
"""simple docstring"""


import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


A_    							=					logging.getLogger(__name__)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			# save results
			if os.path.exists(lowerCAmelCase__						):
						if os.path.exists(os.path.join(lowerCAmelCase__	,'''config.json'''						)						) and os.path.isfile(
						    os.path.join(lowerCAmelCase__	,'''config.json'''						)						):
									os.remove(os.path.join(lowerCAmelCase__	,'''config.json'''						)						)
						if os.path.exists(os.path.join(lowerCAmelCase__	,'''pytorch_model.bin'''						)						) and os.path.isfile(
						    os.path.join(lowerCAmelCase__	,'''pytorch_model.bin'''						)						):
									os.remove(os.path.join(lowerCAmelCase__	,'''pytorch_model.bin'''						)						)
			else:
						os.makedirs(lowerCAmelCase__						)
			model.save_pretrained(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  2
			if unlogit:
						lowerCamelCase_					=  torch.pow(lowerCAmelCase__	,lowerCAmelCase__						)
			lowerCamelCase_					=  p * torch.log(lowerCAmelCase__						)
			lowerCamelCase_					=  0
			return -plogp.sum(dim=-1						)






def  lowercase						(	lowerCAmelCase__						):
			logger.info('''lv, h >\t''' + '''\t'''.join(f"{x + 1}" for x in range(len(lowerCAmelCase__						)						)						)						)
			for row in range(len(lowerCAmelCase__						)						):
						if tensor.dtype != torch.long:
									logger.info(f"layer {row + 1}:\t" + '''\t'''.join(f"{x:.5f}" for x in tensor[row].cpu().data						)						)
						else:
									logger.info(f"layer {row + 1}:\t" + '''\t'''.join(f"{x:d}" for x in tensor[row].cpu().data						)						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=True	,lowerCAmelCase__=True	,lowerCAmelCase__=None	,lowerCAmelCase__=False						):
			lowerCamelCase_		, lowerCamelCase_					=  model.config.num_hidden_layers, model.config.num_attention_heads
			lowerCamelCase_					=  torch.zeros(lowerCAmelCase__	,lowerCAmelCase__						).to(args.device						)
			lowerCamelCase_					=  torch.zeros(lowerCAmelCase__	,lowerCAmelCase__						).to(args.device						)

			if head_mask is None:
						lowerCamelCase_					=  torch.ones(lowerCAmelCase__	,lowerCAmelCase__						).to(args.device						)

			head_mask.requires_grad_(requires_grad=lowerCAmelCase__						)
			# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
			if actually_pruned:
						lowerCamelCase_					=  None

			lowerCamelCase_					=  0.0
			lowerCamelCase_					=  0.0
			for step, inputs in enumerate(tqdm(lowerCAmelCase__	,desc='''Iteration'''	,disable=args.local_rank not in [-1, 0]						)						):
						lowerCamelCase_					=  tuple(t.to(args.device						) for t in inputs						)
						((lowerCamelCase_)		, )					=  inputs

						# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
						lowerCamelCase_					=  model(lowerCAmelCase__	,labels=lowerCAmelCase__	,head_mask=lowerCAmelCase__						)
						#  (loss), lm_logits, presents, (all hidden_states), (attentions)
						lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  (
						    outputs[0],
						    outputs[1],
						    outputs[-1],
						)  # Loss and logits are the first, attention the last
						loss.backward()  # Backpropagate to populate the gradients in the head mask
						total_loss += loss.detach().cpu().numpy()
						if compute_entropy:
									for layer, attn in enumerate(lowerCAmelCase__						):
												lowerCamelCase_					=  entropy(attn.detach()	,lowerCAmelCase__						)
												attn_entropy[layer] += masked_entropy.sum(-1						).sum(0						).sum(0						).detach()

						if compute_importance:
									head_importance += head_mask.grad.abs().detach()
						tot_tokens += torch.ones_like(lowerCAmelCase__						).float().detach().sum().data

