
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


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

if TYPE_CHECKING:
   import pyspark







@dataclass
class 					lowerCamelCase		(  datasets.BuilderConfig  ):
     UpperCAmelCase__    :							Optional[datasets.Features]									=		None



def 						lowercase_      (       A__			,		A__			,		)							->							Optional[Any]:
    """simple docstring"""

    import pyspark

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

    return generate_fn







class 					lowerCamelCase		(  _BaseExamplesIterable  ):





     def __init__(self				:   Tuple    ,   _A				:   "pyspark.sql.DataFrame"    ,   _A				:   Any=None    ,   )						->						Optional[Any]:
         snake_case								= df
         snake_case								= partition_order or range(self.df.rdd.getNumPartitions()       )
         snake_case								= _generate_iterable_examples(self.df    ,   self.partition_order       )





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





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   np.random.Generator       )						->						"SparkExamplesIterable":
         snake_case								= list(range(self.df.rdd.getNumPartitions()       )       )
         generator.shuffle(_A       )
         return SparkExamplesIterable(self.df    ,   partition_order=_A       )





     def 		UpperCAmelCase(self				:   str    ,   _A				:   int    ,   _A				:   int       )						->						"SparkExamplesIterable":
         snake_case								= self.split_shard_indices_by_worker(_A    ,   _A       )
         return SparkExamplesIterable(self.df    ,   partition_order=_A       )





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







class 					lowerCamelCase		(  datasets.DatasetBuilder  ):
     UpperCAmelCase__    :							str									=		SparkConfig





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

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

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





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						List[Any]:
         # Returns the path of the created file.
         def create_cache_and_write_probe(_A				:   str       ):
             # 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=_A       )
             snake_case								= 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(_A    ,   "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:
             snake_case								= (
                 self._spark.sparkContext.parallelize(range(1       )    ,   1       ).mapPartitions(_A       ).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				:   str       )						->						Optional[int]:
         return datasets.DatasetInfo(features=self.config.features       )





     def 		UpperCAmelCase(self				:   int    ,   _A				:   datasets.download.download_manager.DownloadManager       )						->						str:
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN       )]





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   Optional[Any]       )						->						Optional[int]:
         import pyspark

         def get_arrow_batch_size(_A				:   Union[str, Any]       ):
             for batch in it:
                 yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}       )

         snake_case								= self.df.count()
         snake_case								= df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
         # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
         snake_case								= (
             self.df.limit(_A       )
             .repartition(1       )
             .mapInArrow(_A    ,   "batch_bytes: long"       )
             .agg(pyspark.sql.functions.sum("batch_bytes"       ).alias("sample_bytes"       )       )
             .collect()[0]
             .sample_bytes
             / sample_num_rows
         )
         snake_case								= approx_bytes_per_row * df_num_rows
         if approx_total_size > max_shard_size:
             # Make sure there is at least one row per partition.
             snake_case								= min(_A    ,   int(approx_total_size / max_shard_size       )       )
             snake_case								= self.df.repartition(_A       )





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   str    ,   _A				:   str    ,   _A				:   int    ,   )						->						Iterable[Tuple[int, bool, Union[int, tuple]]]:
         import pyspark

         snake_case								= ParquetWriter if file_format == "parquet" else ArrowWriter
         snake_case								= os.path.join(self._working_dir    ,   os.path.basename(_A       )       ) if self._working_dir else fpath
         snake_case								= 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.
         snake_case								= self.config.features
         snake_case								= self._writer_batch_size
         snake_case								= self._fs.storage_options

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

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

         snake_case								= (
             self.df.mapInArrow(_A    ,   "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				:   str    ,   _A				:   "datasets.SplitGenerator"    ,   _A				:   str = "arrow"    ,   _A				:   Optional[Union[str, int]] = None    ,   _A				:   Optional[int] = None    ,   **_A				:   Any    ,   )						->						List[Any]:
         self._validate_cache_dir()

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

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

         snake_case								= 0
         snake_case								= 0
         snake_case								= 0
         snake_case								= []
         snake_case								= []

         for task_id, content in self._prepare_split_single(_A    ,   _A    ,   _A       ):
             (
                 (
                 snake_case
             )					,							(
                 snake_case
             )					,							(
                 snake_case
             )					,							(
                 snake_case
             )					,							
             )								= 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(_A       )

         snake_case								= total_num_examples
         snake_case								= total_num_bytes

