


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowerCAmelCase           =		[
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]





class       A    (   unittest.TestCase ):


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None					):
									__lowercase=			None
									__lowercase=			os.path.abspath(os.path.join('examples'      ,				'by_feature'					)					)
									__lowercase=			os.path.abspath('examples'					)
									for item in os.listdir(lowerCAmelCase					):
											if item not in EXCLUDE_EXAMPLES:
													__lowercase=			os.path.join(lowerCAmelCase      ,				lowerCAmelCase					)
													if os.path.isfile(lowerCAmelCase					) and ".py" in item_path:
															with self.subTest(
															    tested_script=lowerCAmelCase      ,				feature_script=lowerCAmelCase      ,				tested_section='main()' if parser_only else 'training_function()'      ,				):
																	__lowercase=			compare_against_test(
																	    os.path.join(lowerCAmelCase      ,				lowerCAmelCase					)      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)
																	__lowercase=			'\n'.join(lowerCAmelCase					)
																	if special_strings is not None:
																			for string in special_strings:
																					__lowercase=			diff.replace(lowerCAmelCase      ,				''					)
																	self.assertEqual(lowerCAmelCase      ,				''					)


							def 			_A       (self					):
									self.one_complete_example('complete_nlp_example.py'      ,				lowerCAmelCase					)
									self.one_complete_example('complete_nlp_example.py'      ,				lowerCAmelCase					)







							def 			_A       (self					):
									__lowercase=			os.path.abspath(os.path.join('examples'      ,				'cv_example.py'					)					)
									__lowercase=			[
									    ' ' * 1_6 + '{\n\n',
									    ' ' * 2_0 + '"accuracy": eval_metric["accuracy"],\n\n',
									    ' ' * 2_0 + '"f1": eval_metric["f1"],\n\n',
									    ' ' * 2_0 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
									    ' ' * 2_0 + '"epoch": epoch,\n\n',
									    ' ' * 1_6 + '},\n\n',
									    ' ' * 1_6 + 'step=epoch,\n',
									    ' ' * 1_2,
									    ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
									]
									self.one_complete_example('complete_cv_example.py'      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)
									self.one_complete_example('complete_cv_example.py'      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)





@mock.patch.dict(os.environ					,				{'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class       A    (   A_ ):
							UpperCamelCase_   :							str       =False


							@classmethod
							def 			_A       (cls					):
									super().setUpClass()
									__lowercase=			tempfile.mkdtemp()
									__lowercase=			os.path.join(cls._tmpdir      ,				'default_config.yml'					)

									write_basic_config(save_location=cls.configPath					)
									__lowercase=			['accelerate', 'launch', '--config_file', cls.configPath]


							@classmethod
							def 			_A       (cls					):
									super().tearDownClass()
									shutil.rmtree(cls._tmpdir					)


							def 			_A       (self					):
									__lowercase=			f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
									run_command(self._launch_args + testargs					)
									self.assertTrue(os.path.exists(os.path.join(self.tmpdir      ,				'epoch_0'					)					)					)


							def 			_A       (self					):
									__lowercase=			f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
									__lowercase=			run_command(self._launch_args + testargs					)
									self.assertTrue(os.path.exists(os.path.join(self.tmpdir      ,				'step_2'					)					)					)


							def 			_A       (self					):
									__lowercase=			f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir      ,				"epoch_0"					)}\n        '.split()
									__lowercase=			run_command(self._launch_args + testargs      ,				return_stdout=lowerCAmelCase					)
									self.assertNotIn('epoch 0:'      ,				lowerCAmelCase					)
									self.assertIn('epoch 1:'      ,				lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir      ,				"step_2"					)}\n        '.split()
									__lowercase=			run_command(self._launch_args + testargs      ,				return_stdout=lowerCAmelCase					)
									if torch.cuda.is_available():
											__lowercase=			torch.cuda.device_count()
									else:
											__lowercase=			1
									if num_processes > 1:
											self.assertNotIn('epoch 0:'      ,				lowerCAmelCase					)
											self.assertIn('epoch 1:'      ,				lowerCAmelCase					)
									else:
											self.assertIn('epoch 0:'      ,				lowerCAmelCase					)
											self.assertIn('epoch 1:'      ,				lowerCAmelCase					)


							@slow
							def 			_A       (self					):
									__lowercase=			'\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
									with mock.patch.dict(os.environ      ,				{'TESTING_MOCKED_DATALOADERS': '0'}					):
											__lowercase=			run_command(self._launch_args + testargs      ,				return_stdout=lowerCAmelCase					)
											__lowercase=			re.findall('({.+})'      ,				lowerCAmelCase					)
											__lowercase=			[r for r in results if 'accuracy' in r][-1]
											__lowercase=			ast.literal_eval(lowerCAmelCase					)
											self.assertGreaterEqual(results['accuracy']      ,				0.75					)


							def 			_A       (self					):
									__lowercase=			['examples/by_feature/multi_process_metrics.py']
									run_command(self._launch_args + testargs					)


							@require_trackers
							@mock.patch.dict(os.environ      ,				{'WANDB_MODE': 'offline'}					)
							def 			_A       (self					):
									with tempfile.TemporaryDirectory() as tmpdir:
											__lowercase=			f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
											run_command(self._launch_args + testargs					)
											self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase      ,				'tracking'					)					)					)


							def 			_A       (self					):
									__lowercase=			['examples/by_feature/gradient_accumulation.py']
									run_command(self._launch_args + testargs					)







							def 			_A       (self					):
									__lowercase=			['examples/by_feature/local_sgd.py']
									run_command(self._launch_args + testargs					)










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        _lowerCamelCase(	lowercase__=None   , lowercase__=None	)						->	Dict:

		'''simple docstring'''







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





@dataclass
class       A    :
							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        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







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







def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







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





class       A    :


							def __init__(self      ,				lowerCAmelCase					):
									__lowercase=			args
									__lowercase=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

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







							def 			_A       (self					):
									__lowercase,							__lowercase=			plt.subplots()
									__lowercase=			'Time usage' if self.args.is_time else 'Memory usage'
									__lowercase=			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=			sorted(set(self.result_dict[model_name]['bsz']					)					)
											__lowercase=			sorted(set(self.result_dict[model_name]['seq_len']					)					)
											__lowercase=			self.result_dict[model_name]['result']

											((__lowercase),							(__lowercase))=			(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

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

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

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

											title_str += f' {label_model_name} vs.'

									__lowercase=			title_str[:-4]
									__lowercase=			'Time in s' if self.args.is_time else 'Memory in MB'

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

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







def        _lowerCamelCase(	)						->	str:

		'''simple docstring'''







		__lowercase=			HfArgumentParser(lowercase__	)
		__lowercase=			parser.parse_args_into_dataclasses()[0]
		__lowercase=			Plot(args=lowercase__	)
		plot.plot()


if __name__ == "__main__":
	main()