			# Normalize
			attn_entropy /= tot_tokens
			head_importance /= tot_tokens
			# Layerwise importance normalization
			if not args.dont_normalize_importance_by_layer:
						lowerCamelCase_					=  2
						lowerCamelCase_					=  torch.pow(torch.pow(lowerCAmelCase__	,lowerCAmelCase__						).sum(-1						)	,1 / exponent						)
						head_importance /= norm_by_layer.unsqueeze(-1						) + 1E-20

			if not args.dont_normalize_global_importance:
						lowerCamelCase_					=  (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

			# Print matrices
			if compute_entropy:
						logger.info('''Attention entropies'''						)
						print_ad_tensor(lowerCAmelCase__						)
			if compute_importance:
						logger.info('''Head importance scores'''						)
						print_ad_tensor(lowerCAmelCase__						)
			logger.info('''Head ranked by importance scores'''						)
			lowerCamelCase_					=  torch.zeros(head_importance.numel()	,dtype=torch.long	,device=args.device						)
			lowerCamelCase_					=  torch.arange(
			    head_importance.numel()	,device=args.device						)
			lowerCamelCase_					=  head_ranks.view_as(lowerCAmelCase__						)
			print_ad_tensor(lowerCAmelCase__						)
			return attn_entropy, head_importance, total_loss






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__						)
			lowerCamelCase_					=  1 / loss  # instead of downsteam score use the LM loss
			logger.info('''Pruning: original score: %f, threshold: %f'''	,lowerCAmelCase__	,original_score * args.masking_threshold						)

			lowerCamelCase_					=  torch.ones_like(lowerCAmelCase__						)
			lowerCamelCase_					=  max(1	,int(new_head_mask.numel() * args.masking_amount						)						)

			lowerCamelCase_					=  original_score
			while current_score >= original_score * args.masking_threshold:
						lowerCamelCase_					=  new_head_mask.clone().detach()  # save current head mask
						# heads from least important to most - keep only not-masked heads
						lowerCamelCase_					=  float('''Inf'''						)
						lowerCamelCase_					=  head_importance.view(-1						).sort()[1]

						if len(lowerCAmelCase__						) <= num_to_mask:
									print('''BREAK BY num_to_mask'''						)
									break

						# mask heads
						lowerCamelCase_					=  current_heads_to_mask[:num_to_mask]
						logger.info('''Heads to mask: %s'''	,str(current_heads_to_mask.tolist()						)						)
						lowerCamelCase_					=  new_head_mask.view(-1						)
						lowerCamelCase_					=  0.0
						lowerCamelCase_					=  new_head_mask.view_as(lowerCAmelCase__						)
						lowerCamelCase_					=  new_head_mask.clone().detach()
						print_ad_tensor(lowerCAmelCase__						)

						# Compute metric and head importance again
						lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(
						    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__	,head_mask=lowerCAmelCase__						)
						lowerCamelCase_					=  1 / loss
						logger.info(
						    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''	,lowerCAmelCase__	,new_head_mask.sum()	,new_head_mask.sum() / new_head_mask.numel() * 100	,)

			logger.info('''Final head mask'''						)
			print_ad_tensor(lowerCAmelCase__						)
			np.save(os.path.join(args.output_dir	,'''head_mask.npy'''						)	,head_mask.detach().cpu().numpy()						)

			return head_mask






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  datetime.now()
			lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(
			    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__	,compute_importance=lowerCAmelCase__	,head_mask=lowerCAmelCase__						)
			lowerCamelCase_					=  1 / loss
			lowerCamelCase_					=  datetime.now() - before_time

			lowerCamelCase_					=  sum(p.numel() for p in model.parameters()						)
			lowerCamelCase_					=  {
			    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase__						)						)
			}

			for k, v in heads_to_prune.items():
						if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
									lowerCamelCase_					=  [
									    v,
									]

			assert sum(len(lowerCAmelCase__						) for h in heads_to_prune.values()						) == (1 - head_mask.long()).sum().item()
			model.prune_heads(lowerCAmelCase__						)
			lowerCamelCase_					=  sum(p.numel() for p in model.parameters()						)

