic_name__			, )

										lowercase    :      Tuple       = do_lower_case
										lowercase    :      Tuple       = remove_space
										lowercase    :      List[str]       = keep_accents
										lowercase    :      Union[str, Any]       = vocab_file
										lowercase    :      int       = False if not self.vocab_file else True






						def      __snake_case      (		self  :Any			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      List[str]       = [self.sep_token_id]
										lowercase    :      Any       = [self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






						def      __snake_case      (		self  :int			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Tuple       = [self.sep_token_id]
										lowercase    :      Union[str, Any]       = [self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep				) * [0]
										return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






						def      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				):
														copyfile(self.vocab_file			, __magic_name__				)

										return (out_vocab_file,)
