"""simple docstring"""






import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase=None			,       __UpperCAmelCase=None					)    ->    Tuple:
   return field(default_factory=lambda: default			,       metadata=lowercase_					)


@dataclass
class        UpperCAmelCase		:



    """simple docstring"""

    _UpperCAmelCase      :str		       =					field(
        metadata={"help": "The csv file to plot."}       ,)
    _UpperCAmelCase      :bool		       =					field(
        default=_UpperCAmelCase       ,metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}       ,)
    _UpperCAmelCase      :bool		       =					field(
        default=_UpperCAmelCase       ,metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}       ,)
    _UpperCAmelCase      :bool		       =					field(
        default=_UpperCAmelCase       ,metadata={"help": "Disable logarithmic scale when plotting"}       ,)
    _UpperCAmelCase      :bool		       =					field(
        default=_UpperCAmelCase       ,metadata={
            "help": "Whether the csv file has training results or inference results. Defaults to inference results."
        }       ,)
    _UpperCAmelCase      :Optional[str]		       =					field(
        default=_UpperCAmelCase       ,metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}       ,)
    _UpperCAmelCase      :Optional[List[str]]		       =					list_field(
        default=_UpperCAmelCase       ,metadata={"help": "List of model names that are used instead of the ones in the csv file."}    )



def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase					)    ->    int:
   try:
      int(lowercase_					)
      return True
   except ValueError:
      return False



def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase					)    ->    Optional[int]:
   try:
      float(lowercase_					)
      return True
   except ValueError:
      return False


class        UpperCAmelCase		:



    """simple docstring"""

    def __init__(  self       ,     _UpperCAmelCase				):
       lowercase__:  Optional[int]   = args
       lowercase__:  Tuple   = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

       with open(self.args.csv_file       ,     newline=''''''				) as csv_file:
          lowercase__:  List[Any]   = csv.DictReader(A__				)
          for row in reader:
             lowercase__:  Optional[int]   = row["""model"""]
             self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']				)				)
             self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']				)				)
             if can_convert_to_int(row['''result''']				):
                # value is not None
                lowercase__:  str   = int(row['''result''']				)
             elif can_convert_to_float(row['''result''']				):
                # value is not None
                lowercase__:  Tuple   = float(row['''result''']				)






    def _snake_case     (  self				):
       lowercase__:  Tuple   = plt.subplots()
       lowercase__:  List[str]   = """Time usage""" if self.args.is_time else """Memory usage"""
       lowercase__:  Dict   = title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

       if not self.args.no_log_scale:
          # set logarithm scales
          ax.set_xscale('''log'''				)
          ax.set_yscale('''log'''				)

       for axis in [ax.xaxis, ax.yaxis]:
          axis.set_major_formatter(ScalarFormatter()				)

       for model_name_idx, model_name in enumerate(self.result_dict.keys()				):
          lowercase__:  str   = sorted(set(self.result_dict[model_name]['''bsz''']				)				)
          lowercase__:  Optional[Any]   = sorted(set(self.result_dict[model_name]['''seq_len''']				)				)
          lowercase__:  int   = self.result_dict[model_name]["""result"""]

          (lowercase__):  Dict   = (
              (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
          )

          lowercase__:  List[Any]   = (
              model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
          )

          for inner_loop_value in inner_loop_array:
             if self.args.plot_along_batch:
                lowercase__:  Optional[int]   = np.asarray(
                    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,     dtype=A__       ,     )
             else:
                lowercase__:  List[Any]   = np.asarray(
                    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,     dtype=np.floataa       ,     )

             (lowercase__):  List[str]   = (
                 ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
             )

             lowercase__:  Dict   = np.asarray(A__       ,     A__				)[: len(A__				)]
             plt.scatter(
                 A__       ,     A__       ,     label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""				)
             plt.plot(A__       ,     A__       ,     '''--'''				)

          title_str += F""" {label_model_name} vs."""

       lowercase__:  int   = title_str[:-4]
       lowercase__:  str   = """Time in s""" if self.args.is_time else """Memory in MB"""

       # plot
       plt.title(A__				)
       plt.xlabel(A__				)
       plt.ylabel(A__				)
       plt.legend()

       if self.args.figure_png_file is not None:
          plt.savefig(self.args.figure_png_file				)
       else:
          plt.show()



def SCREAMING_SNAKE_CASE__     (  )    ->    int:
   lowercase__:  Optional[Any]   = HfArgumentParser(lowercase_					)
   lowercase__:  str   = parser.parse_args_into_dataclasses()[0]
   lowercase__:  List[Any]   = Plot(args=lowercase_					)
   plot.plot()


if __name__ == "__main__":
       main()



import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


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

if TYPE_CHECKING:
	import pyspark



@dataclass
class        _a		(datasets.BuilderConfig							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[datasets.Features]    				=			None



def 		UpperCamelCase   (lowercase_:					"pyspark.sql.DataFrame"       , lowercase_:					List[int]       , )	-> Dict:
	import pyspark

