
"""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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  []
			for part_id in partition_order:
						lowerCamelCase_					=  df.where(f"SPARK_PARTITION_ID() = {part_id}"						).collect()
						for row_idx, row in enumerate(lowerCAmelCase__						):
									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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(100						).repartition(1						)
			lowerCamelCase_					=  Spark(lowerCAmelCase__						)
			# 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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(10						).repartition(2						)
			lowerCamelCase_					=  [1, 0]
			lowerCamelCase_					=  _generate_iterable_examples(lowerCAmelCase__	,lowerCAmelCase__						)  # Reverse the partitions.
			lowerCamelCase_					=  _get_expected_row_ids_and_row_dicts_for_partition_order(lowerCAmelCase__	,lowerCAmelCase__						)

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






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

						lowerCamelCase_					=  SparkExamplesIterable(lowerCAmelCase__						).shuffle_data_sources(lowerCAmelCase__						)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(lowerCAmelCase__						):
									lowerCamelCase_		, lowerCamelCase_					=  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  lowercase						(	):
			lowerCamelCase_					=  pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
			lowerCamelCase_					=  spark.range(20						).repartition(4						)

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

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

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

			return model


if __name__ == "__main__":
						A_    							=					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""")
						A_    							=					parser.parse_args()
						A_    							=					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)



