





import torch

from transformers import AutoModel

class      lowerCAmelCase							(      torch.nn.Module    ):
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Tuple="sayef/fsner-bert-base-uncased"			)    ->			Optional[int]:
     super(UpperCAmelCase    ,  self			).__init__()

     lowerCamelCase__					:   int      				=	AutoModel.from_pretrained(UpperCAmelCase    ,  return_dict=UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	torch.nn.CosineSimilarity(3    ,  1e-08			)
     lowerCamelCase__					:   Dict      				=	torch.nn.Softmax(dim=1			)
 def   A_			(   self						:   int    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Union[str, Any]:
     return self.bert(**UpperCAmelCase			).last_hidden_state
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Tuple			)    ->			int:
     return token_embeddings.sum(2    ,  keepdim=UpperCAmelCase			)
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Optional[Any]=1			)    ->			Tuple:
     return self.softmax(T * self.cos(UpperCAmelCase    ,  UpperCAmelCase			)			)







 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   int			)    ->			Any:
     lowerCamelCase__					:   int      				=	W_supports['sizes'].tolist()
     lowerCamelCase__					:   str      				=	W_supports['start_token_id'].item()
     lowerCamelCase__					:   int      				=	W_supports['end_token_id'].item()

     del W_supports["sizes"]
     del W_supports["start_token_id"]
     del W_supports["end_token_id"]

     lowerCamelCase__					:   Optional[Any]      				=	self.BERT(**UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	self.BERT(**UpperCAmelCase			)

     lowerCamelCase__					:   Dict      				=	None
     lowerCamelCase__					:   List[str]      				=	None

     lowerCamelCase__					:   Any      				=	W_supports['input_ids'] == start_token_id
     lowerCamelCase__					:   List[Any]      				=	W_supports['input_ids'] == end_token_id

     for i, size in enumerate(UpperCAmelCase			):
         if i == 0:
             lowerCamelCase__					:   str      				=	0
         else:
             lowerCamelCase__					:   List[Any]      				=	support_sizes[i - 1]

         lowerCamelCase__					:   int      				=	S[s : s + size][start_token_masks[s : s + size]]
         lowerCamelCase__					:   Dict      				=	S[s : s + size][end_token_masks[s : s + size]]

         lowerCamelCase__					:   Optional[Any]      				=	torch.matmul(q[i]    ,  s_start.T			).sum(1			).softmax(0			)
         lowerCamelCase__					:   List[str]      				=	torch.matmul(q[i]    ,  s_end.T			).sum(1			).softmax(0			)

         if p_starts is not None:
             lowerCamelCase__					:   int      				=	torch.vstack((p_starts, p_start)			)
             lowerCamelCase__					:   Dict      				=	torch.vstack((p_ends, p_end)			)
         else:
             lowerCamelCase__					:   List[Any]      				=	p_start
             lowerCamelCase__					:   Union[str, Any]      				=	p_end

     return p_starts, p_ends












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

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


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

        lowerCamelCase__					:   Tuple      				=	SparkExamplesIterable(_UpperCAmelCase				).shuffle_data_sources(_UpperCAmelCase				)
        assert shuffled_it.n_shards == 3
        for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
            lowerCamelCase__				,							lowerCamelCase__					:   Union[str, 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   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Tuple      				=	spark.range(20				).repartition(4				)

    # Partitions 0 and 2
    lowerCamelCase__					:   Optional[Any]      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=0		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   List[str]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[0, 2]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   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
    lowerCamelCase__					:   Dict      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=1		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   Optional[int]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[1, 3]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   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   ( )    ->  Dict:
    lowerCamelCase__					:   int      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   Dict      				=	Spark(_UpperCAmelCase				)
    # 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





