


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       UpperCAmelCase__						(UpperCamelCase_=None							,UpperCamelCase_=None    ):



     """simple docstring"""






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

@dataclass
class   A__ :







    """simple docstring"""

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






def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     try:
          int(UpperCamelCase_    )
          return True
     except ValueError:
          return False
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






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

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    args
         snake_case					=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

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




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

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

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

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

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

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

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

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

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






def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    HfArgumentParser(UpperCamelCase_    )
     snake_case					=    parser.parse_args_into_dataclasses()[0]
     snake_case					=    Plot(args=UpperCamelCase_    )
     plot.plot()


if __name__ == "__main__":
 main()








from __future__ import annotations

import time

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   list[tuple[int, int]]

_SCREAMING_SNAKE_CASE      :			Any      				=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_SCREAMING_SNAKE_CASE      :			Any      				=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    pos_x
         snake_case					=    pos_y
         snake_case					=    (pos_y, pos_x)
         snake_case					=    goal_x
         snake_case					=    goal_y
         snake_case					=    parent






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    Node(start[1]      , start[0]      , goal[1]      , goal[0]      , __snake_case   )
         snake_case					=    Node(goal[1]      , goal[0]      , goal[1]      , goal[0]      , __snake_case   )

         snake_case					=    [self.start]
         snake_case					=    False
    def 		a_      (	self   ):
         while self.node_queue:
              snake_case					=    self.node_queue.pop(0   )

              if current_node.pos == self.target.pos:
                   snake_case					=    True
                   return self.retrace_path(__snake_case   )

              snake_case					=    self.get_successors(__snake_case   )

              for node in successors:
                   self.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.start.pos]
         return None
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         for action in delta:
              snake_case					=    parent.pos_x + action[1]
              snake_case					=    parent.pos_y + action[0]
              if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__snake_case   ) - 1):
                   continue

              if grid[pos_y][pos_x] != 0:
                   continue

              successors.append(
                  Node(__snake_case      , __snake_case      , self.target.pos_y      , self.target.pos_x      , __snake_case   )   )
         return successors




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    node
         snake_case					=    []
         while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x)   )
              snake_case					=    current_node.parent
         path.reverse()
         return path






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    False
    def 		a_      (	self   ):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
              snake_case					=    self.fwd_bfs.node_queue.pop(0   )
              snake_case					=    self.bwd_bfs.node_queue.pop(0   )

              if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case					=    True
                   return self.retrace_bidirectional_path(
                       __snake_case      , __snake_case   )

              snake_case					=    current_bwd_node
              snake_case					=    current_fwd_node

              snake_case					=    {
                  self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case   ),
                  self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case   ),
              }

              for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                        bfs.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.fwd_bfs.start.pos]
         return None




    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    self.fwd_bfs.retrace_path(__snake_case   )
         snake_case					=    self.bwd_bfs.retrace_path(__snake_case   )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case					=    fwd_path + bwd_path
         return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   (0, 0)
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   BreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   bfs.search()
 _SCREAMING_SNAKE_CASE      :			int      				=   time.time() - start_bfs_time

 print("Unidirectional BFS computation time : ", bfs_time)

 _SCREAMING_SNAKE_CASE      :			Any      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   BidirectionalBreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   bd_bfs.search()
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   time.time() - start_bd_bfs_time

 print("Bidirectional BFS computation time : ", bd_bfs_time)




