'''simple docstring'''




import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase		:				Dict	       =		logging.get_logger(__name__)

set_seed(770)


_lowercase		:				List[Any]	       =		{
    "c_attn": "att_proj",
    "c_proj": "out_proj",
    "c_fc": "in_proj",
    "transformer.": "",
    "h.": "layers.",
    "ln_1": "layernorm_1",
    "ln_2": "layernorm_2",
    "ln_f": "layernorm_final",
    "wpe": "position_embeds_layer",
    "wte": "input_embeds_layer",
}


_lowercase		:				Dict	       =		{
    "text_small": {
        "repo_id": "suno/bark",
        "file_name": "text.pt",
    },
    "coarse_small": {
        "repo_id": "suno/bark",
        "file_name": "coarse.pt",
    },
    "fine_small": {
        "repo_id": "suno/bark",
        "file_name": "fine.pt",
    },
    "text": {
        "repo_id": "suno/bark",
        "file_name": "text_2.pt",
    },
    "coarse": {
        "repo_id": "suno/bark",
        "file_name": "coarse_2.pt",
    },
    "fine": {
        "repo_id": "suno/bark",
        "file_name": "fine_2.pt",
    },
}

_lowercase		:				Union[str, Any]	       =		os.path.dirname(os.path.abspath(__file__))
_lowercase		:				int	       =		os.path.join(os.path.expanduser("~"), ".cache")
_lowercase		:				List[str]	       =		os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Any=False				)    ->   int:
							lowercase_      :    Tuple       		= model_type
							if use_small:
														key += "_small"
							return os.path.join(UpperCAmelCase__   ,   REMOTE_MODEL_PATHS[key]["""file_name"""]				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   int:
							os.makedirs(UpperCAmelCase__   ,   exist_ok=UpperCAmelCase__				)
							hf_hub_download(repo_id=UpperCAmelCase__   ,   filename=UpperCAmelCase__   ,   local_dir=UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Union[str, Any]=False   ,   UpperCAmelCase__ :      Union[str, Any]="text"				)    ->   str:
							if model_type == "text":
														lowercase_      :    List[Any]       		= BarkSemanticModel
														lowercase_      :    str       		= BarkSemanticConfig
														lowercase_      :    Union[str, Any]       		= BarkSemanticGenerationConfig
							elif model_type == "coarse":
														lowercase_      :    Union[str, Any]       		= BarkCoarseModel
														lowercase_      :    List[str]       		= BarkCoarseConfig
														lowercase_      :    int       		= BarkCoarseGenerationConfig
							elif model_type == "fine":
														lowercase_      :    Tuple       		= BarkFineModel
														lowercase_      :    int       		= BarkFineConfig
														lowercase_      :    Optional[int]       		= BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							lowercase_      :    List[str]       		= F'''{model_type}_small''' if use_small else model_type
							lowercase_      :    Optional[int]       		= REMOTE_MODEL_PATHS[model_key]
							if not os.path.exists(UpperCAmelCase__				):
														logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''				)
														_download(model_info["""repo_id"""]   ,   model_info["""file_name"""]				)
							lowercase_      :    int       		= torch.load(UpperCAmelCase__   ,   map_location=UpperCAmelCase__				)
							# this is a hack
							lowercase_      :    int       		= checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														lowercase_      :    Optional[Any]       		= model_args["""vocab_size"""]
														lowercase_      :    Dict       		= model_args["""vocab_size"""]
														del model_args["vocab_size"]

