import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


__lowercase										= datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
    import pyspark


@dataclass
class      lowerCamelCase_					(       datasets.BuilderConfig			):






    '''simple docstring'''






    a__     :						Optional[datasets.Features]      =				None



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      ):
    '''simple docstring'''







    import pyspark

    def generate_fn():
        __UpperCamelCase					:Tuple        =		df.select('''*'''  ,      pyspark.sql.functions.spark_partition_id().alias('''part_id'''     )     )
        for partition_id in partition_order:
            __UpperCamelCase					:str        =		df_with_partition_id.select('''*'''     ).where(f"""part_id = {partition_id}"""     ).drop('''part_id'''     )
            __UpperCamelCase					:Optional[int]        =		partition_df.collect()
            __UpperCamelCase					:Tuple        =		0
            for row in rows:
                yield f"""{partition_id}_{row_id}""", row.asDict()
                row_id += 1

    return generate_fn


class      lowerCamelCase_					(       _BaseExamplesIterable			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=None			,			)	->     int:
        __UpperCamelCase					:List[str]        =		df
        __UpperCamelCase					:Optional[Any]        =		partition_order or range(self.df.rdd.getNumPartitions())
        __UpperCamelCase					:int        =		_generate_iterable_examples(self.df			,			self.partition_order)







    def __iter__(     self)	->     List[Any]:
        yield from self.generate_examples_fn()







    def       UpperCamelCase__       (     self			,			__lowercase)	->     "SparkExamplesIterable":
        __UpperCamelCase					:str        =		list(range(self.df.rdd.getNumPartitions()))
        generator.shuffle(__lowercase)
        return SparkExamplesIterable(self.df			,			partition_order=__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     "SparkExamplesIterable":
        __UpperCamelCase					:Optional[int]        =		self.split_shard_indices_by_worker(__lowercase			,			__lowercase)
        return SparkExamplesIterable(self.df			,			partition_order=__lowercase)







    @property
    def       UpperCamelCase__       (     self)	->     int:
        return len(self.partition_order)


class      lowerCamelCase_					(       datasets.DatasetBuilder			):






    '''simple docstring'''






    a__     :						List[Any]      =				SparkConfig







    def __init__(     self			,			__lowercase			,			__lowercase = None			,			__lowercase = None			,			**__lowercase			,			)	->     Optional[int]:
        import pyspark

        __UpperCamelCase					:Optional[int]        =		pyspark.sql.SparkSession.builder.getOrCreate()
        __UpperCamelCase					:Any        =		df
        __UpperCamelCase					:str        =		working_dir

        super().__init__(
            cache_dir=__lowercase			,			config_name=str(self.df.semanticHash())			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self)	->     Tuple:
        # Returns the path of the created file.
        def create_cache_and_write_probe(__lowercase):
            # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
            # already exist.
            os.makedirs(self._cache_dir			,			exist_ok=__lowercase)
            __UpperCamelCase					:List[str]        =		os.path.join(self._cache_dir			,			'''fs_test''' + uuid.uuida().hex)
            # Opening the file in append mode will create a new file unless it already exists, in which case it will not
            # change the file contents.
            open(__lowercase			,			'''a''')
            return [probe_file]

        if self._spark.conf.get('''spark.master'''			,			'''''').startswith('''local'''):
            return

        # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
        # accessible to the driver.
        # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
        if self._cache_dir:
            __UpperCamelCase					:Tuple        =		(
                self._spark.sparkContext.parallelize(range(1)			,			1).mapPartitions(__lowercase).collect()
            )
            if os.path.isfile(probe[0]):
                return

        raise ValueError(
            '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''')







    def       UpperCamelCase__       (     self)	->     int:
        return datasets.DatasetInfo(features=self.config.features)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Union[str, Any]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN)]







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Dict:
        import pyspark

        def get_arrow_batch_size(__lowercase):
            for batch in it:
                yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]})

