


"""simple docstring"""


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

A     :   Dict			    =			[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

A     :   Dict			    =			[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

A     :   List[Any]			    =			[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
	# loop over downblocks/upblocks

	for j in range(2):
		# loop over resnets/attentions for downblocks
		A     :   Any			    =			f'''down_blocks.{i}.resnets.{j}.'''
		A     :   int			    =			f'''input_blocks.{3*i + j + 1}.0.'''
		unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

		if i < 3:
			# no attention layers in down_blocks.3
			A     :   int			    =			f'''down_blocks.{i}.attentions.{j}.'''
			A     :   Optional[Any]			    =			f'''input_blocks.{3*i + j + 1}.1.'''
			unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

	for j in range(3):
		# loop over resnets/attentions for upblocks
		A     :   int			    =			f'''up_blocks.{i}.resnets.{j}.'''
		A     :   Dict			    =			f'''output_blocks.{3*i + j}.0.'''
		unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

		if i > 0:
			# no attention layers in up_blocks.0
			A     :   List[str]			    =			f'''up_blocks.{i}.attentions.{j}.'''
			A     :   int			    =			f'''output_blocks.{3*i + j}.1.'''
			unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

	if i < 3:
		# no downsample in down_blocks.3
		A     :   Tuple			    =			f'''down_blocks.{i}.downsamplers.0.conv.'''
		A     :   Optional[int]			    =			f'''input_blocks.{3*(i+1)}.0.op.'''
		unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

		# no upsample in up_blocks.3
		A     :   int			    =			f'''up_blocks.{i}.upsamplers.0.'''
		A     :   Tuple			    =			f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A     :   List[Any]			    =			"mid_block.attentions.0."
A     :   str			    =			"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	A     :   Optional[Any]			    =			f'''mid_block.resnets.{j}.'''
	A     :   str			    =			f'''middle_block.{2*j}.'''
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			{k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						__lowerCAmelCase				    =			sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												__lowerCAmelCase				    =			v.replace(__lowerCamelCase    , __lowerCamelCase      )
									__lowerCAmelCase				    =			v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									__lowerCAmelCase				    =			v.replace(__lowerCamelCase    , __lowerCamelCase      )
						__lowerCAmelCase				    =			v
			__lowerCAmelCase				    =			{v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

A     :   List[Any]			    =			[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
	# down_blocks have two resnets
	for j in range(2):
		A     :   int			    =			f'''encoder.down_blocks.{i}.resnets.{j}.'''
		A     :   str			    =			f'''encoder.down.{i}.block.{j}.'''
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		A     :   Optional[Any]			    =			f'''down_blocks.{i}.downsamplers.0.'''
		A     :   int			    =			f'''down.{i}.downsample.'''
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		A     :   List[Any]			    =			f'''up_blocks.{i}.upsamplers.0.'''
		A     :   Union[str, Any]			    =			f'''up.{3-i}.upsample.'''
		vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

	# up_blocks have three resnets
	# also, up blocks in hf are numbered in reverse from sd
	for j in range(3):
		A     :   Optional[int]			    =			f'''decoder.up_blocks.{i}.resnets.{j}.'''
		A     :   Tuple			    =			f'''decoder.up.{3-i}.block.{j}.'''
		vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
	A     :   Union[str, Any]			    =			f'''mid_block.resnets.{i}.'''
	A     :   Dict			    =			f'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A     :   Optional[Any]			    =			[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return w.reshape(*w.shape    , 1    , 1      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			{k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									__lowerCAmelCase				    =			v.replace(__lowerCamelCase    , __lowerCamelCase      )
						__lowerCAmelCase				    =			v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												__lowerCAmelCase				    =			v.replace(__lowerCamelCase    , __lowerCamelCase      )
									__lowerCAmelCase				    =			v
			__lowerCAmelCase				    =			{v: vae_state_dict[k] for k, v in mapping.items()}
			__lowerCAmelCase				    =			["q", "k", "v", "proj_out"]
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if f"mid.attn_1.{weight_name}.weight" in k:
												print(f"Reshaping {k} for SD format"      )
												__lowerCAmelCase				    =			reshape_weight_for_sd(__lowerCamelCase      )
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


A     :   Union[str, Any]			    =			[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
A     :   Union[str, Any]			    =			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A     :   Tuple			    =			re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A     :   Dict			    =			{"q": 0, "k": 1, "v": 2}