							# convert Bark model arguments to HF Bark model arguments
							lowercase_      :    Dict       		= model_args.pop("""n_head"""				)
							lowercase_      :    Optional[int]       		= model_args.pop("""n_embd"""				)
							lowercase_      :    Optional[Any]       		= model_args.pop("""n_layer"""				)

							lowercase_      :    Union[str, Any]       		= ConfigClass(**checkpoint["""model_args"""]				)
							lowercase_      :    List[str]       		= ModelClass(config=UpperCAmelCase__				)
							lowercase_      :    int       		= GenerationConfigClass()

							lowercase_      :    List[Any]       		= model_generation_config
							lowercase_      :    str       		= checkpoint["""model"""]
							# fixup checkpoint
							lowercase_      :    Any       		= """_orig_mod."""
							for k, v in list(state_dict.items()				):
														if k.startswith(UpperCAmelCase__				):
																					# replace part of the key with corresponding layer name in HF implementation
																					lowercase_      :    str       		= k[len(UpperCAmelCase__				) :]
																					for old_layer_name in new_layer_name_dict:
																												lowercase_      :    str       		= new_k.replace(UpperCAmelCase__   ,   new_layer_name_dict[old_layer_name]				)

																					lowercase_      :    Dict       		= state_dict.pop(UpperCAmelCase__				)

							lowercase_      :    str       		= set(state_dict.keys()				) - set(model.state_dict().keys()				)
							lowercase_      :    str       		= {k for k in extra_keys if not k.endswith(""".attn.bias"""				)}
							lowercase_      :    List[Any]       		= set(model.state_dict().keys()				) - set(state_dict.keys()				)
							lowercase_      :    List[str]       		= {k for k in missing_keys if not k.endswith(""".attn.bias"""				)}
							if len(UpperCAmelCase__				) != 0:
														raise ValueError(F'''extra keys found: {extra_keys}'''				)
							if len(UpperCAmelCase__				) != 0:
														raise ValueError(F'''missing keys: {missing_keys}'''				)
							model.load_state_dict(UpperCAmelCase__   ,   strict=UpperCAmelCase__				)
							lowercase_      :    Dict       		= model.num_parameters(exclude_embeddings=UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= checkpoint["""best_val_loss"""].item()
							logger.info(F'''model loaded: {round(n_params/1e6   ,   1				)}M params, {round(UpperCAmelCase__   ,   3				)} loss'''				)
							model.eval()
							model.to(UpperCAmelCase__				)
							del checkpoint, state_dict

							return model




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Optional[int]=False   ,   UpperCAmelCase__ :      Dict="text"				)    ->   str:
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							lowercase_      :    str       		= """cpu"""  # do conversion on cpu

							lowercase_      :    List[str]       		= _get_ckpt_path(UpperCAmelCase__   ,   use_small=UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= _load_model(UpperCAmelCase__   ,   UpperCAmelCase__   ,   model_type=UpperCAmelCase__   ,   use_small=UpperCAmelCase__				)

							# load bark initial model
							lowercase_      :    Union[str, Any]       		= _bark_load_model(UpperCAmelCase__   ,   """cpu"""   ,   model_type=UpperCAmelCase__   ,   use_small=UpperCAmelCase__				)

							if model_type == "text":
														lowercase_      :    List[Any]       		= bark_model["""model"""]

							if model.num_parameters(exclude_embeddings=UpperCAmelCase__				) != bark_model.get_num_params():
														raise ValueError("""initial and new models don't have the same number of parameters"""				)

							# check if same output as the bark model
							lowercase_      :    str       		= 5
							lowercase_      :    Any       		= 10

							if model_type in ["text", "coarse"]:
														lowercase_      :    List[Any]       		= torch.randint(256   ,   (batch_size, sequence_length)   ,   dtype=torch.int				)
														lowercase_      :    Tuple       		= bark_model(UpperCAmelCase__				)[0]

														lowercase_      :    List[Any]       		= model(UpperCAmelCase__				)

														# take last logits
														lowercase_      :    Dict       		= output_new_model_total.logits[:, [-1], :]

							else:
														lowercase_      :    Dict       		= 3
														lowercase_      :    Dict       		= 8
														lowercase_      :    Optional[Any]       		= torch.randint(256   ,   (batch_size, sequence_length, n_codes_total)   ,   dtype=torch.int				)

														lowercase_      :    int       		= model(UpperCAmelCase__   ,   UpperCAmelCase__				)
														lowercase_      :    Tuple       		= bark_model(UpperCAmelCase__   ,   UpperCAmelCase__				)

														lowercase_      :    Optional[int]       		= output_new_model_total.logits

							# output difference should come from the difference of self-attention implementation design
							if output_new_model.shape != output_old_model.shape:
														raise ValueError("""initial and new outputs don't have the same shape"""				)
							if (output_new_model - output_old_model).abs().max().item() > 1e-3:
														raise ValueError("""initial and new outputs are not equal"""				)

