
"""simple docstring"""
import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


SCREAMING_SNAKE_CASE_			      =					(30, 487)
SCREAMING_SNAKE_CASE_			      =					(36, 1_024)
SCREAMING_SNAKE_CASE_			      =					(100, 100)
SCREAMING_SNAKE_CASE_			      =					100

SCREAMING_SNAKE_CASE_			      =					datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_			      =					os.path.split(__file__)
SCREAMING_SNAKE_CASE_			      =					os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))

@get_duration
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						with ArrowWriter(features=_lowerCAmelCase				,  path=os.path.join(_lowerCAmelCase				,  """beta.arrow"""		)		) as writer:
												for key, record in dummy_data:
																		__lowerCAmelCase        =       my_features.encode_example(_lowerCAmelCase		)
																		writer.write(_lowerCAmelCase		)
												__lowerCAmelCase ,      __lowerCAmelCase        =       writer.finalize()

@get_duration
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       datasets.Dataset.from_file(
						    filename=os.path.join(_lowerCAmelCase				,  """beta.arrow"""		)				,  info=datasets.DatasetInfo(features=_lowerCAmelCase		)		)
						for _ in dataset:
												pass

@get_duration
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       datasets.Dataset.from_file(
						    filename=os.path.join(_lowerCAmelCase				,  """beta.arrow"""		)				,  info=datasets.DatasetInfo(features=_lowerCAmelCase		)		)
						dataset.set_format("""numpy"""		)
						for _ in dataset:
												pass

@get_duration
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       10
						__lowerCAmelCase        =       datasets.Dataset.from_file(
						    filename=os.path.join(_lowerCAmelCase				,  """beta.arrow"""		)				,  info=datasets.DatasetInfo(features=_lowerCAmelCase		)		)
						for i in range(0				,  len(_lowerCAmelCase		)				,  _lowerCAmelCase		):
												__lowerCAmelCase        =       dataset[i : i + batch_size]

@get_duration
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       10
						__lowerCAmelCase        =       datasets.Dataset.from_file(
						    filename=os.path.join(_lowerCAmelCase				,  """beta.arrow"""		)				,  info=datasets.DatasetInfo(features=_lowerCAmelCase		)		)
						dataset.set_format("""numpy"""		)
						for i in range(0				,  len(_lowerCAmelCase		)				,  _lowerCAmelCase		):
												__lowerCAmelCase        =       dataset[i : i + batch_size]

@get_duration
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       datasets.Dataset.from_file(
						    filename=os.path.join(_lowerCAmelCase				,  """beta.arrow"""		)				,  info=datasets.DatasetInfo(features=_lowerCAmelCase		)		)
						for col in feats:
												__lowerCAmelCase        =       dataset[col]

@get_duration
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       datasets.Dataset.from_file(
						    filename=os.path.join(_lowerCAmelCase				,  """beta.arrow"""		)				,  info=datasets.DatasetInfo(features=_lowerCAmelCase		)		)
						dataset.set_format("""numpy"""		)
						for col in feats:
												__lowerCAmelCase        =       dataset[col]

def 				lowercase      ():
						__lowerCAmelCase        =       {}
						__lowerCAmelCase        =       (
						    read_unformated,
						    read_formatted_as_numpy,
						    read_batch_unformated,
						    read_batch_formatted_as_numpy,
						    read_col_unformated,
						    read_col_formatted_as_numpy,
						)
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase        =       datasets.Features({"""image""": ArrayaD(_lowerCAmelCase				,  dtype="""float32"""		)}		)
												__lowerCAmelCase        =       generate_examples(features=_lowerCAmelCase				,  num_examples=_lowerCAmelCase		)
												__lowerCAmelCase        =       write(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)
												for read_func in read_functions:
																		__lowerCAmelCase        =       read_func(_lowerCAmelCase				,  _lowerCAmelCase		)

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
												# )
												__lowerCAmelCase        =       datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""		)		)		)}		)
												__lowerCAmelCase        =       generate_examples(
												    features=_lowerCAmelCase				,  num_examples=_lowerCAmelCase				,  seq_shapes={"""image""": SPEED_TEST_SHAPE}		)
												__lowerCAmelCase        =       write(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)
												for read_func in read_functions:
																		__lowerCAmelCase        =       read_func(_lowerCAmelCase				,  _lowerCAmelCase		)

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
												# )
												__lowerCAmelCase        =       datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""		)		)}		)
												__lowerCAmelCase        =       generate_examples(
												    features=_lowerCAmelCase				,  num_examples=_lowerCAmelCase				,  seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}				,  )
												__lowerCAmelCase        =       write(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)
												for read_func in read_functions:
																		__lowerCAmelCase        =       read_func(_lowerCAmelCase				,  _lowerCAmelCase		)

						with open(_lowerCAmelCase				,  """wb"""		) as f:
												f.write(json.dumps(_lowerCAmelCase		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
			benchmark_array_xd()


"""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      (_lowerCAmelCase=None				,  _lowerCAmelCase=None		):
						return field(default_factory=lambda: default				,  metadata=_lowerCAmelCase		)
@dataclass
class      lowerCAmelCase_						:



							'''simple docstring'''





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

def 				lowercase      (_lowerCAmelCase		):
						try:
												int(_lowerCAmelCase		)
												return True
						except ValueError:
												return False

def 				lowercase      (_lowerCAmelCase		):
						try:
												float(_lowerCAmelCase		)
												return True
						except ValueError:
												return False
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       List[Any]:
													__lowerCAmelCase        =       args
													__lowerCAmelCase        =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

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




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       plt.subplots()
													__lowerCAmelCase        =       """Time usage""" if self.args.is_time else """Memory usage"""
													__lowerCAmelCase        =       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        =       sorted(set(self.result_dict[model_name]["""bsz"""]							)							)
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""seq_len"""]							)							)
																			__lowerCAmelCase        =       self.result_dict[model_name]["""result"""]

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

																			__lowerCAmelCase        =       (
																			    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        =       np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,							dtype=snake_case_  ,							)
																									else:
																															__lowerCAmelCase        =       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))        =       (
																									    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																									)

																									__lowerCAmelCase        =       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."""

													__lowerCAmelCase        =       title_str[:-4]
													__lowerCAmelCase        =       """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      ():
						__lowerCAmelCase        =       HfArgumentParser(_lowerCAmelCase		)
						__lowerCAmelCase        =       parser.parse_args_into_dataclasses()[0]
						__lowerCAmelCase        =       Plot(args=_lowerCAmelCase		)
						plot.plot()


if __name__ == "__main__":
			main()