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			{}
			__lowerCAmelCase				    =			{}
			__lowerCAmelCase				    =			{}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith(".self_attn.q_proj.weight"      )
						    or k.endswith(".self_attn.k_proj.weight"      )
						    or k.endswith(".self_attn.v_proj.weight"      )
						):
									__lowerCAmelCase				    =			k[: -len(".q_proj.weight"      )]
									__lowerCAmelCase				    =			k[-len("q_proj.weight"      )]
									if k_pre not in capture_qkv_weight:
												__lowerCAmelCase				    =			[None, None, None]
									__lowerCAmelCase				    =			v
									continue

						if (
						    k.endswith(".self_attn.q_proj.bias"      )
						    or k.endswith(".self_attn.k_proj.bias"      )
						    or k.endswith(".self_attn.v_proj.bias"      )
						):
									__lowerCAmelCase				    =			k[: -len(".q_proj.bias"      )]
									__lowerCAmelCase				    =			k[-len("q_proj.bias"      )]
									if k_pre not in capture_qkv_bias:
												__lowerCAmelCase				    =			[None, None, None]
									__lowerCAmelCase				    =			v
									continue

						__lowerCAmelCase				    =			textenc_pattern.sub(lambda _UpperCamelCase      : protected[re.escape(m.group(0      )      )]    , __lowerCamelCase      )
						__lowerCAmelCase				    =			v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
						__lowerCAmelCase				    =			textenc_pattern.sub(lambda _UpperCamelCase      : protected[re.escape(m.group(0      )      )]    , __lowerCamelCase      )
						__lowerCAmelCase				    =			torch.cat(__lowerCamelCase      )

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
						__lowerCAmelCase				    =			textenc_pattern.sub(lambda _UpperCamelCase      : protected[re.escape(m.group(0      )      )]    , __lowerCamelCase      )
						__lowerCAmelCase				    =			torch.cat(__lowerCamelCase      )

			return new_state_dict


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return text_enc_dict


if __name__ == "__main__":
	A     :   str			    =			argparse.ArgumentParser()

	parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
	parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
	parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
	parser.add_argument(
	    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
	)

	A     :   Union[str, Any]			    =			parser.parse_args()

	assert args.model_path is not None, "Must provide a model path!"

	assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

	# Path for safetensors
	A     :   Union[str, Any]			    =			osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
	A     :   Optional[Any]			    =			osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
	A     :   List[Any]			    =			osp.join(args.model_path, "text_encoder", "model.safetensors")

	# Load models from safetensors if it exists, if it doesn't pytorch
	if osp.exists(unet_path):
		A     :   Optional[int]			    =			load_file(unet_path, device="cpu")
	else:
		A     :   List[str]			    =			osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
		A     :   str			    =			torch.load(unet_path, map_location="cpu")

	if osp.exists(vae_path):
		A     :   str			    =			load_file(vae_path, device="cpu")
	else:
		A     :   List[Any]			    =			osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
		A     :   str			    =			torch.load(vae_path, map_location="cpu")

	if osp.exists(text_enc_path):
		A     :   List[str]			    =			load_file(text_enc_path, device="cpu")
	else:
		A     :   Any			    =			osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
		A     :   int			    =			torch.load(text_enc_path, map_location="cpu")

	# Convert the UNet model
	A     :   Union[str, Any]			    =			convert_unet_state_dict(unet_state_dict)
	A     :   Tuple			    =			{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

	# Convert the VAE model
	A     :   Optional[int]			    =			convert_vae_state_dict(vae_state_dict)
	A     :   int			    =			{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

	# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
	A     :   List[Any]			    =			"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

	if is_vaa_model:
		# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
		A     :   Optional[Any]			    =			{"transformer." + k: v for k, v in text_enc_dict.items()}
		A     :   str			    =			convert_text_enc_state_dict_vaa(text_enc_dict)
		A     :   List[Any]			    =			{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
	else:
		A     :   List[Any]			    =			convert_text_enc_state_dict(text_enc_dict)
		A     :   Tuple			    =			{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

	# Put together new checkpoint
	A     :   Any			    =			{**unet_state_dict, **vae_state_dict, **text_enc_dict}
	if args.half:
		A     :   List[str]			    =			{k: v.half() for k, v in state_dict.items()}

	if args.use_safetensors:
		save_file(state_dict, args.checkpoint_path)
	else:
		A     :   List[Any]			    =			{"state_dict": state_dict}
		torch.save(state_dict, args.checkpoint_path)






"""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


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

if TYPE_CHECKING:
 import pyspark

@dataclass
class       lowerCAmelCase__				(						datasets.BuilderConfig	):
     __a						=					None






def 					_UpperCAmelCase      ( __lowerCamelCase   :       "pyspark.sql.DataFrame" ,					__lowerCamelCase   :       List[int] ,					)     ->					Optional[int]:
  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			:			Optional[int]    ,      _lowerCamelCase			:			"pyspark.sql.DataFrame"    ,      _lowerCamelCase			:			List[Any]=None    ,      ):
       _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			:			Optional[int]			):
       yield from self.generate_examples_fn()







