




"""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 							lowercase   (snake_case__				:	List[Any]=None ,    snake_case__				:	Dict=None	)       ->					Tuple:


				'''simple docstring'''






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






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





def 							lowercase   (snake_case__				:	str	)       ->					str:


				'''simple docstring'''






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





def 							lowercase   (snake_case__				:	Optional[int]	)       ->					Any:


				'''simple docstring'''






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






class  SCREAMING_SNAKE_CASE__   :


						def __init__(							self			:     List[Any]  ,  lowerCAmelCase			:     str   ):
										lowerCAmelCase		=			args
										lowerCAmelCase		=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

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




						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase	,  lowerCAmelCase		=			plt.subplots()
										lowerCAmelCase		=			"""Time usage""" if self.args.is_time else """Memory usage"""
										lowerCAmelCase		=			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()   ):
														lowerCAmelCase		=			sorted(set(self.result_dict[model_name]["""bsz"""]   )   )
														lowerCAmelCase		=			sorted(set(self.result_dict[model_name]["""seq_len"""]   )   )
														lowerCAmelCase		=			self.result_dict[model_name]["""result"""]

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

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

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

																		lowerCAmelCase		=			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.'''

										lowerCAmelCase		=			title_str[:-4]
										lowerCAmelCase		=			"""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 							lowercase   ()       ->					Union[str, Any]:


				'''simple docstring'''






				lowerCAmelCase		=			HfArgumentParser(snake_case__	)
				lowerCAmelCase		=			parser.parse_args_into_dataclasses()[0]
				lowerCAmelCase		=			Plot(args=snake_case__	)
				plot.plot()


if __name__ == "__main__":
		main()











"""simple docstring"""







import numpy as np
import requests





def 							lowercase   ()       ->					Optional[int]:


				'''simple docstring'''






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





def 							lowercase   (snake_case__				:	List[str] ,    snake_case__				:	int ,    snake_case__				:	List[str] ,    snake_case__				:	Tuple ,    snake_case__				:	Union[str, Any]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			len_data

				lowerCAmelCase		=			np.dot(snake_case__ ,    data_x.transpose()	)
				prod -= data_y.transpose()
				lowerCAmelCase		=			np.dot(snake_case__ ,    snake_case__	)
				lowerCAmelCase		=			theta - (alpha / n) * sum_grad
				return theta





def 							lowercase   (snake_case__				:	Any ,    snake_case__				:	int ,    snake_case__				:	Any ,    snake_case__				:	Dict	)       ->					List[str]:


				'''simple docstring'''






				lowerCAmelCase		=			np.dot(snake_case__ ,    data_x.transpose()	)
				prod -= data_y.transpose()
				lowerCAmelCase		=			np.sum(np.square(snake_case__	)	)
				lowerCAmelCase		=			sum_elem / (2 * len_data)
				return error





def 							lowercase   (snake_case__				:	Optional[Any] ,    snake_case__				:	List[Any]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			100_000
				lowerCAmelCase		=			0.0_001_550

				lowerCAmelCase		=			data_x.shape[1]
				lowerCAmelCase		=			data_x.shape[0] - 1

				lowerCAmelCase		=			np.zeros((1, no_features)	)

				for i in range(0 ,    snake_case__	):
								lowerCAmelCase		=			run_steep_gradient_descent(snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__	)
								lowerCAmelCase		=			sum_of_square_error(snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__	)
								print(f'''At Iteration {i + 1} - Error is {error:.5f}'''	)

				return theta





def 							lowercase   (snake_case__				:	List[str] ,    snake_case__				:	Optional[int]	)       ->					List[str]:


				'''simple docstring'''






				lowerCAmelCase		=			sum(abs(y - predicted_y[i]	) for i, y in enumerate(snake_case__	)	)
				return total / len(snake_case__	)





def 							lowercase   ()       ->					Optional[int]:


				'''simple docstring'''






				lowerCAmelCase		=			collect_dataset()

				lowerCAmelCase		=			data.shape[0]
				lowerCAmelCase		=			np.c_[np.ones(snake_case__	), data[:, :-1]].astype(snake_case__	)
				lowerCAmelCase		=			data[:, -1].astype(snake_case__	)

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


if __name__ == "__main__":
		main()





