'''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 __a (	__lowerCamelCase			:						Any=None ,		__lowerCamelCase			:						int=None				)	->							Optional[int]:
      '''simple docstring'''


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





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




def __a (	__lowerCamelCase			:						List[str]				)	->							Tuple:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						Any				)	->							Dict:
      '''simple docstring'''


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





class 							lowercase						:


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[Any])      ->					List[Any]:
         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 __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Union[str, Any]:
         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 __a (	)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      HfArgumentParser(__lowerCamelCase				)
      lowercase_     =      parser.parse_args_into_dataclasses()[0]
      lowercase_     =      Plot(args=__lowerCamelCase				)
      plot.plot()


if __name__ == "__main__":
       main()







'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	bool = True)      ->					None:
         lowercase_     =      {}  # dictionary of lists
         lowercase_     =      directed


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T)      ->					GraphAdjacencyList[T]:

         if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      [source_vertex]
         else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      []
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      []

         return self


   def __repr__(   self				:	str)      ->					str:
         return pformat(self.adj_list)






