





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__    (     __a=None ,			__a=None ):
	return field(default_factory=lambda: default ,			metadata=__a )
@dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      str			   =							field(
						    metadata={"help": "The csv file to plot."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Disable logarithmic scale when plotting"}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={
						        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
						    }							,					)
						__magic_name__:      Optional[str]			   =							field(
						    default=snake_case_							,					metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}							,					)
						__magic_name__:      Optional[List[str]]			   =							list_field(
						    default=snake_case_							,					metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		int(__a )
		return True
	except ValueError:
		return False


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		float(__a )
		return True
	except ValueError:
		return False
class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   Dict		, _A   :   List[Any]						)				->     str:







							"""simple docstring"""


							snake_case_		:      int           =						args
							snake_case_		:      List[Any]           =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

							with open(self.args.csv_file		, newline=''						) as csv_file:
								snake_case_		:      Union[str, Any]           =						csv.DictReader(_A						)
								for row in reader:
									snake_case_		:      List[str]           =						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
										snake_case_		:      Dict           =						int(row['result']						)
									elif can_convert_to_float(row['result']						):
										# value is not None
										snake_case_		:      Optional[int]           =						float(row['result']						)






						def UpperCAmelCase_					(   self   :   Any						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      Any           =						plt.subplots()
							snake_case_		:      Tuple           =						'Time usage' if self.args.is_time else 'Memory usage'
							snake_case_		:      Tuple           =						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()						):
								snake_case_		:      Tuple           =						sorted(set(self.result_dict[model_name]['bsz']						)						)
								snake_case_		:      Dict           =						sorted(set(self.result_dict[model_name]['seq_len']						)						)
								snake_case_		:      Optional[int]           =						self.result_dict[model_name]['result']

								((snake_case_)    ,(snake_case_))		:      Optional[int]           =						(
								    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
								)

								snake_case_		:      List[str]           =						(
								    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:
										snake_case_		:      int           =						np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		, dtype=_A		, )
									else:
										snake_case_		:      str           =						np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		, dtype=np.floataa		, )

									((snake_case_)    ,(snake_case_))		:      Union[str, Any]           =						(
									    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
									)

									snake_case_		:      Tuple           =						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."""

							snake_case_		:      Optional[int]           =						title_str[:-4]
							snake_case_		:      Tuple           =						'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__    (     ):
	snake_case_		:      Dict           =						HfArgumentParser(__a )
	snake_case_		:      List[str]           =						parser.parse_args_into_dataclasses()[0]
	snake_case_		:      int           =						Plot(args=__a )
	plot.plot()


if __name__ == "__main__":
						main()









'''simple docstring'''





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch






def  lowerCamelCase_  (     ):
 lowerCamelCase__	=  Dataset.from_dict({"i": range(17)})
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  3
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(lowercase__) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






@pytest.mark.parametrize("shards_per_node"					,  [1, 2, 3])
def  lowerCamelCase_  (     lowercase__):
 def gen(lowercase__):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  shards_per_node * world_size
 lowerCamelCase__	=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(lowercase__)]}
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__					,  gen_kwargs=lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  2
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))

 lowerCamelCase__	=  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle(seed=42)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle()

 lowerCamelCase__	=  split_dataset_by_node(full_ds.shuffle(seed=42)					,  rank=0					,  world_size=lowercase__)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(full_ds.shuffle()					,  rank=0					,  world_size=lowercase__)






@pytest.mark.parametrize("streaming"					,  [False, True])
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  2
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())






@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,  [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,  )
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  True
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())