							Path(UpperCAmelCase__				).mkdir(exist_ok=UpperCAmelCase__				)
							model.save_pretrained(UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      Tuple   ,   )    ->   List[str]:
							lowercase_      :    Dict       		= os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__				)

							lowercase_      :    str       		= BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase__   ,   """config.json"""				)				)
							lowercase_      :    Optional[Any]       		= BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase__   ,   """config.json"""				)				)
							lowercase_      :    List[Any]       		= BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase__   ,   """config.json"""				)				)
							lowercase_      :    Any       		= EncodecConfig.from_pretrained("""facebook/encodec_24khz"""				)

							lowercase_      :    List[str]       		= BarkSemanticModel.from_pretrained(UpperCAmelCase__				)
							lowercase_      :    str       		= BarkCoarseModel.from_pretrained(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= BarkFineModel.from_pretrained(UpperCAmelCase__				)
							lowercase_      :    Tuple       		= EncodecModel.from_pretrained("""facebook/encodec_24khz"""				)

							lowercase_      :    Union[str, Any]       		= BarkConfig.from_sub_model_configs(
							    UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)

							lowercase_      :    List[Any]       		= BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config   ,   coarseAcoustic.generation_config   ,   fineAcoustic.generation_config				)

							lowercase_      :    Union[str, Any]       		= BarkModel(UpperCAmelCase__				)

							lowercase_      :    str       		= semantic
							lowercase_      :    Optional[int]       		= coarseAcoustic
							lowercase_      :    Tuple       		= fineAcoustic
							lowercase_      :    Union[str, Any]       		= codec

							lowercase_      :    Tuple       		= bark_generation_config

							Path(UpperCAmelCase__				).mkdir(exist_ok=UpperCAmelCase__				)
							bark.save_pretrained(UpperCAmelCase__   ,   repo_id=UpperCAmelCase__   ,   push_to_hub=UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				int	       =		argparse.ArgumentParser()
						# Required parameters

						parser.add_argument("model_type", type=str, help="text, coarse or fine.")
						parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")

						_lowercase		:				int	       =		parser.parse_args()

						load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)






'''simple docstring'''




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		:				str	       =		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
						import pyspark





@dataclass
class 					__magic_name__		(					datasets.BuilderConfig):
			UpperCamelCase__       = None




def 							lowerCamelCase			(   UpperCAmelCase__ :      "pyspark.sql.DataFrame"   ,   UpperCAmelCase__ :      List[int]   ,   )    ->   str:
							import pyspark

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

							return generate_fn





class 					__magic_name__		(					_BaseExamplesIterable):







			def __init__(			self       :						int       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						Optional[int]=None       ,    ):
										lowercase_      :    Dict       		= df
										lowercase_      :    Optional[Any]       		= partition_order or range(self.df.rdd.getNumPartitions()					)
										lowercase_      :    Optional[Any]       		= _generate_iterable_examples(self.df       ,    self.partition_order					)







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						np.random.Generator					):
										lowercase_      :    str       		= list(range(self.df.rdd.getNumPartitions()					)					)
										generator.shuffle(lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										lowercase_      :    str       		= self.split_shard_indices_by_worker(lowercase_       ,    lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return len(self.partition_order					)





class 					__magic_name__		(					datasets.DatasetBuilder):
			UpperCamelCase__       = SparkConfig







			def __init__(			self       :						Tuple       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						str = None       ,    lowercase_       :						str = None       ,    **lowercase_       :						str       ,    ):
										import pyspark

