




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    snake_case   (				A=None		,  A=None     )				->				str:




	'''simple docstring'''


	return field(default_factory=lambda: default		,  metadata=A     )


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






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


	try:
		int(A     )
		return True
	except ValueError:
		return False






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


	try:
		float(A     )
		return True
	except ValueError:
		return False


class 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




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

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





					def       lowercase_				(							self   ):







						'''simple docstring'''




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

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

							lowerCamelCase_				:						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:
									lowerCamelCase_				:						List[Any]				  =				np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,			dtype=__lowercase					,			)
								else:
									lowerCamelCase_				:						int				  =				np.asarray(
									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,			dtype=np.floataa					,			)

								((lowerCamelCase_)  ,     (lowerCamelCase_))				:						Dict				  =				(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								lowerCamelCase_				:						Union[str, Any]				  =				np.asarray(__lowercase					,			__lowercase   )[: len(__lowercase   )]
								plt.scatter(
								    __lowercase					,			__lowercase					,			label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"   )
								plt.plot(__lowercase					,			__lowercase					,			'--'   )

							title_str += f" {label_model_name} vs."

						lowerCamelCase_				:						Tuple				  =				title_str[:-4]
						lowerCamelCase_				:						Optional[Any]				  =				'Time in s' if self.args.is_time else 'Memory in MB'

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

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






def    snake_case   (				)				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						Tuple				  =				HfArgumentParser(A     )
	lowerCamelCase_				:						Optional[Any]				  =				parser.parse_args_into_dataclasses()[0]
	lowerCamelCase_				:						Optional[int]				  =				Plot(args=A     )
	plot.plot()


if __name__ == "__main__":
							main()








'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment






class        a__     (      unittest.TestCase    ):
     '''simple docstring'''







     def      _UpperCamelCase (self     :						int       )	->							Optional[int]:
           __A		 =   inspect.getfile(accelerate.test_utils       )
           __A		 =   os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ["scripts", "test_sync.py"]       )






     @require_cpu
     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           debug_launcher(test_sync.main					,					num_processes=1       )






     @require_cpu
     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							Optional[Any]:
           debug_launcher(test_sync.main       )






     @require_single_gpu
     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							List[str]:
           test_sync.main()






     @require_multi_gpu
     def      _UpperCamelCase (self     :						Optional[int]       )	->							List[str]:
           print(f"Found {torch.cuda.device_count()} devices."       )
           __A		 =   ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
           with patch_environment(omp_num_threads=1       ):
                 execute_subprocess_async(lowercase__					,					env=os.environ.copy()       )