			lowerCamelCase_					=  datetime.now()
			lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  compute_heads_importance(
			    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,compute_entropy=lowerCAmelCase__	,compute_importance=lowerCAmelCase__	,head_mask=lowerCAmelCase__	,actually_pruned=lowerCAmelCase__	,)

			lowerCamelCase_					=  1 / loss
			lowerCamelCase_					=  datetime.now() - before_time

			logger.info(
			    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''	,lowerCAmelCase__	,lowerCAmelCase__	,pruned_num_params / original_num_params * 100	,)
			logger.info('''Pruning: score with masking: %f score with pruning: %f'''	,lowerCAmelCase__	,lowerCAmelCase__						)
			logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''	,original_time / new_time * 100						)
			save_model(lowerCAmelCase__	,args.output_dir						)






def  lowercase						(	):
			lowerCamelCase_					=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--data_dir'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,required=lowerCAmelCase__	,help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''	,)
			parser.add_argument(
			    '''--model_name_or_path'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,required=lowerCAmelCase__	,help='''Path to pretrained model or model identifier from huggingface.co/models'''	,)
			parser.add_argument(
			    '''--output_dir'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,required=lowerCAmelCase__	,help='''The output directory where the model predictions and checkpoints will be written.'''	,)

			# Other parameters
			parser.add_argument(
			    '''--config_name'''	,default=''''''	,type=lowerCAmelCase__	,help='''Pretrained config name or path if not the same as model_name_or_path'''	,)
			parser.add_argument(
			    '''--tokenizer_name'''	,default=''''''	,type=lowerCAmelCase__	,help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''	,)
			parser.add_argument(
			    '''--cache_dir'''	,default=lowerCAmelCase__	,type=lowerCAmelCase__	,help='''Where do you want to store the pre-trained models downloaded from s3'''	,)
			parser.add_argument(
			    '''--data_subset'''	,type=lowerCAmelCase__	,default=-1	,help='''If > 0: limit the data to a subset of data_subset instances.'''						)
			parser.add_argument(
			    '''--overwrite_output_dir'''	,action='''store_true'''	,help='''Whether to overwrite data in output directory'''						)
			parser.add_argument(
			    '''--overwrite_cache'''	,action='''store_true'''	,help='''Overwrite the cached training and evaluation sets'''						)

			parser.add_argument(
			    '''--dont_normalize_importance_by_layer'''	,action='''store_true'''	,help='''Don\'t normalize importance score by layers'''						)
			parser.add_argument(
			    '''--dont_normalize_global_importance'''	,action='''store_true'''	,help='''Don\'t normalize all importance scores between 0 and 1'''	,)

			parser.add_argument(
			    '''--try_masking'''	,action='''store_true'''	,help='''Whether to try to mask head until a threshold of accuracy.'''						)
			parser.add_argument(
			    '''--masking_threshold'''	,default=0.9	,type=lowerCAmelCase__	,help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''	,)
			parser.add_argument(
			    '''--masking_amount'''	,default=0.1	,type=lowerCAmelCase__	,help='''Amount to heads to masking at each masking step.'''						)
			parser.add_argument('''--metric_name'''	,default='''acc'''	,type=lowerCAmelCase__	,help='''Metric to use for head masking.'''						)

			parser.add_argument(
			    '''--max_seq_length'''	,default=128	,type=lowerCAmelCase__	,help=(
			        '''The maximum total input sequence length after WordPiece tokenization. \n'''
			        '''Sequences longer than this will be truncated, sequences shorter padded.'''
			    )	,)
			parser.add_argument('''--batch_size'''	,default=1	,type=lowerCAmelCase__	,help='''Batch size.'''						)

			parser.add_argument('''--seed'''	,type=lowerCAmelCase__	,default=42						)
			parser.add_argument('''--local_rank'''	,type=lowerCAmelCase__	,default=-1	,help='''local_rank for distributed training on gpus'''						)
			parser.add_argument('''--no_cuda'''	,action='''store_true'''	,help='''Whether not to use CUDA when available'''						)
			parser.add_argument('''--server_ip'''	,type=lowerCAmelCase__	,default=''''''	,help='''Can be used for distant debugging.'''						)
			parser.add_argument('''--server_port'''	,type=lowerCAmelCase__	,default=''''''	,help='''Can be used for distant debugging.'''						)
			lowerCamelCase_					=  parser.parse_args()

