

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      :			Union[str, Any]		,__a      :			Dict	)			->     str:

					"""simple docstring"""

					_a	:    Optional[int]         =		[]
					for part_id in partition_order:
										_a	:    Tuple         =		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 (	)			->     Any:

					"""simple docstring"""

					_a	:    Optional[Any]         =		pyspark.sql.SparkSession.builder.master('''local[*]'''	).appName('''pyspark'''	).getOrCreate()
					_a	:    Optional[Any]         =		spark.range(100	).repartition(1	)
					_a	:    str         =		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 (	)			->     Tuple:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		pyspark.sql.SparkSession.builder.master('''local[*]'''	).appName('''pyspark'''	).getOrCreate()
					_a	:    List[Any]         =		spark.range(10	).repartition(2	)
					_a	:    Any         =		[1, 0]
					_a	:    Optional[int]         =		_generate_iterable_examples(__a		,__a	)  # Reverse the partitions.
					_a	:    Any         =		_get_expected_row_ids_and_row_dicts_for_partition_order(__a		,__a	)

					for i, (row_id, row_dict) in enumerate(generate_fn()	):
										_a      ,					_a	:    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 (	)			->     Tuple:

					"""simple docstring"""

					_a	:    List[str]         =		pyspark.sql.SparkSession.builder.master('''local[*]'''	).appName('''pyspark'''	).getOrCreate()
					_a	:    Any         =		spark.range(10	).repartition(1	)
					_a	:    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 (	)			->     Optional[Any]:

					"""simple docstring"""

					_a	:    Tuple         =		pyspark.sql.SparkSession.builder.master('''local[*]'''	).appName('''pyspark'''	).getOrCreate()
					_a	:    Tuple         =		spark.range(30	).repartition(3	)
					# Mock the generator so that shuffle reverses the partition indices.
					with patch('''numpy.random.Generator'''	) as generator_mock:
										_a	:    Union[str, Any]         =		lambda __a	: x.reverse()
										_a	:    Union[str, Any]         =		_get_expected_row_ids_and_row_dicts_for_partition_order(__a		,[2, 1, 0]	)

										_a	:    List[Any]         =		SparkExamplesIterable(__a	).shuffle_data_sources(__a	)
										assert shuffled_it.n_shards == 3
										for i, (row_id, row_dict) in enumerate(__a	):
															_a      ,					_a	:    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 (	)			->     List[str]:

					"""simple docstring"""

					_a	:    Dict         =		pyspark.sql.SparkSession.builder.master('''local[*]'''	).appName('''pyspark'''	).getOrCreate()
					_a	:    List[Any]         =		spark.range(20	).repartition(4	)

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

					# Partitions 1 and 3
					_a	:    Any         =		SparkExamplesIterable(__a	).shard_data_sources(worker_id=1		,num_workers=2	)
					assert shard_it_a.n_shards == 2
					_a	:    Union[str, Any]         =		_get_expected_row_ids_and_row_dicts_for_partition_order(__a		,[1, 3]	)
					for i, (row_id, row_dict) in enumerate(__a	):
										_a      ,					_a	:    Optional[int]         =		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 (	)			->     List[str]:

					"""simple docstring"""

					_a	:    Dict         =		pyspark.sql.SparkSession.builder.master('''local[*]'''	).appName('''pyspark'''	).getOrCreate()
					_a	:    List[Any]         =		spark.range(100	).repartition(1	)
					_a	:    Union[str, Any]         =		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






from collections import OrderedDict
from typing import Mapping

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


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_a	:    Dict         =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_a	:    List[Any]         =		{0: '''batch''', 1: '''sequence'''}
												_a	:    Union[str, Any]         =		{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]       )



