import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE__   =					[]
for i in range(6):
					# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
					)
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
					)
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
					# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
					)
					rename_keys.append(
					    (
					        f"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
					        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
					    )
					)
					rename_keys.append(
					    (
					        f"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
					        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
					    )
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

					# q, k, v projections in self/cross-attention in decoder for conditional DETR
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
					)

					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
					)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			int   )   ->			str:





						"""simple docstring"""






						snake_case        =     state_dict.pop(_UpperCamelCase   )
						snake_case        =     val





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			List[Any]:





						"""simple docstring"""






						snake_case        =     OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		snake_case        =     key.replace('backbone.0.body'	,     'backbone.conv_encoder.model'   )
																		snake_case        =     value
												else:
																		snake_case        =     value

						return new_state_dict





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			List[Any]=False   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case        =     ''
						if is_panoptic:
												snake_case        =     'conditional_detr.'

						# first: transformer encoder
						for i in range(6   ):
												# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
												snake_case        =     state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
												snake_case        =     state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
												# next, add query, keys and values (in that order) to the state dict
												snake_case        =     in_proj_weight[:2_5_6, :]
												snake_case        =     in_proj_bias[:2_5_6]
												snake_case        =     in_proj_weight[2_5_6:5_1_2, :]
												snake_case        =     in_proj_bias[2_5_6:5_1_2]
												snake_case        =     in_proj_weight[-2_5_6:, :]
												snake_case        =     in_proj_bias[-2_5_6:]





def 				lowerCAmelCase__      (     )   ->			Optional[int]:





						"""simple docstring"""






						snake_case        =     'http://images.cocodataset.org/val2017/000000039769.jpg'
						snake_case        =     Image.open(requests.get(_UpperCamelCase	,     stream=_UpperCamelCase   ).raw   )

						return im





@torch.no_grad()
def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			int   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     ConditionalDetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												snake_case        =     'resnet101'
						if "dc5" in model_name:
												snake_case        =     True
						snake_case        =     'panoptic' in model_name
						if is_panoptic:
												snake_case        =     2_5_0
						else:
												snake_case        =     9_1
												snake_case        =     'huggingface/label-files'
												snake_case        =     'coco-detection-id2label.json'
												snake_case        =     json.load(open(hf_hub_download(_UpperCamelCase	,     _UpperCamelCase	,     repo_type='dataset'   )	,     'r'   )   )
												snake_case        =     {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
												snake_case        =     idalabel
												snake_case        =     {v: k for k, v in idalabel.items()}

						# load image processor
						snake_case        =     'coco_panoptic' if is_panoptic else 'coco_detection'
						snake_case        =     ConditionalDetrImageProcessor(format=_UpperCamelCase   )

						# prepare image
						snake_case        =     prepare_img()
						snake_case        =     image_processor(images=_UpperCamelCase	,     return_tensors='pt'   )
						snake_case        =     encoding['pixel_values']

						logger.info(f"""Converting model {model_name}..."""   )

						# load original model from torch hub
						snake_case        =     torch.hub.load('DeppMeng/ConditionalDETR'	,     _UpperCamelCase	,     pretrained=_UpperCamelCase   ).eval()
						snake_case        =     conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		snake_case        =     'conditional_detr.' + src
												rename_key(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						snake_case        =     rename_backbone_keys(_UpperCamelCase   )
						# query, key and value matrices need special treatment
						read_in_q_k_v(_UpperCamelCase	,     is_panoptic=_UpperCamelCase   )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						snake_case        =     'conditional_detr.model.' if is_panoptic else 'model.'
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith('conditional_detr'   )
																		    and not key.startswith('class_labels_classifier'   )
																		    and not key.startswith('bbox_predictor'   )
																		):
																								snake_case        =     state_dict.pop(_UpperCamelCase   )
																								snake_case        =     val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								snake_case        =     state_dict.pop(_UpperCamelCase   )
																								snake_case        =     val
																		elif key.startswith('bbox_attention'   ) or key.startswith('mask_head'   ):
																								continue
																		else:
																								snake_case        =     state_dict.pop(_UpperCamelCase   )
																								snake_case        =     val
												else:
																		if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
																								snake_case        =     state_dict.pop(_UpperCamelCase   )
																								snake_case        =     val
    # finally, create HuggingFace model and load state dict
						snake_case        =     ConditionalDetrForSegmentation(_UpperCamelCase   ) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCamelCase   )
						model.load_state_dict(_UpperCamelCase   )
						model.eval()
						model.push_to_hub(repo_id=_UpperCamelCase	,     organization='DepuMeng'	,     commit_message='Add model'   )
						# verify our conversion
						snake_case        =     conditional_detr(_UpperCamelCase   )
						snake_case        =     model(_UpperCamelCase   )
						assert torch.allclose(outputs.logits	,     original_outputs['pred_logits']	,     atol=1e-4   )
						assert torch.allclose(outputs.pred_boxes	,     original_outputs['pred_boxes']	,     atol=1e-4   )
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks	,     original_outputs['pred_masks']	,     atol=1e-4   )

						# Save model and image processor
						logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
						Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
						model.save_pretrained(_UpperCamelCase   )
						image_processor.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    default="conditional_detr_resnet50",
					    type=str,
					    help="Name of the CONDITIONAL_DETR model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_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


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

if TYPE_CHECKING:
					import pyspark



@dataclass
class 					lowerCAmelCase_      (     datasets.BuilderConfig   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[datasets.Features]     					=  None





def 				lowerCAmelCase__      (     _UpperCamelCase    :			"pyspark.sql.DataFrame"	,     _UpperCamelCase    :			List[int]	,     )   ->			Dict:





						"""simple docstring"""






						import pyspark

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

						return generate_fn



class 					lowerCAmelCase_      (     _BaseExamplesIterable   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										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      ):






										"""simple docstring"""

										yield from self.generate_examples_fn()

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.split_shard_indices_by_worker(lowerCAmelCase						,      lowerCAmelCase      )
										return SparkExamplesIterable(self.df						,      partition_order=lowerCAmelCase      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return len(self.partition_order      )





class 					lowerCAmelCase_      (     datasets.DatasetBuilder   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[str]     					=  SparkConfig

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										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 						snake_case	(			self      ):






										"""simple docstring"""

										def create_cache_and_write_probe(lowerCAmelCase      ):
																# 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 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										import pyspark

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

										snake_case        =     self.df.count()
										snake_case        =     df_num_rows if df_num_rows <= 1_00 else 1_00
										# 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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										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      ):
																# 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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = "arrow"						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										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						,      lowerCAmelCase						,      lowerCAmelCase						,      ):
																						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 						snake_case	(			self						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										return SparkExamplesIterable(self.df      )