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print('''Waiting for debugger attach'''						)
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)	,redirect_output=lowerCAmelCase__						)
						ptvsd.wait_for_attach()

			# Setup devices and distributed training
			if args.local_rank == -1 or args.no_cuda:
						lowerCamelCase_					=  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
						lowerCamelCase_					=  0 if args.no_cuda else torch.cuda.device_count()
			else:
						torch.cuda.set_device(args.local_rank						)
						lowerCamelCase_					=  torch.device('''cuda'''	,args.local_rank						)
						lowerCamelCase_					=  1
						torch.distributed.init_process_group(backend='''nccl'''						)  # Initializes the distributed backend

			# Setup logging
			logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN						)
			logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device	,args.n_gpu	,bool(args.local_rank != -1						)						)						)

			lowerCamelCase_					=  GPTaLMHeadModel.from_pretrained(args.model_name_or_path						)

			# Distributed and parallel training
			model.to(args.device						)
			if args.local_rank != -1:
						lowerCamelCase_					=  nn.parallel.DistributedDataParallel(
						    lowerCAmelCase__	,device_ids=[args.local_rank]	,output_device=args.local_rank	,find_unused_parameters=lowerCAmelCase__						)
			elif args.n_gpu > 1:
						lowerCamelCase_					=  nn.DataParallel(lowerCAmelCase__						)

			# Print/save training arguments
			os.makedirs(args.output_dir	,exist_ok=lowerCAmelCase__						)
			torch.save(lowerCAmelCase__	,os.path.join(args.output_dir	,'''run_args.bin'''						)						)
			logger.info('''Training/evaluation parameters %s'''	,lowerCAmelCase__						)

			# Prepare dataset
			lowerCamelCase_					=  np.concatenate(
			    [
			        np.loadtxt(args.data_dir	,dtype=np.intaa						),
			    ]						)
			lowerCamelCase_					=  (torch.from_numpy(lowerCAmelCase__						),)
			lowerCamelCase_					=  TensorDataset(*lowerCAmelCase__						)
			lowerCamelCase_					=  RandomSampler(lowerCAmelCase__						)
			lowerCamelCase_					=  DataLoader(lowerCAmelCase__	,sampler=lowerCAmelCase__	,batch_size=args.batch_size						)

			# Compute head entropy and importance score
			compute_heads_importance(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)

			# Try head masking (set heads to zero until the score goes under a threshole)
			# and head pruning (remove masked heads and see the effect on the network)
			if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
						lowerCamelCase_					=  mask_heads(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
						prune_heads(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)


if __name__ == "__main__":
						main()









'''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


lowerCAmelCase_      :  Any				     =  datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark







@dataclass
class      SCREAMING_SNAKE_CASE    (						datasets.BuilderConfig				):
      __magic_name__  :       Optional[datasets.Features]   							=					None



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					"pyspark.sql.DataFrame" ,    UpperCamelCase__    :					List[int] ,    ):




     """simple docstring"""


     import pyspark

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

     return generate_fn







class      SCREAMING_SNAKE_CASE    (						_BaseExamplesIterable				):






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


           '''simple docstring'''

           a_     :							Tuple     						=  df
           a_     :							Dict     						=  partition_order or range(self.df.rdd.getNumPartitions()       )
           a_     :							Dict     						=  _generate_iterable_examples(self.df						,						self.partition_order       )






      def __iter__( self   :  Optional[Any]       ):


           '''simple docstring'''

           yield from self.generate_examples_fn()






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  np.random.Generator       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  list(range(self.df.rdd.getNumPartitions()       )       )
           generator.shuffle(lowercase__       )
           return SparkExamplesIterable(self.df						,						partition_order=lowercase__       )






      def 							lowercase_						( self   :  Any						,						lowercase__   :  int						,						lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.split_shard_indices_by_worker(lowercase__						,						lowercase__       )
           return SparkExamplesIterable(self.df						,						partition_order=lowercase__       )






