




"""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 						UpperCAmelCase				(			A__:    Dict=None							,				A__:    Union[str, Any]=None     )  ->     Optional[int]:
			return field(default_factory=lambda: default							,				metadata=A__     )

@dataclass
class __lowercase:




					'''simple docstring'''


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





def 						UpperCAmelCase				(			A__:    int     )  ->     Union[str, Any]:
			try:
						int(A__     )
						return True
			except ValueError:
						return False





def 						UpperCAmelCase				(			A__:    Optional[int]     )  ->     Dict:
			try:
						float(A__     )
						return True
			except ValueError:
						return False

class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a     ):
								__lowerCamelCase					:	str	       =				args
								__lowerCamelCase					:	Union[str, Any]	       =				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

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





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

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

											__lowerCamelCase					:	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:
																	__lowerCamelCase					:	Union[str, Any]	       =				np.asarray(
																	    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,	dtype=__a						,	)
														else:
																	__lowerCamelCase					:	Dict	       =				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))					:	Union[str, Any]	       =				(
														    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
														)

														__lowerCamelCase					:	List[Any]	       =				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.'''

								__lowerCamelCase					:	List[str]	       =				title_str[:-4]
								__lowerCamelCase					:	Union[str, Any]	       =				'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 						UpperCAmelCase				(			)  ->     List[Any]:
			__lowerCamelCase					:	List[str]	       =				HfArgumentParser(A__     )
			__lowerCamelCase					:	Union[str, Any]	       =				parser.parse_args_into_dataclasses()[0]
			__lowerCamelCase					:	Dict	       =				Plot(args=A__     )
			plot.plot()


if __name__ == "__main__":
					main()









"""simple docstring"""
import pytest

import datasets


# Import fixture modules as plugins
a_				:    Tuple										=     ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Optional[Any]     )  ->     List[str]:
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ['integration', 'unit']     ):
									continue
						item.add_marker(pytest.mark.unit     )





def 						UpperCAmelCase				(			A__:    int     )  ->     Optional[int]:
			config.addinivalue_line('markers'							,				'torchaudio_latest: mark test to run with torchaudio>=0.12'     )





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    List[Any]     )  ->     Any:
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			__lowerCamelCase					:	str	       =				tmp_path_factory.getbasetemp() / 'cache'
			__lowerCamelCase					:	Dict	       =				test_hf_cache_home / 'datasets'
			__lowerCamelCase					:	int	       =				test_hf_cache_home / 'metrics'
			__lowerCamelCase					:	List[str]	       =				test_hf_cache_home / 'modules'
			monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'							,				str(A__     )     )
			__lowerCamelCase					:	List[Any]	       =				test_hf_datasets_cache / 'downloads'
			monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'							,				str(A__     )     )
			__lowerCamelCase					:	Optional[int]	       =				test_hf_datasets_cache / 'downloads' / 'extracted'
			monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'							,				str(A__     )     )





@pytest.fixture(autouse=A__							,				scope='session'     )
def 						UpperCAmelCase				(			)  ->     Any:
			datasets.disable_progress_bar()





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     Dict:
			# don't take tests into account when counting downloads
			monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'							,				A__     )






@pytest.fixture
def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     Optional[Any]:
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'							,				A__     )



