
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__A				=datasets.utils.logging.get_logger(__name__)




@dataclass
class     _SCREAMING_SNAKE_CASE     (      datasets.BuilderConfig						):
	lowerCAmelCase__         = 1_00_00
	lowerCAmelCase__         = None
	lowerCAmelCase__         = None




class     _SCREAMING_SNAKE_CASE     (      datasets.ArrowBasedBuilder						):
	lowerCAmelCase__         = ParquetConfig



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					return datasets.DatasetInfo(features=self.config.features )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Tuple:
					if not self.config.data_files:
									raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
					lowerCamelCase_						=			dl_manager.download_and_extract(self.config.data_files )
					if isinstance(lowercase						,  (str, list, tuple) ):
									lowerCamelCase_						=			data_files
									if isinstance(lowercase						,  lowercase ):
													lowerCamelCase_						=			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									lowerCamelCase_						=			[dl_manager.iter_files(lowercase ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,  gen_kwargs={"files": files} )]
					lowerCamelCase_						=			[]
					for split_name, files in data_files.items():
									if isinstance(lowercase						,  lowercase ):
													lowerCamelCase_						=			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									lowerCamelCase_						=			[dl_manager.iter_files(lowercase ) for file in files]
									# Infer features is they are stoed in the arrow schema
									if self.info.features is None:
													for file in itertools.chain.from_iterable(lowercase ):
																	with open(lowercase						,  "rb" ) as f:
																					lowerCamelCase_						=			datasets.Features.from_arrow_schema(pq.read_schema(lowercase ) )
																	break
									splits.append(datasets.SplitGenerator(name=lowercase						,  gen_kwargs={"files": files} ) )
					return splits



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       pa.Table:
					if self.info.features is not None:
									# more expensive cast to support nested features with keys in a different order
									# allows str <-> int/float or str to Audio for example
									lowerCamelCase_						=			table_cast(lowercase						,  self.info.features.arrow_schema )
					return pa_table



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Dict:
					lowerCamelCase_						=			self.info.features.arrow_schema if self.info.features is not None else None
					if self.info.features is not None and self.config.columns is not None:
									if sorted(field.name for field in schema ) != sorted(self.config.columns ):
													raise ValueError(
													    f'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'' )
					for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase ) ):
									with open(lowercase						,  "rb" ) as f:
													lowerCamelCase_						=			pq.ParquetFile(lowercase )
													try:
																	for batch_idx, record_batch in enumerate(
																	    parquet_file.iter_batches(batch_size=self.config.batch_size						,  columns=self.config.columns ) ):
																					lowerCamelCase_						=			pa.Table.from_batches([record_batch] )
																					# Uncomment for debugging (will print the Arrow table size and elements)
																					# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																					# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																					yield f'{file_idx}_{batch_idx}', self._cast_table(lowercase )
													except ValueError as e:
																	logger.error(f'Failed to read file \'{file}\' with error {type(lowercase )}: {e}' )
																	raise






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()






