



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        lowercase_							(A : Tuple=None		,					A : Dict=None      ):
       return field(default_factory=lambda: default		,					metadata=lowercase__      )




@dataclass
class 					snake_case__						:






     """simple docstring"""


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


def        lowercase_							(A : str      ):
       try:
              int(lowercase__      )
              return True
       except ValueError:
              return False
def        lowercase_							(A : List[Any]      ):
       try:
              float(lowercase__      )
              return True
       except ValueError:
              return False




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  List[Any], _snake_case  :  str					)   ->int:
            snake_case__							:	Any   	=	args
            snake_case__							:	Tuple   	=	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

            with open(self.args.csv_file, newline=''					) as csv_file:
                   snake_case__							:	Optional[Any]   	=	csv.DictReader(_snake_case					)
                   for row in reader:
                          snake_case__							:	Optional[Any]   	=	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__							:	List[str]   	=	int(row['result']					)
                          elif can_convert_to_float(row['result']					):
                                 # value is not None
                                 snake_case__							:	List[Any]   	=	float(row['result']					)






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

                   (snake_case__)							:	Optional[int]   	=	(
                       (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                   )

                   snake_case__							:	Union[str, Any]   	=	(
                       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__							:	Optional[Any]   	=	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__							:	Optional[int]   	=	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__)							:	int   	=	(
                              ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                          )

                          snake_case__							:	List[Any]   	=	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__							:	Dict   	=	title_str[:-4]
            snake_case__							:	List[Any]   	=	'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        lowercase_							():
       snake_case__							:	Any   	=	HfArgumentParser(lowercase__      )
       snake_case__							:	List[Any]   	=	parser.parse_args_into_dataclasses()[0]
       snake_case__							:	Dict   	=	Plot(args=lowercase__      )
       plot.plot()


if __name__ == "__main__":
 main()












"""simple docstring"""






def      _snake_case			(				lowercase__							,    lowercase__		):
				# "extended trapezoidal rule"
				# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
				_lowerCamelCase     :			List[Any]    =    (boundary[1] - boundary[0]) / steps
				_lowerCamelCase     :			Tuple    =    boundary[0]
				_lowerCamelCase     :			Dict    =    boundary[1]
				_lowerCamelCase     :			List[Any]    =    make_points(lowercase__							,    lowercase__							,    lowercase__		)
				_lowerCamelCase     :			List[Any]    =    0.0
				y += (h / 2.0) * f(lowercase__		)
				for i in x_i:
								# print(i)
								y += h * f(lowercase__		)
				y += (h / 2.0) * f(lowercase__		)
				return y







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			str    =    a + h
				while x < (b - h):
								yield x
								_lowerCamelCase     :			int    =    x + h







def      _snake_case			(				lowercase__		):  # enter your function here
				_lowerCamelCase     :			Optional[Any]    =    (x - 0) * (x - 0)
				return y







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    0.0  # Lower bound of integration
				_lowerCamelCase     :			Optional[int]    =    1.0  # Upper bound of integration
				_lowerCamelCase     :			List[str]    =    1_0.0  # define number of steps or resolution
				_lowerCamelCase     :			List[Any]    =    [a, b]  # define boundary of integration
				_lowerCamelCase     :			Optional[Any]    =    method_a(lowercase__							,    lowercase__		)
				print(f'''y = {y}'''		)


if __name__ == "__main__":
			main()