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

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										# Returns the path of the created file.
										def create_cache_and_write_probe(lowercase_       :						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=lowercase_					)
																	lowercase_      :    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:
																	lowercase_      :    str       		= (
																	    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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										return datasets.DatasetInfo(features=self.config.features					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						datasets.download.download_manager.DownloadManager					):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										import pyspark

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

										lowercase_      :    Union[str, Any]       		= self.df.count()
										lowercase_      :    Union[str, 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.
										lowercase_      :    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
										)
										lowercase_      :    List[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.
																	lowercase_      :    Any       		= min(lowercase_       ,    int(approx_total_size / max_shard_size					)					)
																	lowercase_      :    Any       		= self.df.repartition(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str       ,    lowercase_       :						str       ,    lowercase_       :						int       ,    ):
										import pyspark

										lowercase_      :    Any       		= ParquetWriter if file_format == """parquet""" else ArrowWriter
										lowercase_      :    Dict       		= os.path.join(self._working_dir       ,    os.path.basename(lowercase_					)					) if self._working_dir else fpath
										lowercase_      :    Optional[Any]       		= 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.
										lowercase_      :    Tuple       		= self.config.features
										lowercase_      :    Any       		= self._writer_batch_size
										lowercase_      :    List[str]       		= self._fs.storage_options

										def write_arrow(lowercase_       :						str					):
																	# Within the same SparkContext, no two task attempts will share the same attempt ID.
																	lowercase_      :    List[str]       		= pyspark.TaskContext().taskAttemptId()
																	lowercase_      :    Dict       		= 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"""]       ,    )
																	lowercase_      :    int       		= 0
																	lowercase_      :    List[Any]       		= 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_       ,    )
																	lowercase_      :    Optional[Any]       		= 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:
																															lowercase_		,		lowercase_      :    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
																															lowercase_      :    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_       ,    )
																								lowercase_      :    List[str]       		= pa.Table.from_batches([batch]					)
																								writer.write_table(lowercase_					)

																	if writer._num_bytes > 0:
																								lowercase_		,		lowercase_      :    str       		= 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_					)					):
																															lowercase_      :    Optional[Any]       		= os.path.join(os.path.dirname(lowercase_					)       ,    os.path.basename(lowercase_					)					)
																															shutil.move(lowercase_       ,    lowercase_					)

										lowercase_      :    Union[str, Any]       		= (
										    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 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						"datasets.SplitGenerator"       ,    lowercase_       :						str = "arrow"       ,    lowercase_       :						Optional[Union[str, int]] = None       ,    lowercase_       :						Optional[int] = None       ,    **lowercase_       :						List[str]       ,    ):
										self._validate_cache_dir()

										lowercase_      :    int       		= convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
										self._repartition_df_if_needed(lowercase_					)
										lowercase_      :    Tuple       		= not is_remote_filesystem(self._fs					)
										lowercase_      :    int       		= os.path.join if is_local else posixpath.join

										lowercase_      :    Dict       		= """-TTTTT-SSSSS-of-NNNNN"""
										lowercase_      :    Dict       		= f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
										lowercase_      :    Optional[int]       		= path_join(self._output_dir       ,    lowercase_					)

										lowercase_      :    Any       		= 0
										lowercase_      :    Tuple       		= 0
										lowercase_      :    int       		= 0
										lowercase_      :    Dict       		= []
										lowercase_      :    Union[str, Any]       		= []

										for task_id, content in self._prepare_split_single(lowercase_       ,    lowercase_       ,    lowercase_					):
																	(
																	    (
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		
																	)      :    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_					)

										lowercase_      :    List[str]       		= total_num_examples
										lowercase_      :    int       		= total_num_bytes

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

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

																	lowercase_      :    Union[str, Any]       		= []
																	lowercase_      :    Tuple       		= 0
																	for i in range(len(lowercase_					)					):
																								lowercase_		,		lowercase_      :    List[Any]       		= 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
																	lowercase_      :    List[str]       		= 0
																	lowercase_      :    Optional[Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						"datasets.SplitGenerator"       ,    ):
										return SparkExamplesIterable(self.df					)





