code: 
  '''simple docstring'''
  
  
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       copy.deepcopy(lowercase__	)
  						lowercase__							 =       sorted(lowercase__	)
  						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
  						return quantized
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
  						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
  						# count number of elements per group
  						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
  						lowercase__							 =       [0] + bins + [np.inf]
  						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
  						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
  						return groups
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															raise ValueError(
  															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
  									lowercase__							 =       sampler
  									lowercase__							 =       group_ids
  									lowercase__							 =       batch_size
  
  
  
  			def __iter__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  
  									lowercase__							 =       0
  									for idx in self.sampler:
  															lowercase__							 =       self.group_ids[idx]
  															buffer_per_group[group_id].append(lowerCamelCase__       )
  															samples_per_group[group_id].append(lowerCamelCase__       )
  															if len(buffer_per_group[group_id]       ) == self.batch_size:
  																					yield buffer_per_group[group_id]  # TODO
  																					num_batches += 1
  																					del buffer_per_group[group_id]
  															assert len(buffer_per_group[group_id]       ) < self.batch_size
  
  									# now we have run out of elements that satisfy
  									# the group criteria, let's return the remaining
  									# elements so that the size of the sampler is
  									# deterministic
  									lowercase__							 =       len(self       )
  									lowercase__							 =       expected_num_batches - num_batches
  									if num_remaining > 0:
  															# for the remaining batches, group the batches by similar lengths
  															lowercase__							 =       []
  															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
  																					batch_idx.extend(lowerCamelCase__       )
  																					if len(lowerCamelCase__       ) >= self.batch_size:
  																											yield batch_idx[: self.batch_size]
  																											lowercase__							 =       batch_idx[self.batch_size :]
  																											num_remaining -= 1
  															if len(lowerCamelCase__       ) > 0:
  																					yield batch_idx
  																					num_remaining -= 1
  									assert num_remaining == 0
  
  
  
  			def __len__(					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
  
style_context: 
  '''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  _A		(							lowercase__=None		,		lowercase__=None	):
  						return field(default_factory=lambda: default		,		metadata=lowercase__	)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The csv file to plot."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Disable logarithmic scale when plotting"""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }		,						)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,						)
  			lowerCamelCase							:							Optional[List[str]]       		=  list_field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}				)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												int(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												float(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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  _A		(							):
  						lowercase__							 =       HfArgumentParser(lowercase__	)
  						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
  						lowercase__							 =       Plot(args=lowercase__	)
  						plot.plot()
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  