        __UpperCamelCase					:int        =		self.df.count()
        __UpperCamelCase					:List[Any]        =		df_num_rows if df_num_rows <= 100 else 100
        # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
        __UpperCamelCase					:Union[str, Any]        =		(
            self.df.limit(__lowercase)
            .repartition(1)
            .mapInArrow(__lowercase			,			'''batch_bytes: long''')
            .agg(pyspark.sql.functions.sum('''batch_bytes''').alias('''sample_bytes'''))
            .collect()[0]
            .sample_bytes
            / sample_num_rows
        )
        __UpperCamelCase					:Union[str, Any]        =		approx_bytes_per_row * df_num_rows
        if approx_total_size > max_shard_size:
            # Make sure there is at least one row per partition.
            __UpperCamelCase					:Dict        =		min(__lowercase			,			int(approx_total_size / max_shard_size))
            __UpperCamelCase					:List[str]        =		self.df.repartition(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     Iterable[Tuple[int, bool, Union[int, tuple]]]:
        import pyspark

        __UpperCamelCase					:Any        =		ParquetWriter if file_format == '''parquet''' else ArrowWriter
        __UpperCamelCase					:Optional[Any]        =		os.path.join(self._working_dir			,			os.path.basename(__lowercase)) if self._working_dir else fpath
        __UpperCamelCase					:List[str]        =		file_format == '''parquet'''

        # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
        # pickling the SparkContext.
        __UpperCamelCase					:Optional[int]        =		self.config.features
        __UpperCamelCase					:int        =		self._writer_batch_size
        __UpperCamelCase					:Dict        =		self._fs.storage_options

        def write_arrow(__lowercase):
            # Within the same SparkContext, no two task attempts will share the same attempt ID.
            __UpperCamelCase					:Tuple        =		pyspark.TaskContext().taskAttemptId()
            __UpperCamelCase					:str        =		next(__lowercase			,			__lowercase)
            if first_batch is None:
                # Some partitions might not receive any data.
                return pa.RecordBatch.from_arrays(
                    [[task_id], [0], [0]]			,			names=['''task_id''', '''num_examples''', '''num_bytes''']			,			)
            __UpperCamelCase					:Optional[Any]        =		0
            __UpperCamelCase					:Dict        =		writer_class(
                features=__lowercase			,			path=working_fpath.replace('''SSSSS'''			,			f"""{shard_id:05d}""").replace('''TTTTT'''			,			f"""{task_id:05d}""")			,			writer_batch_size=__lowercase			,			storage_options=__lowercase			,			embed_local_files=__lowercase			,			)
            __UpperCamelCase					:str        =		pa.Table.from_batches([first_batch])
            writer.write_table(__lowercase)
            for batch in it:
                if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                    __UpperCamelCase  ,	__UpperCamelCase					:Dict        =		writer.finalize()
                    writer.close()
                    yield pa.RecordBatch.from_arrays(
                        [[task_id], [num_examples], [num_bytes]]			,			names=['''task_id''', '''num_examples''', '''num_bytes''']			,			)
                    shard_id += 1
                    __UpperCamelCase					:Optional[Any]        =		writer_class(
                        features=writer._features			,			path=working_fpath.replace('''SSSSS'''			,			f"""{shard_id:05d}""").replace('''TTTTT'''			,			f"""{task_id:05d}""")			,			writer_batch_size=__lowercase			,			storage_options=__lowercase			,			embed_local_files=__lowercase			,			)
                __UpperCamelCase					:Any        =		pa.Table.from_batches([batch])
                writer.write_table(__lowercase)

            if writer._num_bytes > 0:
                __UpperCamelCase  ,	__UpperCamelCase					:int        =		writer.finalize()
                writer.close()
                yield pa.RecordBatch.from_arrays(
                    [[task_id], [num_examples], [num_bytes]]			,			names=['''task_id''', '''num_examples''', '''num_bytes''']			,			)

            if working_fpath != fpath:
                for file in os.listdir(os.path.dirname(__lowercase)):
                    __UpperCamelCase					:Tuple        =		os.path.join(os.path.dirname(__lowercase)			,			os.path.basename(__lowercase))
                    shutil.move(__lowercase			,			__lowercase)

        __UpperCamelCase					:Tuple        =		(
            self.df.mapInArrow(__lowercase			,			'''task_id: long, num_examples: long, num_bytes: long''')
            .groupBy('''task_id''')
            .agg(
                pyspark.sql.functions.sum('''num_examples''').alias('''total_num_examples''')			,			pyspark.sql.functions.sum('''num_bytes''').alias('''total_num_bytes''')			,			pyspark.sql.functions.count('''num_bytes''').alias('''num_shards''')			,			pyspark.sql.functions.collect_list('''num_examples''').alias('''shard_lengths''')			,			)
            .collect()
        )
        for row in stats:
            yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = "arrow"			,			__lowercase = None			,			__lowercase = None			,			**__lowercase			,			)	->     Tuple:
        self._validate_cache_dir()

