



"""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 	snake_case__  (						__lowerCamelCase							:       Optional[int]=None				,						__lowerCamelCase							:       Union[str, Any]=None      ):
			"""simple docstring"""
			return field(default_factory=lambda: default				,						metadata=__lowerCamelCase      )

@dataclass
class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





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






def 	snake_case__  (						__lowerCamelCase							:       Any      ):
			"""simple docstring"""
			try:
						int(__lowerCamelCase      )
						return True
			except ValueError:
						return False






def 	snake_case__  (						__lowerCamelCase							:       List[str]      ):
			"""simple docstring"""
			try:
						float(__lowerCamelCase      )
						return True
			except ValueError:
						return False

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    List[Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						List[str]						      =args
								lowerCamelCase__						:						str						      =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

								with open(self.args.csv_file,    newline=''''''	) as csv_file:
											lowerCamelCase__						:						Dict						      =csv.DictReader(lowerCamelCase	)
											for row in reader:
														lowerCamelCase__						:						Tuple						      =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__						:						Union[str, Any]						      =int(row['''result''']	)
														elif can_convert_to_float(row['''result''']	):
																	# value is not None
																	lowerCamelCase__						:						Tuple						      =float(row['''result''']	)




					def 						snake_case			(			self				:    Optional[int]	)->   Union[str, Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Any						      =plt.subplots()
								lowerCamelCase__						:						Any						      ='''Time usage''' if self.args.is_time else '''Memory usage'''
								lowerCamelCase__						:						Tuple						      =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__						:						Optional[Any]						      =sorted(set(self.result_dict[model_name]['''bsz''']	)	)
											lowerCamelCase__						:						List[str]						      =sorted(set(self.result_dict[model_name]['''seq_len''']	)	)
											lowerCamelCase__						:						List[str]						      =self.result_dict[model_name]['''result''']

											((lowerCamelCase__)      ,  (lowerCamelCase__))						:						str						      =(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											lowerCamelCase__						:						Optional[int]						      =(
											    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__						:						List[str]						      =np.asarray(
																	    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],    dtype=lowerCamelCase,    )
														else:
																	lowerCamelCase__						:						int						      =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__))						:						Optional[Any]						      =(
														    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
														)

														lowerCamelCase__						:						Any						      =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.'''

								lowerCamelCase__						:						Dict						      =title_str[:-4]
								lowerCamelCase__						:						List[Any]						      ='''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 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						int						      =HfArgumentParser(__lowerCamelCase      )
			lowerCamelCase__						:						int						      =parser.parse_args_into_dataclasses()[0]
			lowerCamelCase__						:						Optional[int]						      =Plot(args=__lowerCamelCase      )
			plot.plot()


if __name__ == "__main__":
		main()








"""simple docstring"""






from __future__ import annotations

import sys


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					letter
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					{}






				def __repr__(    self  ):
								return f'''{self.letter}:{self.freq}'''


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					left
								SCREAMING_SNAKE_CASE__  				=					right


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					{}
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												SCREAMING_SNAKE_CASE__  				=					chars[c] + 1 if c in chars else 1
				return sorted((Letter(lowerCamelCase_    ,       lowerCamelCase_	) for c, f in chars.items())    ,       key=lambda lowerCamelCase_	: x.freq	)


def __lowercase  (   lowerCamelCase_	:   list[Letter]	):
				SCREAMING_SNAKE_CASE__  				=					letters  # type: ignore
				while len(lowerCamelCase_	) > 1:
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					left.freq + right.freq
								SCREAMING_SNAKE_CASE__  				=					TreeNode(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
								response.append(lowerCamelCase_	)
								response.sort(key=lambda lowerCamelCase_	: x.freq	)
				return response[0]


def __lowercase  (   lowerCamelCase_	:   Letter | TreeNode    ,       lowerCamelCase_	:   str	):
				if isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								SCREAMING_SNAKE_CASE__  				=					bitstring
								return [root]
				SCREAMING_SNAKE_CASE__  				=					root  # type: ignore
				SCREAMING_SNAKE_CASE__  				=					[]
				letters += traverse_tree(treenode.left    ,       bitstring + "0"	)
				letters += traverse_tree(treenode.right    ,       bitstring + "1"	)
				return letters


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					parse_file(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					build_tree(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					{
				    k: v for letter in traverse_tree(lowerCamelCase_    ,       ""	) for k, v in letter.bitstring.items()
				}
				print(F'''Huffman Coding  of {file_path}: '''	)
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												print(letters[c]    ,       end=" "	)
				print()


if __name__ == "__main__":
	# pass the file path to the huffman function
	huffman(sys.argv[1])
