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

			for i, (row_id, row_dict) in enumerate(generate_fn()			):
						_snake_case     , _snake_case          =      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 	a_		(	)			->  int:
			_snake_case          =      pyspark.sql.SparkSession.builder.master('local[*]'			).appName('pyspark'			).getOrCreate()
			_snake_case          =      spark.range(10			).repartition(1			)
			_snake_case          =      SparkExamplesIterable(__lowercase			)
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(__lowercase			):
						assert row_id == f'''0_{i}'''
						assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def 	a_		(	)			->  str:
			_snake_case          =      pyspark.sql.SparkSession.builder.master('local[*]'			).appName('pyspark'			).getOrCreate()
			_snake_case          =      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          =      lambda __lowercase			: x.reverse()
						_snake_case          =      _get_expected_row_ids_and_row_dicts_for_partition_order(__lowercase	, [2, 1, 0]			)

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

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

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)