from __future__ import annotations

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

__a  :					List[str]          =  TypeVar("""T""")
__a  :					int          =  TypeVar("""U""")




class        _UpperCamelCase				(						Generic[T, U]			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							key
							__lowercase			      =							val
							__lowercase			      =							0
							__lowercase			      =							None
							__lowercase			      =							None





			def __repr__(       self						)					-> str:

							'''simple docstring'''


							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        _UpperCamelCase				(						Generic[T, U]			):


			"""simple docstring"""






			def __init__(       self						)					-> None:

							'''simple docstring'''


							__lowercase			      =							DoubleLinkedListNode(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							DoubleLinkedListNode(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase      ,					__lowercase			      =							self.rear, self.head





			def __repr__(       self						)					-> str:

							'''simple docstring'''


							__lowercase			      =							['''DoubleLinkedList''']
							__lowercase			      =							self.head
							while node.next is not None:
											rep.append(str(lowerCAmelCase__						)						)
											__lowercase			      =							node.next
							rep.append(str(self.rear						)						)
							return ",\n    ".join(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> None:

							'''simple docstring'''


							__lowercase			      =							self.rear.prev

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

							__lowercase			      =							node
							__lowercase			      =							previous
							__lowercase			      =							node
							__lowercase			      =							self.rear
							node.freq += 1
							self._position_node(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> None:

							'''simple docstring'''


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

											__lowercase			      =							previous_node.prev
											__lowercase			      =							node.prev
											__lowercase			      =							previous_node
											__lowercase			      =							node





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> DoubleLinkedListNode[T, U] | None:

							'''simple docstring'''


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

							__lowercase			      =							node.next
							__lowercase			      =							node.prev
							__lowercase			      =							None
							__lowercase			      =							None
							return node




class        _UpperCamelCase				(						Generic[T, U]			):


			"""simple docstring"""






			__a  :			dict[Callable[[T], U], LFUCache[T, U]]      			=						{}





			def __init__(       self				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							DoubleLinkedList()
							__lowercase			      =							capacity
							__lowercase			      =							0
							__lowercase			      =							0
							__lowercase			      =							0
							__lowercase			      =							{}





			def __repr__(       self						)					-> str:

							'''simple docstring'''


							return (
							    F"CacheInfo(hits={self.hits}, misses={self.miss}, "
							    F"capacity={self.capacity}, current_size={self.num_keys})"
							)





			def __contains__(       self				,       lowerCAmelCase__						)					-> bool:

							'''simple docstring'''


							return key in self.cache





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> U | None:

							'''simple docstring'''


							if key in self.cache:
											self.hits += 1
											__lowercase			      =							self.cache[key]
											__lowercase			      =							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(lowerCAmelCase__						)
											return node.val
							self.miss += 1
							return None





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> None:

							'''simple docstring'''


							if key not in self.cache:
											if self.num_keys >= self.capacity:
															# delete first node when over capacity
															__lowercase			      =							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(lowerCAmelCase__						) is not None
															# first_node guaranteed to be in list

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

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





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__ = 1_28						)					-> Callable[[Callable[[T], U]], Callable[..., U]]:

							'''simple docstring'''


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

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

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

											setattr(lowerCAmelCase__				,       '''cache_info'''				,       lowerCAmelCase__						)  # noqa: B010

											return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()
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						(   lowercase=None							,      lowercase=None							):







				"""simple docstring"""



				return field(default_factory=lambda: default							,      metadata=lowercase							)




@dataclass
class        _UpperCamelCase				:


			"""simple docstring"""






			__a  :			str      			=						field(
			    metadata={'''help''': '''The csv file to plot.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Disable logarithmic scale when plotting'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={
			        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
			    }						,)
			__a  :			Optional[str]      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}						,)
			__a  :			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     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				try:
								float(lowercase							)
								return True
				except ValueError:
								return False




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> str:

							'''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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Union[str, Any]:

							'''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     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							HfArgumentParser(lowercase							)
				__lowercase			      =							parser.parse_args_into_dataclasses()[0]
				__lowercase			      =							Plot(args=lowercase							)
				plot.plot()


if __name__ == "__main__":
		main()