


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_     (  snake_case=None			,							snake_case=None     )  ->				Union[str, Any]:
       return field(default_factory=lambda: default			,							metadata=snake_case     )





@dataclass
class   __a			:
 __lowercase :							str        = field(
     metadata={'help': 'The csv file to plot.'}       ,       )
 __lowercase :							bool        = field(
     default=__UpperCamelCase       ,       metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}       ,       )
 __lowercase :							bool        = field(
     default=__UpperCamelCase       ,       metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}       ,       )
 __lowercase :							bool        = field(
     default=__UpperCamelCase       ,       metadata={'help': 'Disable logarithmic scale when plotting'}       ,       )
 __lowercase :							bool        = field(
     default=__UpperCamelCase       ,       metadata={
         'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
     }       ,       )
 __lowercase :							Optional[str]        = field(
     default=__UpperCamelCase       ,       metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}       ,       )
 __lowercase :							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    snake_case_     (  snake_case     )  ->				List[Any]:
       try:
              int(snake_case     )
              return True
       except ValueError:
              return False




def    snake_case_     (  snake_case     )  ->				Union[str, Any]:
       try:
              float(snake_case     )
              return True
       except ValueError:
              return False





class   __a			:
 def __init__(   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   args
        lowercase__:   Tuple  			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

        with open(self.args.csv_file				, newline='' ) as csv_file:
               lowercase__:   Tuple  			=   csv.DictReader(lowerCAmelCase__ )
               for row in reader:
                      lowercase__:   int  			=   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__:   Any  			=   int(row['result'] )
                      elif can_convert_to_float(row['result'] ):
                             # value is not None
                             lowercase__:   Dict  			=   float(row['result'] )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        lowercase__			,						lowercase__:   List[str]  			=   plt.subplots()
        lowercase__:   List[str]  			=   'Time usage' if self.args.is_time else 'Memory usage'
        lowercase__:   List[str]  			=   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__:   Dict  			=   sorted(set(self.result_dict[model_name]['bsz'] ) )
               lowercase__:   int  			=   sorted(set(self.result_dict[model_name]['seq_len'] ) )
               lowercase__:   Any  			=   self.result_dict[model_name]['result']

               ((lowercase__)			,						(lowercase__)):   Any  			=   (
                   (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
               )

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

                      lowercase__:   str  			=   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__:   Optional[int]  			=   title_str[:-4]
        lowercase__:   int  			=   '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_     (  )  ->				Tuple:
       lowercase__:   List[Any]  			=   HfArgumentParser(snake_case     )
       lowercase__:   Dict  			=   parser.parse_args_into_dataclasses()[0]
       lowercase__:   str  			=   Plot(args=snake_case     )
       plot.plot()


if __name__ == "__main__":
 main()






__lowerCAmelCase		       =							{
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				list[str]:
       lowercase__:   int  			=   set()
       # keep track of all the paths to be checked
       lowercase__:   Optional[int]  			=   [[start]]

       # return path if start is goal
       if start == goal:
              return [start]

       # keeps looping until all possible paths have been checked
       while queue:
              # pop the first path from the queue
              lowercase__:   List[Any]  			=   queue.pop(0     )
              # get the last node from the path
              lowercase__:   Optional[int]  			=   path[-1]
              if node not in explored:
                     lowercase__:   Optional[Any]  			=   graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            lowercase__:   Tuple  			=   list(snake_case     )
                            new_path.append(snake_case     )
                            queue.append(snake_case     )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(snake_case     )

    # in case there's no path between the 2 nodes
       return []




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       lowercase__:   Tuple  			=   [start]
       lowercase__:   List[Any]  			=   set(snake_case     )
       # Keep tab on distances from `start` node.
       lowercase__:   Tuple  			=   {start: 0, target: -1}
       while queue:
              lowercase__:   Dict  			=   queue.pop(0     )
              if node == target:
                     lowercase__:   str  			=   (
                         dist[node] if dist[target] == -1 else min(dist[target]			,							dist[node]     )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(snake_case     )
                            queue.append(snake_case     )
                            lowercase__:   List[str]  			=   dist[node] + 1
       return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4