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

	return generate_fn



class        _a		(_BaseExamplesIterable							):


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__=None       ,			):
						A__				:       List[str]	  =						df
						A__				:       Optional[int]	  =						partition_order or range(self.df.rdd.getNumPartitions()     )
						A__				:       Tuple	  =						_generate_iterable_examples(self.df       ,			self.partition_order     )






					def __iter__(					self     ):
						yield from self.generate_examples_fn()






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						list(range(self.df.rdd.getNumPartitions()     )     )
						generator.shuffle(A__     )
						return SparkExamplesIterable(self.df       ,			partition_order=A__     )






					def     __A     (					self       ,			A__       ,			A__     ):
						A__				:       Optional[int]	  =						self.split_shard_indices_by_worker(A__       ,			A__     )
						return SparkExamplesIterable(self.df       ,			partition_order=A__     )






					@property
					def     __A     (					self     ):
						return len(self.partition_order     )



class        _a		(datasets.DatasetBuilder							):


					'''simple docstring'''






					UpperCAmelCase__:  Union[str, Any]    				=			SparkConfig






					def __init__(					self       ,			A__       ,			A__ = None       ,			A__ = None       ,			**A__       ,			):
						import pyspark

						A__				:       Dict	  =						pyspark.sql.SparkSession.builder.getOrCreate()
						A__				:       int	  =						df
						A__				:       Any	  =						working_dir

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






					def     __A     (					self     ):
						# Returns the path of the created file.
						def create_cache_and_write_probe(A__     ):
							# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
							# already exist.
							os.makedirs(self._cache_dir       ,			exist_ok=A__     )
							A__				:       List[str]	  =						os.path.join(self._cache_dir       ,			"""fs_test""" + uuid.uuida().hex     )
							# Opening the file in append mode will create a new file unless it already exists, in which case it will not
							# change the file contents.
							open(A__       ,			"""a"""     )
							return [probe_file]

						if self._spark.conf.get("""spark.master"""       ,			""""""     ).startswith("""local"""     ):
							return

						# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
						# accessible to the driver.
						# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
						if self._cache_dir:
							A__				:       int	  =						(
							    self._spark.sparkContext.parallelize(range(1     )       ,			1     ).mapPartitions(A__     ).collect()
							)
							if os.path.isfile(probe[0]     ):
								return

						raise ValueError(
						    """When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"""     )






					def     __A     (					self     ):
						return datasets.DatasetInfo(features=self.config.features     )






					def     __A     (					self       ,			A__     ):
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN     )]






					def     __A     (					self       ,			A__     ):
						import pyspark

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

						A__				:       Dict	  =						self.df.count()
						A__				:       List[Any]	  =						df_num_rows if df_num_rows <= 100 else 100
						# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
						A__				:       Union[str, Any]	  =						(
						    self.df.limit(A__     )
						    .repartition(1     )
						    .mapInArrow(A__       ,			"""batch_bytes: long"""     )
						    .agg(pyspark.sql.functions.sum("""batch_bytes"""     ).alias("""sample_bytes"""     )     )
						    .collect()[0]
						    .sample_bytes
						    / sample_num_rows
						)
						A__				:       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__				:       Dict	  =						min(A__       ,			int(approx_total_size / max_shard_size     )     )
							A__				:       int	  =						self.df.repartition(A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			):
						import pyspark

						A__				:       Optional[int]	  =						ParquetWriter if file_format == """parquet""" else ArrowWriter
						A__				:       Any	  =						os.path.join(self._working_dir       ,			os.path.basename(A__     )     ) if self._working_dir else fpath
						A__				:       Union[str, Any]	  =						file_format == """parquet"""

						# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
						# pickling the SparkContext.
						A__				:       str	  =						self.config.features
						A__				:       Union[str, Any]	  =						self._writer_batch_size
						A__				:       Tuple	  =						self._fs.storage_options

						def write_arrow(A__     ):
							# Within the same SparkContext, no two task attempts will share the same attempt ID.
							A__				:       str	  =						pyspark.TaskContext().taskAttemptId()
							A__				:       str	  =						next(A__       ,			A__     )
							if first_batch is None:
								# Some partitions might not receive any data.
								return pa.RecordBatch.from_arrays(
								    [[task_id], [0], [0]]       ,			names=["""task_id""", """num_examples""", """num_bytes"""]       ,			)
							A__				:       List[Any]	  =						0
							A__				:       Optional[Any]	  =						writer_class(
							    features=A__       ,			path=working_fpath.replace("""SSSSS"""       ,			F"""{shard_id:05d}"""     ).replace("""TTTTT"""       ,			F"""{task_id:05d}"""     )       ,			writer_batch_size=A__       ,			storage_options=A__       ,			embed_local_files=A__       ,			)
							A__				:       Tuple	  =						pa.Table.from_batches([first_batch]     )
							writer.write_table(A__     )
							for batch in it:
								if max_shard_size is not None and writer._num_bytes >= max_shard_size:
									A__  ,  A__				:       Union[str, 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__				:       Tuple	  =						writer_class(
									    features=writer._features       ,			path=working_fpath.replace("""SSSSS"""       ,			F"""{shard_id:05d}"""     ).replace("""TTTTT"""       ,			F"""{task_id:05d}"""     )       ,			writer_batch_size=A__       ,			storage_options=A__       ,			embed_local_files=A__       ,			)
								A__				:       Optional[int]	  =						pa.Table.from_batches([batch]     )
								writer.write_table(A__     )

							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(A__     )     ):
									A__				:       Optional[Any]	  =						os.path.join(os.path.dirname(A__     )       ,			os.path.basename(A__     )     )
									shutil.move(A__       ,			A__     )

						A__				:       Tuple	  =						(
						    self.df.mapInArrow(A__       ,			"""task_id: long, num_examples: long, num_bytes: long"""     )
						    .groupBy("""task_id"""     )
						    .agg(
						        pyspark.sql.functions.sum("""num_examples"""     ).alias("""total_num_examples"""     )       ,			pyspark.sql.functions.sum("""num_bytes"""     ).alias("""total_num_bytes"""     )       ,			pyspark.sql.functions.count("""num_bytes"""     ).alias("""num_shards"""     )       ,			pyspark.sql.functions.collect_list("""num_examples"""     ).alias("""shard_lengths"""     )       ,			)
						    .collect()
						)
						for row in stats:
							yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)






					def     __A     (					self       ,			A__       ,			A__ = "arrow"       ,			A__ = None       ,			A__ = None       ,			**A__       ,			):
						self._validate_cache_dir()

						A__				:       Union[str, Any]	  =						convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE     )
						self._repartition_df_if_needed(A__     )
						A__				:       Any	  =						not is_remote_filesystem(self._fs     )
						A__				:       Optional[int]	  =						os.path.join if is_local else posixpath.join

						A__				:       Dict	  =						"""-TTTTT-SSSSS-of-NNNNN"""
						A__				:       Any	  =						F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
						A__				:       Any	  =						path_join(self._output_dir       ,			A__     )

						A__				:       Tuple	  =						0
						A__				:       str	  =						0
						A__				:       List[Any]	  =						0
						A__				:       List[Any]	  =						[]
						A__				:       Optional[Any]	  =						[]

						for task_id, content in self._prepare_split_single(A__       ,			A__       ,			A__     ):
							(
							    (
							    A__
							)  ,  (
							    A__
							)  ,  (
							    A__
							)  ,  (
							    A__
							)  ,  
							)				:       List[Any]	  =						content
							if num_bytes > 0:
								total_num_examples += num_examples
								total_num_bytes += num_bytes
								total_shards += num_shards
								task_id_and_num_shards.append((task_id, num_shards)     )
								all_shard_lengths.extend(A__     )

						A__				:       Optional[int]	  =						total_num_examples
						A__				:       Union[str, Any]	  =						total_num_bytes

						# should rename everything at the end
						logger.debug(F"""Renaming {total_shards} shards."""     )
						if total_shards > 1:
							A__				:       int	  =						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__				:       Dict	  =						self._fs

							# use the -SSSSS-of-NNNNN pattern
							def _rename_shard(
							    A__       ,			A__       ,			A__       ,			):
								rename(
								    A__       ,			fpath.replace("""SSSSS"""       ,			F"""{shard_id:05d}"""     ).replace("""TTTTT"""       ,			F"""{task_id:05d}"""     )       ,			fpath.replace("""TTTTT-SSSSS"""       ,			F"""{global_shard_id:05d}"""     ).replace("""NNNNN"""       ,			F"""{total_shards:05d}"""     )       ,			)

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






					def     __A     (					self       ,			A__       ,			):
						return SparkExamplesIterable(self.df     )



