




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


_lowerCamelCase     :		List[str]								=       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark


@dataclass
class     UpperCamelCase_   (			datasets.BuilderConfig					):

    '''simple docstring'''






    UpperCAmelCase__							  =				None







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    )       ->	List[Any]:



       """simple docstring"""

       import pyspark

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

       return generate_fn


class     UpperCamelCase_   (			_BaseExamplesIterable					):

    '''simple docstring'''






    def __init__(      self				:     Dict ,			UpperCAmelCase__				:     "pyspark.sql.DataFrame" ,			UpperCAmelCase__				:     int=None ,			)						->Optional[int]:





           '''simple docstring'''

           A__							=   df
           A__							=   partition_order or range(self.df.rdd.getNumPartitions())
           A__							=   _generate_iterable_examples(self.df ,			self.partition_order)


    def __iter__(      self				:     Union[str, Any])						->Tuple:





           '''simple docstring'''

           yield from self.generate_examples_fn()


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     np.random.Generator)						->"SparkExamplesIterable":





           '''simple docstring'''

           A__							=   list(range(self.df.rdd.getNumPartitions()))
           generator.shuffle(UpperCAmelCase__)
           return SparkExamplesIterable(self.df ,			partition_order=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int)						->"SparkExamplesIterable":





           '''simple docstring'''

           A__							=   self.split_shard_indices_by_worker(UpperCAmelCase__ ,			UpperCAmelCase__)
           return SparkExamplesIterable(self.df ,			partition_order=UpperCAmelCase__)





    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->int:





           '''simple docstring'''

           return len(self.partition_order)


class     UpperCamelCase_   (			datasets.DatasetBuilder					):

    '''simple docstring'''






    UpperCAmelCase__							  =				SparkConfig


    def __init__(      self				:     List[Any] ,			UpperCAmelCase__				:     "pyspark.sql.DataFrame" ,			UpperCAmelCase__				:     str = None ,			UpperCAmelCase__				:     str = None ,			**UpperCAmelCase__				:     Dict ,			)						->Optional[int]:





           '''simple docstring'''

           import pyspark

           A__							=   pyspark.sql.SparkSession.builder.getOrCreate()
           A__							=   df
           A__							=   working_dir

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           def create_cache_and_write_probe(UpperCAmelCase__				:     Optional[Any]):
                  # 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=UpperCAmelCase__)
                  A__							=   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(UpperCAmelCase__ ,			'''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:
                  A__							=   (
                      self._spark.sparkContext.parallelize(range(1) ,			1).mapPartitions(UpperCAmelCase__).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 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Union[str, Any]:





           '''simple docstring'''

           return datasets.DatasetInfo(features=self.config.features)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     datasets.download.download_manager.DownloadManager)						->Tuple:





           '''simple docstring'''

           return [datasets.SplitGenerator(name=datasets.Split.TRAIN)]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     Any)						->List[Any]:





           '''simple docstring'''

           import pyspark

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

           A__							=   self.df.count()
           A__							=   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.
           A__							=   (
               self.df.limit(UpperCAmelCase__)
               .repartition(1)
               .mapInArrow(UpperCAmelCase__ ,			'''batch_bytes: long''')
               .agg(pyspark.sql.functions.sum('''batch_bytes''').alias('''sample_bytes'''))
               .collect()[0]
               .sample_bytes
               / sample_num_rows
           )
           A__							=   approx_bytes_per_row * df_num_rows
           if approx_total_size > max_shard_size:
                  # Make sure there is at least one row per partition.
                  A__							=   min(UpperCAmelCase__ ,			int(approx_total_size / max_shard_size))
                  A__							=   self.df.repartition(UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     int ,			)						->Iterable[Tuple[int, bool, Union[int, tuple]]]:





           '''simple docstring'''

           import pyspark

           A__							=   ParquetWriter if file_format == '''parquet''' else ArrowWriter
           A__							=   os.path.join(self._working_dir ,			os.path.basename(UpperCAmelCase__)) if self._working_dir else fpath
           A__							=   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.
           A__							=   self.config.features
           A__							=   self._writer_batch_size
           A__							=   self._fs.storage_options

           def write_arrow(UpperCAmelCase__				:     List[Any]):
                  # Within the same SparkContext, no two task attempts will share the same attempt ID.
                  A__							=   pyspark.TaskContext().taskAttemptId()
                  A__							=   next(UpperCAmelCase__ ,			UpperCAmelCase__)
                  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'''] ,			)
                  A__							=   0
                  A__							=   writer_class(
                      features=UpperCAmelCase__ ,			path=working_fpath.replace('''SSSSS''' ,			f"""{shard_id:05d}""").replace('''TTTTT''' ,			f"""{task_id:05d}""") ,			writer_batch_size=UpperCAmelCase__ ,			storage_options=UpperCAmelCase__ ,			embed_local_files=UpperCAmelCase__ ,			)
                  A__							=   pa.Table.from_batches([first_batch])
                  writer.write_table(UpperCAmelCase__)
                  for batch in it:
                         if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                                A__			,							A__							=   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
                                A__							=   writer_class(
                                    features=writer._features ,			path=working_fpath.replace('''SSSSS''' ,			f"""{shard_id:05d}""").replace('''TTTTT''' ,			f"""{task_id:05d}""") ,			writer_batch_size=UpperCAmelCase__ ,			storage_options=UpperCAmelCase__ ,			embed_local_files=UpperCAmelCase__ ,			)
                         A__							=   pa.Table.from_batches([batch])
                         writer.write_table(UpperCAmelCase__)

                  if writer._num_bytes > 0:
                         A__			,							A__							=   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(UpperCAmelCase__)):
                                A__							=   os.path.join(os.path.dirname(UpperCAmelCase__) ,			os.path.basename(UpperCAmelCase__))
                                shutil.move(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   (
               self.df.mapInArrow(UpperCAmelCase__ ,			'''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 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     "datasets.SplitGenerator" ,			UpperCAmelCase__				:     str = "arrow" ,			UpperCAmelCase__				:     Optional[Union[str, int]] = None ,			UpperCAmelCase__				:     Optional[int] = None ,			**UpperCAmelCase__				:     Union[str, Any] ,			)						->List[str]:





           '''simple docstring'''

           self._validate_cache_dir()

           A__							=   convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE)
           self._repartition_df_if_needed(UpperCAmelCase__)
           A__							=   not is_remote_filesystem(self._fs)
           A__							=   os.path.join if is_local else posixpath.join

           A__							=   '''-TTTTT-SSSSS-of-NNNNN'''
           A__							=   f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
           A__							=   path_join(self._output_dir ,			UpperCAmelCase__)

           A__							=   0
           A__							=   0
           A__							=   0
           A__							=   []
           A__							=   []

           for task_id, content in self._prepare_split_single(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__):
                  (
                      (
                      A__
                  )			,							(
                      A__
                  )			,							(
                      A__
                  )			,							(
                      A__
                  )			,							
                  )							=   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(UpperCAmelCase__)

           A__							=   total_num_examples
           A__							=   total_num_bytes

           # should rename everything at the end
           logger.debug(f"""Renaming {total_shards} shards.""")
           if total_shards > 1:
                  A__							=   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.
                  A__							=   self._fs

                  # use the -SSSSS-of-NNNNN pattern
                  def _rename_shard(
                      UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			):
                         rename(
                             UpperCAmelCase__ ,			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}""") ,			)

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     "datasets.SplitGenerator" ,			)						->SparkExamplesIterable:





           '''simple docstring'''

           return SparkExamplesIterable(self.df)








# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de


