



"""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 				_lowerCAmelCase   (     lowercase_=None	,			lowercase_=None		):
      return field(default_factory=lambda: default	,			metadata=lowerCAmelCase__		)







@dataclass
class    A_    :



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


def 				_lowerCAmelCase   (     lowercase_		):
      try:
            int(lowerCAmelCase__		)
            return True
      except ValueError:
            return False

def 				_lowerCAmelCase   (     lowercase_		):
      try:
            float(lowerCAmelCase__		)
            return True
      except ValueError:
            return False







class    A_    :



      """simple docstring"""






      def __init__(      self				:Optional[int]     ,						lowercase_				:str      )       ->    Tuple:
            UpperCAmelCase    =				args
            UpperCAmelCase    =				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

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






      def 			UpperCAmelCase__  (      self				:Optional[int]      )       ->    Tuple:
            UpperCAmelCase    =				plt.subplots()
            UpperCAmelCase    =				'Time usage' if self.args.is_time else 'Memory usage'
            UpperCAmelCase    =				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()      ):
                  UpperCAmelCase    =				sorted(set(self.result_dict[model_name]['bsz']      )      )
                  UpperCAmelCase    =				sorted(set(self.result_dict[model_name]['seq_len']      )      )
                  UpperCAmelCase    =				self.result_dict[model_name]['result']

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

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

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

                        UpperCAmelCase    =				np.asarray(lowercase_     ,						lowercase_      )[: len(lowercase_      )]
                        plt.scatter(
                            lowercase_     ,						lowercase_     ,						label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""      )
                        plt.plot(lowercase_     ,						lowercase_     ,						'--'      )

                  title_str += f""" {label_model_name} vs."""

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

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

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


def 				_lowerCAmelCase   (     ):
      UpperCAmelCase    =				HfArgumentParser(lowerCAmelCase__		)
      UpperCAmelCase    =				parser.parse_args_into_dataclasses()[0]
      UpperCAmelCase    =				Plot(args=lowerCAmelCase__		)
      plot.plot()


if __name__ == "__main__":
    main()










"""simple docstring"""


import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class    A_    (  nn.Linear       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
												UpperCAmelCase    =				pruning_method

												if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		UpperCAmelCase    =				mask_scale
																		UpperCAmelCase    =				mask_init
																		UpperCAmelCase    =				nn.Parameter(torch.empty(self.weight.size()      )      )
																		self.init_mask()






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												if self.mask_init == "constant":
																		init.constant_(self.mask_scores     ,						val=self.mask_scale      )
												elif self.mask_init == "uniform":
																		init.uniform_(self.mask_scores     ,						a=-self.mask_scale     ,						b=self.mask_scale      )
												elif self.mask_init == "kaiming":
																		init.kaiming_uniform_(self.mask_scores     ,						a=math.sqrt(5      )      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												elif self.pruning_method == "l0":
																		UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				-0.1, 1.1, 2 / 3
																		if self.training:
																								UpperCAmelCase    =				torch.zeros_like(self.mask_scores      ).uniform_().clamp(0.0001     ,						0.9999      )
																								UpperCAmelCase    =				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b      )
																		else:
																								UpperCAmelCase    =				torch.sigmoid(self.mask_scores      )
																		UpperCAmelCase    =				s * (r - l) + l
																		UpperCAmelCase    =				s_bar.clamp(min=0.0     ,						max=1.0      )
												# Mask weights with computed mask
												UpperCAmelCase    =				mask * self.weight
												# Compute output (linear layer) with masked weights
												return nn.functional.linear(lowercase_     ,						lowercase_     ,						self.bias      )