      @property
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           return len(self.partition_order       )







class      SCREAMING_SNAKE_CASE    (						datasets.DatasetBuilder				):
      __magic_name__  :       Optional[int]   							=					SparkConfig






      def __init__( self   :  Optional[Any]						,						lowercase__   :  "pyspark.sql.DataFrame"						,						lowercase__   :  str = None						,						lowercase__   :  str = None						,						**lowercase__   :  Dict						,						):


           '''simple docstring'''

           import pyspark

           a_     :							Dict     						=  pyspark.sql.SparkSession.builder.getOrCreate()
           a_     :							List[str]     						=  df
           a_     :							List[str]     						=  working_dir

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






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           def create_cache_and_write_probe(lowercase__   :  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=lowercase__       )
                a_     :							List[Any]     						=  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:
                a_     :							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 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  datasets.download.download_manager.DownloadManager       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Any						,						lowercase__   :  int       ):


           '''simple docstring'''

           import pyspark

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

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






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  str						,						lowercase__   :  int						,						):


           '''simple docstring'''

           import pyspark

           a_     :							str     						=  ParquetWriter if file_format == """parquet""" else ArrowWriter
           a_     :							int     						=  os.path.join(self._working_dir						,						os.path.basename(lowercase__       )       ) if self._working_dir else fpath
           a_     :							Optional[int]     						=  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_     :							int     						=  self.config.features
           a_     :							List[str]     						=  self._writer_batch_size
           a_     :							str     						=  self._fs.storage_options

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

                if writer._num_bytes > 0:
                     a_						,     a_     :							Any     						=  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__       )       ):
                          a_     :							str     						=  os.path.join(os.path.dirname(lowercase__       )						,						os.path.basename(lowercase__       )       )
                          shutil.move(lowercase__						,						lowercase__       )

           a_     :							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 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  "datasets.SplitGenerator"						,						lowercase__   :  str = "arrow"						,						lowercase__   :  Optional[Union[str, int]] = None						,						lowercase__   :  Optional[int] = None						,						**lowercase__   :  Optional[int]						,						):


           '''simple docstring'''

           self._validate_cache_dir()

           a_     :							Dict     						=  convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE       )
           self._repartition_df_if_needed(lowercase__       )
           a_     :							Any     						=  not is_remote_filesystem(self._fs       )
           a_     :							str     						=  os.path.join if is_local else posixpath.join

           a_     :							Optional[Any]     						=  """-TTTTT-SSSSS-of-NNNNN"""
           a_     :							int     						=  F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
           a_     :							Optional[Any]     						=  path_join(self._output_dir						,						lowercase__       )

           a_     :							Dict     						=  0
           a_     :							Optional[Any]     						=  0
           a_     :							Dict     						=  0
           a_     :							List[str]     						=  []
           a_     :							Dict     						=  []

           for task_id, content in self._prepare_split_single(lowercase__						,						lowercase__						,						lowercase__       ):
                (
                    (
                    a_
                )						,     (
                    a_
                )						,     (
                    a_
                )						,     (
                    a_
                )						,     
                )     :							Tuple     						=  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__       )

           a_     :							List[Any]     						=  total_num_examples
           a_     :							int     						=  total_num_bytes

           # should rename everything at the end
           logger.debug(F"Renaming {total_shards} shards."       )
           if total_shards > 1:
                a_     :							Dict     						=  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_     :							List[str]     						=  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}"       )						,						)

                a_     :							int     						=  []
                a_     :							List[str]     						=  0
                for i in range(len(lowercase__       )       ):
                     a_						,     a_     :							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
                a_     :							int     						=  0
                a_     :							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 							lowercase_						( self   :  Tuple						,						lowercase__   :  "datasets.SplitGenerator"						,						):


           '''simple docstring'''

           return SparkExamplesIterable(self.df       )
