



'''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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE=None ,		SCREAMING_SNAKE_CASE=None     ):
		return field(default_factory=lambda: default ,		metadata=A__     )




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







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		try:
				int(A__     )
				return True
		except ValueError:
				return False







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		try:
				float(A__     )
				return True
		except ValueError:
				return False




class 				__lowerCAmelCase:






			def __init__(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      args
					SCREAMING_SNAKE_CASE_						:List[Any]										=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      plt.subplots()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      'Time usage' if self.args.is_time else 'Memory usage'
					SCREAMING_SNAKE_CASE_						: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()	):
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      sorted(set(self.result_dict[model_name]['bsz']	)	)
							SCREAMING_SNAKE_CASE_						:Dict										=      sorted(set(self.result_dict[model_name]['seq_len']	)	)
							SCREAMING_SNAKE_CASE_						:Any										=      self.result_dict[model_name]['result']

							((SCREAMING_SNAKE_CASE_)						,		(SCREAMING_SNAKE_CASE_))						:List[str]										=      (
							    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
							)

							SCREAMING_SNAKE_CASE_						:Tuple										=      (
							    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:
											SCREAMING_SNAKE_CASE_						:List[str]										=      np.asarray(
											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,       dtype=SCREAMING_SNAKE_CASE__      ,       )
									else:
											SCREAMING_SNAKE_CASE_						:Any										=      np.asarray(
											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,       dtype=np.floataa      ,       )

									((SCREAMING_SNAKE_CASE_)						,		(SCREAMING_SNAKE_CASE_))						:List[str]										=      (
									    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
									)

									SCREAMING_SNAKE_CASE_						:Optional[Any]										=      np.asarray(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__	)[: len(SCREAMING_SNAKE_CASE__	)]
									plt.scatter(
									    SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'	)
									plt.plot(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       '--'	)

							title_str += f' {label_model_name} vs.'

					SCREAMING_SNAKE_CASE_						:Tuple										=      title_str[:-4]
					SCREAMING_SNAKE_CASE_						:List[str]										=      'Time in s' if self.args.is_time else 'Memory in MB'

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

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







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Dict										=      HfArgumentParser(A__     )
		SCREAMING_SNAKE_CASE_						:List[str]										=      parser.parse_args_into_dataclasses()[0]
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      Plot(args=A__     )
		plot.plot()


if __name__ == "__main__":
							main()



"""simple docstring"""




import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




