
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        lowerCamelCase__    (    __lowerCAmelCase  :  Any=None   ,							__lowerCAmelCase  :  int=None    ):




			"""simple docstring"""


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





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






def        lowerCamelCase__    (    __lowerCAmelCase  :  int    ):




			"""simple docstring"""


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






def        lowerCamelCase__    (    __lowerCAmelCase  :  Tuple    ):




			"""simple docstring"""


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





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     args
							lowerCAmelCase_      =     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

							with open(self.args.csv_file    ,    newline=""					) as csv_file:
										lowerCAmelCase_      =     csv.DictReader(__UpperCamelCase					)
										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					)    ->				Optional[int]:
							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=__UpperCamelCase    ,    )
													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(__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."""

							lowerCAmelCase_      =     title_str[:-4]
							lowerCAmelCase_      =     "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        lowerCamelCase__    (    ):




			"""simple docstring"""


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


if __name__ == "__main__":
		main()





class      _lowerCAmelCase :




				def __init__( self					)    ->				None:
							# numbermap keys are the number and its values are lists of indexes sorted
							# in ascending order
							lowerCAmelCase_      =     {}
							# indexmap keys are an index and it's values are the number at that index
							lowerCAmelCase_      =     {}




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_delete() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == item:
													array.pop(_UpperCamelCase					)
													return array
										elif array[mid] < item:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1
							raise ValueError(
							    "Either the item is not in the array or the array was unsorted"					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_insert() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == index:
													# If the item already exists in the array,
													# insert it after the existing item
													array.insert(mid + 1    ,    _UpperCamelCase					)
													return array
										elif array[mid] < index:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
							array.insert(_UpperCamelCase    ,    _UpperCamelCase					)
							return array




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				None:
							# Remove previous index
							if index in self.indexmap:
										lowerCAmelCase_      =     self.indexmap[index]
										if len(self.numbermap[n]					) == 1:
													del self.numbermap[n]
										else:
													lowerCAmelCase_      =     self.binary_search_delete(self.numbermap[n]    ,    _UpperCamelCase					)

        # Set new index
							lowerCAmelCase_      =     number

							# Number not seen before or empty so insert number value
							if number not in self.numbermap:
										lowerCAmelCase_      =     [index]

							# Here we need to perform a binary search insertion in order to insert
							# The item in the correct place
							else:
										lowerCAmelCase_      =     self.binary_search_insert(
										    self.numbermap[number]    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				int:
							return self.numbermap.get(_UpperCamelCase    ,    [-1]					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



