
"""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 lowercase_  (       _snake_case=None       ,_snake_case=None						):
							return field(default_factory=lambda: default       ,metadata=_lowerCamelCase						)



@dataclass
class      lowerCAmelCase_						:


				"""simple docstring"""



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






def lowercase_  (       _snake_case						):
							try:
														int(_lowerCamelCase						)
														return True
							except ValueError:
														return False

def lowercase_  (       _snake_case						):
							try:
														float(_lowerCamelCase						)
														return True
							except ValueError:
														return False



class      lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       args
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

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



				def      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       plt.subplots()
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       """Time usage""" if self.args.is_time else """Memory usage"""
											SCREAMING_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()   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       sorted(set(self.result_dict[model_name]["""bsz"""]   )   )
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       sorted(set(self.result_dict[model_name]["""seq_len"""]   )   )
																		SCREAMING_SNAKE_CASE__						:	Any									=       self.result_dict[model_name]["""result"""]

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

																		SCREAMING_SNAKE_CASE__						:	Tuple									=       (
																		    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:
																																SCREAMING_SNAKE_CASE__						:	List[str]									=       np.asarray(
																																    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,	dtype=snake_case_       ,	)
																									else:
																																SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       np.asarray(
																																    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,	dtype=np.floataa       ,	)

																									(SCREAMING_SNAKE_CASE__)						:	Optional[Any]									=       (
																									    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																									)

																									SCREAMING_SNAKE_CASE__						:	List[str]									=       np.asarray(snake_case_       ,	snake_case_   )[: len(snake_case_   )]
																									plt.scatter(
																									    snake_case_       ,	snake_case_       ,	label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''   )
																									plt.plot(snake_case_       ,	snake_case_       ,	"""--"""   )

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

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

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

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






def lowercase_  (       ):
							SCREAMING_SNAKE_CASE__						:	Dict									=       HfArgumentParser(_lowerCamelCase						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       parser.parse_args_into_dataclasses()[0]
							SCREAMING_SNAKE_CASE__						:	Any									=       Plot(args=_lowerCamelCase						)
							plot.plot()


if __name__ == "__main__":
					main()




"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def lowercase_  (       *_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.cuda.device_count()
							print(f'''Device count: {num_processes}'''						)
							with patch_environment(
							    world_size=_snake_case       ,master_addr="""127.0.01"""       ,master_port="""29500"""       ,mixed_precision=args[1].mixed_precision						):
														SCREAMING_SNAKE_CASE__						:	int									=       PrepareForLaunch(_snake_case       ,distributed_type="""MULTI_GPU"""						)
														torch.multiprocessing.start_processes(_snake_case       ,args=_snake_case       ,nprocs=_snake_case       ,start_method="""spawn"""						)


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					UpperCAmelCase__				:   str   							=			rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					UpperCAmelCase__				:   int   							=			[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					UpperCAmelCase__				:   List[str]   							=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					UpperCAmelCase__				:   Dict   							=			argparse.Namespace(cpu=False, mixed_precision='fp16')
					UpperCAmelCase__				:   Any   							=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])




