
"""simple docstring"""
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
SCREAMING_SNAKE_CASE_			      =					TypeVar('''U''')
class      lowerCAmelCase_						(							Generic[T, U]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_							)   ->       List[str]:
													__lowerCAmelCase        =       key
													__lowerCAmelCase        =       val
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       None




							def __repr__(				self							)   ->       str:
													return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
													    self.key  ,							self.val  ,							self.freq  ,							self.next is not None  ,							self.prev is not None							)
class      lowerCAmelCase_						(							Generic[T, U]				):



							'''simple docstring'''





							def __init__(				self							)   ->       None:
													__lowerCAmelCase        =       DoubleLinkedListNode(snake_case_  ,							snake_case_							)
													__lowerCAmelCase        =       DoubleLinkedListNode(snake_case_  ,							snake_case_							)
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.rear, self.head




							def __repr__(				self							)   ->       str:
													__lowerCAmelCase        =       ["""DoubleLinkedList"""]
													__lowerCAmelCase        =       self.head
													while node.next is not None:
																			rep.append(str(snake_case_							)							)
																			__lowerCAmelCase        =       node.next
													rep.append(str(self.rear							)							)
													return ",\n    ".join(snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													__lowerCAmelCase        =       self.rear.prev

													# All nodes other than self.head are guaranteed to have non-None previous
													assert previous is not None

													__lowerCAmelCase        =       node
													__lowerCAmelCase        =       previous
													__lowerCAmelCase        =       node
													__lowerCAmelCase        =       self.rear
													node.freq += 1
													self._position_node(snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       None:

													while node.prev is not None and node.prev.freq > node.freq:
																			# swap node with previous node
																			__lowerCAmelCase        =       node.prev

																			__lowerCAmelCase        =       previous_node.prev
																			__lowerCAmelCase        =       node.prev
																			__lowerCAmelCase        =       previous_node
																			__lowerCAmelCase        =       node




							def  A__		(				self  ,							snake_case_							)   ->       DoubleLinkedListNode[T, U] | None:

													if node.prev is None or node.next is None:
																			return None

													__lowerCAmelCase        =       node.next
													__lowerCAmelCase        =       node.prev
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       None
													return node
class      lowerCAmelCase_						(							Generic[T, U]				):



							'''simple docstring'''





							_snake_case		     =						{}




							def __init__(				self  ,							snake_case_							)   ->       Dict:
													__lowerCAmelCase        =       DoubleLinkedList()
													__lowerCAmelCase        =       capacity
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       {}




							def __repr__(				self							)   ->       str:
													return (
													    f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
													    f"""capacity={self.capacity}, current_size={self.num_keys})"""
													)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return key in self.cache




							def  A__		(				self  ,							snake_case_							)   ->       U | None:

													if key in self.cache:
																			self.hits += 1
																			__lowerCAmelCase        =       self.cache[key]
																			__lowerCAmelCase        =       self.list.remove(self.cache[key]							)
																			assert node == value_node

																			# node is guaranteed not None because it is in self.cache
																			assert node is not None
																			self.list.add(snake_case_							)
																			return node.val
													self.miss += 1
													return None




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       None:

													if key not in self.cache:
																			if self.num_keys >= self.capacity:
																									# delete first node when over capacity
																									__lowerCAmelCase        =       self.list.head.next

																									# guaranteed to have a non-None first node when num_keys > 0
																									# explain to type checker via assertions
																									assert first_node is not None
																									assert first_node.key is not None
																									assert self.list.remove(snake_case_							) is not None
																									# first_node guaranteed to be in list

																									del self.cache[first_node.key]
																									self.num_keys -= 1
																			__lowerCAmelCase        =       DoubleLinkedListNode(snake_case_  ,							snake_case_							)
																			self.list.add(self.cache[key]							)
																			self.num_keys += 1

													else:
																			__lowerCAmelCase        =       self.list.remove(self.cache[key]							)
																			assert node is not None  # node guaranteed to be in list
																			__lowerCAmelCase        =       value
																			self.list.add(snake_case_							)




							@classmethod
							def  A__		(				cls  ,							snake_case_ = 128							)   ->       Callable[[Callable[[T], U]], Callable[..., U]]:

													def cache_decorator_inner(snake_case_							) -> Callable[..., U]:
																			def cache_decorator_wrapper(*snake_case_							) -> U:
																									if func not in cls.decorator_function_to_instance_map:
																															__lowerCAmelCase        =       LFUCache(snake_case_							)

																									__lowerCAmelCase        =       cls.decorator_function_to_instance_map[func].get(args[0]							)
																									if result is None:
																															__lowerCAmelCase        =       func(*snake_case_							)
																															cls.decorator_function_to_instance_map[func].put(args[0]  ,							snake_case_							)
																									return result

																			def cache_info() -> LFUCache[T, U]:
																									return cls.decorator_function_to_instance_map[func]

																			setattr(snake_case_  ,							"""cache_info"""  ,							snake_case_							)  # noqa: B010

																			return cache_decorator_wrapper

													return cache_decorator_inner


if __name__ == "__main__":
			import doctest

			doctest.testmod()


"""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()