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			np.random.Generator			):
       _snake_case					     =       list(range(self.df.rdd.getNumPartitions()			)			)
       generator.shuffle(_lowerCamelCase			)
       return SparkExamplesIterable(self.df    ,      partition_order=_lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       self.split_shard_indices_by_worker(_lowerCamelCase    ,      _lowerCamelCase			)
       return SparkExamplesIterable(self.df    ,      partition_order=_lowerCamelCase			)







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






class       lowerCAmelCase__				(						datasets.DatasetBuilder	):
     __a						=					SparkConfig







     def __init__(							self			:			str    ,      _lowerCamelCase			:			"pyspark.sql.DataFrame"    ,      _lowerCamelCase			:			str = None    ,      _lowerCamelCase			:			str = None    ,      **_lowerCamelCase			:			List[str]    ,      ):
       import pyspark

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

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







     def      lowercase	(							self			:			str			):
       # Returns the path of the created file.
       def create_cache_and_write_probe(_lowerCamelCase			:			List[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=_lowerCamelCase			)
         _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(_lowerCamelCase    ,      '''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(_lowerCamelCase			).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			:			Dict			):
       return datasets.DatasetInfo(features=self.config.features			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			datasets.download.download_manager.DownloadManager			):
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN			)]







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			List[Any]			):
       import pyspark

       def get_arrow_batch_size(_lowerCamelCase			:			List[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 <= 100 else 100
       # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
       _snake_case					     =       (
           self.df.limit(_lowerCamelCase			)
           .repartition(1			)
           .mapInArrow(_lowerCamelCase    ,      '''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(_lowerCamelCase    ,      int(approx_total_size / max_shard_size			)			)
         _snake_case					     =       self.df.repartition(_lowerCamelCase			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			int    ,      ):
       import pyspark

       _snake_case					     =       ParquetWriter if file_format == '''parquet''' else ArrowWriter
       _snake_case					     =       os.path.join(self._working_dir    ,      os.path.basename(_lowerCamelCase			)			) 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(_lowerCamelCase			:			Tuple			):
         # Within the same SparkContext, no two task attempts will share the same attempt ID.
         _snake_case					     =       pyspark.TaskContext().taskAttemptId()
         _snake_case					     =       next(_lowerCamelCase    ,      _lowerCamelCase			)
         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=_lowerCamelCase    ,      path=working_fpath.replace('''SSSSS'''    ,      f'''{shard_id:05d}'''			).replace('''TTTTT'''    ,      f'''{task_id:05d}'''			)    ,      writer_batch_size=_lowerCamelCase    ,      storage_options=_lowerCamelCase    ,      embed_local_files=_lowerCamelCase    ,      )
         _snake_case					     =       pa.Table.from_batches([first_batch]			)
         writer.write_table(_lowerCamelCase			)
         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=_lowerCamelCase    ,      storage_options=_lowerCamelCase    ,      embed_local_files=_lowerCamelCase    ,      )
           _snake_case					     =       pa.Table.from_batches([batch]			)
           writer.write_table(_lowerCamelCase			)

         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(_lowerCamelCase			)			):
             _snake_case					     =       os.path.join(os.path.dirname(_lowerCamelCase			)    ,      os.path.basename(_lowerCamelCase			)			)
             shutil.move(_lowerCamelCase    ,      _lowerCamelCase			)

       _snake_case					     =       (
           self.df.mapInArrow(_lowerCamelCase    ,      '''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			:			int    ,      _lowerCamelCase			:			"datasets.SplitGenerator"    ,      _lowerCamelCase			:			str = "arrow"    ,      _lowerCamelCase			:			Optional[Union[str, int]] = None    ,      _lowerCamelCase			:			Optional[int] = None    ,      **_lowerCamelCase			:			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(_lowerCamelCase			)
       _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    ,      _lowerCamelCase			)

       _snake_case					     =       0
       _snake_case					     =       0
       _snake_case					     =       0
       _snake_case					     =       []
       _snake_case					     =       []

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

       _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(
             _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      ):
           rename(
               _lowerCamelCase    ,      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(_lowerCamelCase			)			):
           _snake_case     ,					_snake_case					     =       task_id_and_num_shards[i]
           for shard_id in range(_lowerCamelCase			):
             args.append([task_id, shard_id, global_shard_id]			)
             global_shard_id += 1
         self._spark.sparkContext.parallelize(_lowerCamelCase    ,      len(_lowerCamelCase			)			).map(lambda _lowerCamelCase			: _rename_shard(*_lowerCamelCase			)			).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(_lowerCamelCase    ,      ''''''			)    ,      )







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			"datasets.SplitGenerator"    ,      ):
       return SparkExamplesIterable(self.df			)



