






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    (      __lowerCAmelCase=None ,		__lowerCAmelCase=None	)->  Dict:





						"""simple docstring"""



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





@dataclass
class __lowerCamelCase :





	"""simple docstring"""






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



def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



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


def __A    (      __lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



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





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			args
							_UpperCAmelCase									=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			plt.subplots()
							_UpperCAmelCase									=			'Time usage' if self.args.is_time else 'Memory usage'
							_UpperCAmelCase									=			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()    ):
													_UpperCAmelCase									=			sorted(set(self.result_dict[model_name]['bsz']    )    )
													_UpperCAmelCase									=			sorted(set(self.result_dict[model_name]['seq_len']    )    )
													_UpperCAmelCase									=			self.result_dict[model_name]['result']

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

													_UpperCAmelCase									=			(
													    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:
																									_UpperCAmelCase									=			np.asarray(
																									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,    dtype=UpperCAmelCase       ,    )
																			else:
																									_UpperCAmelCase									=			np.asarray(
																									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,    dtype=np.floataa       ,    )

																			((_UpperCAmelCase)					, (_UpperCAmelCase))									=			(
																			    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																			)

																			_UpperCAmelCase									=			np.asarray(UpperCAmelCase       ,    UpperCAmelCase    )[: len(UpperCAmelCase    )]
																			plt.scatter(
																			    UpperCAmelCase       ,    UpperCAmelCase       ,    label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""    )
																			plt.plot(UpperCAmelCase       ,    UpperCAmelCase       ,    '--'    )

													title_str += F""" {label_model_name} vs."""

							_UpperCAmelCase									=			title_str[:-4]
							_UpperCAmelCase									=			'Time in s' if self.args.is_time else 'Memory in MB'

							# plot
							plt.title(UpperCAmelCase    )
							plt.xlabel(UpperCAmelCase    )
							plt.ylabel(UpperCAmelCase    )
							plt.legend()

							if self.args.figure_png_file is not None:
													plt.savefig(self.args.figure_png_file    )
							else:
													plt.show()



def __A    (      )->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			HfArgumentParser(__lowerCAmelCase	)
						_UpperCAmelCase									=			parser.parse_args_into_dataclasses()[0]
						_UpperCAmelCase									=			Plot(args=__lowerCAmelCase	)
						plot.plot()


if __name__ == "__main__":
		main()








class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase = ""       ,    UpperCAmelCase = False    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{}

							# A node will be a leaf if the tree contains its word
							_UpperCAmelCase									=			is_leaf

							_UpperCAmelCase									=			prefix

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			0
							for q, w in zip(self.prefix       ,    UpperCAmelCase    ):
													if q != w:
																			break

													x += 1

							return self.prefix[:x], self.prefix[x:], word[x:]

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							for word in words:
													self.insert(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if self.prefix == word:
													_UpperCAmelCase									=			True

							# Case 2: The node has no edges that have a prefix to the word
							# Solution: We create an edge from the current node to a new one
							# containing the word
							elif word[0] not in self.nodes:
													_UpperCAmelCase									=			RadixNode(prefix=UpperCAmelCase       ,    is_leaf=UpperCAmelCase    )

							else:
													_UpperCAmelCase									=			self.nodes[word[0]]
													_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			incoming_node.match(
													    UpperCAmelCase    )

													# Case 3: The node prefix is equal to the matching
													# Solution: We insert remaining word on the next node
													if remaining_prefix == "":
																			self.nodes[matching_string[0]].insert(UpperCAmelCase    )

													# Case 4: The word is greater equal to the matching
													# Solution: Create a node in between both nodes, change
													# prefixes and add the new node for the remaining word
													else:
																			_UpperCAmelCase									=			remaining_prefix

																			_UpperCAmelCase									=			self.nodes[matching_string[0]]
																			_UpperCAmelCase									=			RadixNode(UpperCAmelCase       ,    UpperCAmelCase    )
																			_UpperCAmelCase									=			aux_node

																			if remaining_word == "":
																									_UpperCAmelCase									=			True
																			else:
																									self.nodes[matching_string[0]].insert(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.nodes.get(word[0]       ,    UpperCAmelCase    )
							if not incoming_node:
													return False
							else:
													_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			incoming_node.match(
													    UpperCAmelCase    )
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																			return False
													# This applies when the word and the prefix are equal
													elif remaining_word == "":
																			return incoming_node.is_leaf
													# We have word remaining so we check the next node
													else:
																			return incoming_node.find(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.nodes.get(word[0]       ,    UpperCAmelCase    )
							if not incoming_node:
													return False
							else:
													_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			incoming_node.match(
													    UpperCAmelCase    )
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																			return False
													# We have word remaining so we check the next node
													elif remaining_word != "":
																			return incoming_node.delete(UpperCAmelCase    )
													else:
																			# If it is not a leaf, we don't have to delete
																			if not incoming_node.is_leaf:
																									return False
																			else:
																									# We delete the nodes if no edges go from it
																									if len(incoming_node.nodes    ) == 0:
																															del self.nodes[word[0]]
																															# We merge the current node with its only child
																															if len(self.nodes    ) == 1 and not self.is_leaf:
																																					_UpperCAmelCase									=			list(self.nodes.values()    )[0]
																																					_UpperCAmelCase									=			merging_node.is_leaf
																																					self.prefix += merging_node.prefix
																																					_UpperCAmelCase									=			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																									elif len(incoming_node.nodes    ) > 1:
																															_UpperCAmelCase									=			False
																									# If there is 1 edge, we merge it with its child
																									else:
																															_UpperCAmelCase									=			list(incoming_node.nodes.values()    )[0]
																															_UpperCAmelCase									=			merging_node.is_leaf
																															incoming_node.prefix += merging_node.prefix
																															_UpperCAmelCase									=			merging_node.nodes

																									return True

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = 0    ):
							"""simple docstring"""







							if self.prefix != "":
													print('-' * height       ,    self.prefix       ,    '  (leaf)' if self.is_leaf else ''    )

							for value in self.nodes.values():
													value.print_tree(height + 1    )



def __A    (      )->  bool:





						"""simple docstring"""



						_UpperCAmelCase									=			'banana bananas bandana band apple all beast'.split()
						_UpperCAmelCase									=			RadixNode()
						root.insert_many(__lowerCAmelCase	)

						assert all(root.find(__lowerCAmelCase	) for word in words	)
						assert not root.find('bandanas'	)
						assert not root.find('apps'	)
						root.delete('all'	)
						assert not root.find('all'	)
						root.delete('banana'	)
						assert not root.find('banana'	)
						assert root.find('bananas'	)

						return True


def __A    (      )->  None:





						"""simple docstring"""



						assert test_trie()


def __A    (      )->  None:





						"""simple docstring"""



						_UpperCAmelCase									=			RadixNode()
						_UpperCAmelCase									=			'banana bananas bandanas bandana band apple all beast'.split()
						root.insert_many(__lowerCAmelCase	)

						print('Words:' ,		__lowerCAmelCase	)
						print('Tree:'	)
						root.print_tree()


if __name__ == "__main__":
		main()