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

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

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





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   "datasets.SplitGenerator"    ,   )						->						SparkExamplesIterable:
         return SparkExamplesIterable(self.df       )



import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_A   =			"bert-base-cased"
_A   =			"google/pegasus-xsum"
_A   =			[" Sam ate lunch today.", "Sams lunch ingredients."]
_A   =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_A   =			"patrickvonplaten/t5-tiny-random"
_A   =			"sshleifer/bart-tiny-random"
_A   =			"sshleifer/tiny-mbart"
_A   =			"sshleifer/tiny-marian-en-de"



def 						lowercase_      (       A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= "\n".join(A__		)
    Path(A__		).open("w"		).writelines(A__		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(A__			,		F'{split}.source'		)			,		A__		)
        _dump_articles(os.path.join(A__			,		F'{split}.target'		)			,		A__		)
    return tmp_dir







class 					lowerCamelCase		(  A_  ):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     @slow
     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[str]       )						->						Optional[int]:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= 8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         snake_case					,							snake_case								= "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   src_lang=_A    ,   tgt_lang=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert isinstance(_A    ,   _A       )
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_src_len
             # show that targets are the same len
             assert batch["labels"].shape[1] == max_tgt_len
             if tok_name != MBART_TINY:
                 continue
             # check language codes in correct place
             snake_case								= shift_tokens_right(batch["labels"]    ,   tokenizer.pad_token_id       )
             assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
             assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

             break  # No need to test every batch





     @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
     def 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Dict:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= LegacySeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=2_0    ,   max_target_length=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_len_source
             assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
             # show that targets were truncated
             assert batch["labels"].shape[1] == trunc_target  # Truncated
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         snake_case								= AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         snake_case								= tmp_dir.joinpath("train.source"       ).open().readlines()
         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(_A    ,   _A    ,   1_2_8    ,   _A       )
         snake_case								= {x.name for x in tmp_dir.iterdir()}
         snake_case								= {x.name for x in save_dir.iterdir()}
         snake_case								= save_dir.joinpath("train.source"       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(_A       ) < len(_A       )
         assert len(_A       ) == 1
         assert len(packed_examples[0]       ) == sum(len(_A       ) for x in orig_examples       )
         assert orig_paths == new_paths





     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,   reason="This test requires fairseq"       )
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         if not FAIRSEQ_AVAILABLE:
             return
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=6_4       )
         snake_case								= 6_4
         snake_case								= ds.make_dynamic_sampler(_A    ,   required_batch_size_multiple=_A       )
         snake_case								= [len(_A       ) for x in batch_sampler]
         assert len(set(_A       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(_A       ) == len(_A       )  # no dropped or added examples
         snake_case								= DataLoader(_A    ,   batch_sampler=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= []
         snake_case								= []
         for batch in data_loader:
             snake_case								= batch["input_ids"].shape
             snake_case								= src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             snake_case								= np.product(batch["input_ids"].shape       )
             num_src_per_batch.append(_A       )
             if num_src_tokens > (max_tokens * 1.1):
                 failures.append(_A       )
         assert num_src_per_batch[0] == max(_A       )
         if failures:
             raise AssertionError(f'too many tokens in {len(_A       )} batches'       )





     def 		UpperCAmelCase(self				:   int       )						->						str:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=5_1_2       )
         snake_case								= 2
         snake_case								= ds.make_sortish_sampler(_A    ,   shuffle=_A       )

         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2    ,   sampler=_A       )

         snake_case								= tokenizer.pad_token_id

         def count_pad_tokens(_A				:   Dict    ,   _A				:   Union[str, Any]="input_ids"       ):
             return [batch[k].eq(_A       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(_A    ,   k="labels"       )       ) < sum(count_pad_tokens(_A    ,   k="labels"       )       )
         assert sum(count_pad_tokens(_A       )       ) < sum(count_pad_tokens(_A       )       )
         assert len(_A       ) == len(_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=1_0_0_0    ,   _A				:   Optional[int]=1_2_8       )						->						List[Any]:
         if os.getenv("USE_REAL_DATA"    ,   _A       ):
             snake_case								= "examples/seq2seq/wmt_en_ro"
             snake_case								= max_len * 2 * 6_4
             if not Path(_A       ).joinpath("train.len"       ).exists():
                 save_len_file(_A    ,   _A       )
         else:
             snake_case								= "examples/seq2seq/test_data/wmt_en_ro"
             snake_case								= max_len * 4
             save_len_file(_A    ,   _A       )

         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   n_obs=_A    ,   )
         return ds, max_tokens, tokenizer





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset()
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=_A       )       )
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=1    ,   add_extra_examples=_A       )       )
         assert idsa.intersection(_A       ) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= AutoTokenizer.from_pretrained(_A    ,   use_fast=_A       )
         if tok_name == MBART_TINY:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   src_lang="EN"    ,   tgt_lang="FR"    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
             assert len(_A       ) == 1 if tok_name == BART_TINY else len(_A       ) == 0

