






'''simple docstring'''

import numpy as np
import requests






def 						_UpperCAmelCase     (	)      ->      str:





						"""simple docstring"""
						lowercase_   : Dict   	=      requests.get(
						    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
						    'master/Week1/ADRvsRating.csv'				)
						lowercase_   : int   	=      response.text.splitlines()
						lowercase_   : Dict   	=      []
						for item in lines:
												lowercase_   : Tuple   	=      item.split(','				)
												data.append(UpperCAmelCase_				)
						data.pop(0				)  # This is for removing the labels from the list
						lowercase_   : str   	=      np.matrix(UpperCAmelCase_				)
						return dataset






def 						_UpperCAmelCase     (	a		:						Union[str, Any]    ,      a		:						Any    ,      a		:						Union[str, Any]    ,      a		:						List[Any]    ,      a		:						Dict				)      ->      List[str]:





						"""simple docstring"""
						lowercase_   : Union[str, Any]   	=      len_data

						lowercase_   : Union[str, Any]   	=      np.dot(UpperCAmelCase_    ,      data_x.transpose()				)
						prod -= data_y.transpose()
						lowercase_   : Dict   	=      np.dot(UpperCAmelCase_    ,      UpperCAmelCase_				)
						lowercase_   : int   	=      theta - (alpha / n) * sum_grad
						return theta






def 						_UpperCAmelCase     (	a		:						Optional[Any]    ,      a		:						Dict    ,      a		:						Dict    ,      a		:						int				)      ->      str:





						"""simple docstring"""
						lowercase_   : List[Any]   	=      np.dot(UpperCAmelCase_    ,      data_x.transpose()				)
						prod -= data_y.transpose()
						lowercase_   : Any   	=      np.sum(np.square(UpperCAmelCase_				)				)
						lowercase_   : List[str]   	=      sum_elem / (2 * len_data)
						return error






def 						_UpperCAmelCase     (	a		:						Optional[Any]    ,      a		:						str				)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : Any   	=      1_0_0_0_0_0
						lowercase_   : str   	=      0.0_00_15_50

						lowercase_   : Tuple   	=      data_x.shape[1]
						lowercase_   : Tuple   	=      data_x.shape[0] - 1

						lowercase_   : Dict   	=      np.zeros((1, no_features)				)

						for i in range(0    ,      UpperCAmelCase_				):
												lowercase_   : Tuple   	=      run_steep_gradient_descent(UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_				)
												lowercase_   : Dict   	=      sum_of_square_error(UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_				)
												print(f"At Iteration {i + 1} - Error is {error:.5f}"				)

						return theta






def 						_UpperCAmelCase     (	a		:						Union[str, Any]    ,      a		:						Any				)      ->      str:





						"""simple docstring"""
						lowercase_   : Tuple   	=      sum(abs(y - predicted_y[i]				) for i, y in enumerate(UpperCAmelCase_				)				)
						return total / len(UpperCAmelCase_				)






def 						_UpperCAmelCase     (	)      ->      Optional[int]:





						"""simple docstring"""
						lowercase_   : Dict   	=      collect_dataset()

						lowercase_   : str   	=      data.shape[0]
						lowercase_   : str   	=      np.c_[np.ones(UpperCAmelCase_				), data[:, :-1]].astype(UpperCAmelCase_				)
						lowercase_   : Dict   	=      data[:, -1].astype(UpperCAmelCase_				)

						lowercase_   : int   	=      run_linear_regression(UpperCAmelCase_    ,      UpperCAmelCase_				)
						lowercase_   : str   	=      theta.shape[1]
						print('Resultant Feature vector : '				)
						for i in range(0    ,      UpperCAmelCase_				):
												print(f"{theta[0, i]:.5f}"				)


if __name__ == "__main__":
						main()






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 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]=None   ,       UpperCAmelCase_	:      Union[str, Any]=None      )->			Tuple:

       """simple docstring"""




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


@dataclass
class        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					str     					=    field(
         metadata={"help": "The csv file to plot."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Disable logarithmic scale when plotting"}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={
             "help": "Whether the csv file has training results or inference results. Defaults to inference results."
         }      ,		)
     _SCREAMING_SNAKE_CASE      :					Optional[str]     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,		)
     _SCREAMING_SNAKE_CASE      :					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 							lowerCamelCase    (							UpperCAmelCase_	:      Dict      )->			int:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]      )->			str:

       """simple docstring"""




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


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase	):
            a			   =args
            a			   =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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






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

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

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

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

                          a			   =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.'''

            a			   =title_str[:-4]
            a			   ="""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 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




       a			   =HfArgumentParser(UpperCAmelCase_      )
       a			   =parser.parse_args_into_dataclasses()[0]
       a			   =Plot(args=UpperCAmelCase_      )
       plot.plot()


if __name__ == "__main__":
    main()