        __UpperCamelCase					:Any        =		convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE)
        self._repartition_df_if_needed(__lowercase)
        __UpperCamelCase					:Any        =		not is_remote_filesystem(self._fs)
        __UpperCamelCase					:Optional[int]        =		os.path.join if is_local else posixpath.join

        __UpperCamelCase					:Dict        =		'''-TTTTT-SSSSS-of-NNNNN'''
        __UpperCamelCase					:Any        =		f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
        __UpperCamelCase					:str        =		path_join(self._output_dir			,			__lowercase)

        __UpperCamelCase					:str        =		0
        __UpperCamelCase					:List[Any]        =		0
        __UpperCamelCase					:str        =		0
        __UpperCamelCase					:Union[str, Any]        =		[]
        __UpperCamelCase					:Optional[Any]        =		[]

        for task_id, content in self._prepare_split_single(__lowercase			,			__lowercase			,			__lowercase):
            (
                (
                __UpperCamelCase
            )  ,	(
                __UpperCamelCase
            )  ,	(
                __UpperCamelCase
            )  ,	(
                __UpperCamelCase
            )  ,	
            )					:Union[str, Any]        =		content
            if num_bytes > 0:
                total_num_examples += num_examples
                total_num_bytes += num_bytes
                total_shards += num_shards
                task_id_and_num_shards.append((task_id, num_shards))
                all_shard_lengths.extend(__lowercase)

        __UpperCamelCase					:Any        =		total_num_examples
        __UpperCamelCase					:List[Any]        =		total_num_bytes

        # should rename everything at the end
        logger.debug(f"""Renaming {total_shards} shards.""")
        if total_shards > 1:
            __UpperCamelCase					:List[str]        =		all_shard_lengths

            # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
            # pickling error due to pickling the SparkContext.
            __UpperCamelCase					:Optional[Any]        =		self._fs

            # use the -SSSSS-of-NNNNN pattern
            def _rename_shard(
                __lowercase			,			__lowercase			,			__lowercase			,			):
                rename(
                    __lowercase			,			fpath.replace('''SSSSS'''			,			f"""{shard_id:05d}""").replace('''TTTTT'''			,			f"""{task_id:05d}""")			,			fpath.replace('''TTTTT-SSSSS'''			,			f"""{global_shard_id:05d}""").replace('''NNNNN'''			,			f"""{total_shards:05d}""")			,			)

            __UpperCamelCase					:int        =		[]
            __UpperCamelCase					:Optional[int]        =		0
            for i in range(len(__lowercase)):
                __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		task_id_and_num_shards[i]
                for shard_id in range(__lowercase):
                    args.append([task_id, shard_id, global_shard_id])
                    global_shard_id += 1
            self._spark.sparkContext.parallelize(__lowercase			,			len(__lowercase)).map(lambda __lowercase: _rename_shard(*__lowercase)).collect()
        else:
            # don't use any pattern
            __UpperCamelCase					:Optional[int]        =		0
            __UpperCamelCase					:Tuple        =		task_id_and_num_shards[0][0]
            self._rename(
                fpath.replace('''SSSSS'''			,			f"""{shard_id:05d}""").replace('''TTTTT'''			,			f"""{task_id:05d}""")			,			fpath.replace(__lowercase			,			'''''')			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			)	->     SparkExamplesIterable:
        return SparkExamplesIterable(self.df)






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      encoder_layers=args.encoder_layers  ,      decoder_layers=args.decoder_layers  ,      encoder_attention_heads=args.encoder_attention_heads  ,      decoder_attention_heads=args.decoder_attention_heads  ,      encoder_ffn_dim=args.encoder_ffn_embed_dim  ,      decoder_ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.encoder_embed_dim  ,      encoder_layerdrop=args.encoder_layerdrop  ,      decoder_layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''relu'''  ,      )

    __UpperCamelCase					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.shared     )

    return model


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''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.''')
    __lowercase										= parser.parse_args()
    __lowercase										= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





