code: 
  
  
  '''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     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]=None   ,					UpperCAmelCase_					:       Optional[Any]=None							)  ->	Optional[Any]:
       return field(default_factory=lambda: default   ,					metadata=UpperCAmelCase_							)
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      str							  =    field(
          metadata={'help': 'The csv file to plot.'}      ,	)
      lowerCamelCase						:      bool							  =    field(
          default=lowerCAmelCase__      ,	metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}      ,	)
      lowerCamelCase						:      bool							  =    field(
          default=lowerCAmelCase__      ,	metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}      ,	)
      lowerCamelCase						:      bool							  =    field(
          default=lowerCAmelCase__      ,	metadata={'help': 'Disable logarithmic scale when plotting'}      ,	)
      lowerCamelCase						:      bool							  =    field(
          default=lowerCAmelCase__      ,	metadata={
              'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
          }      ,	)
      lowerCamelCase						:      Optional[str]							  =    field(
          default=lowerCAmelCase__      ,	metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}      ,	)
      lowerCamelCase						:      Optional[List[str]]							  =    list_field(
          default=lowerCAmelCase__      ,	metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}						)
  
  
  
  
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any							)  ->	List[Any]:
  
  
  
       try:
            int(UpperCAmelCase_							)
            return True
       except ValueError:
            return False
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[Any]							)  ->	Tuple:
  
  
  
       try:
            float(UpperCAmelCase_							)
            return True
       except ValueError:
            return False
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           __lowerCamelCase     :			int              =	args
           __lowerCamelCase     :			Tuple              =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)
  
           with open(self.args.csv_file    ,  newline=''			) as csv_file:
                __lowerCamelCase     :			str              =	csv.DictReader(lowerCAmelCase_			)
                for row in reader:
                     __lowerCamelCase     :			List[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
                          __lowerCamelCase     :			Optional[int]              =	int(row['result']			)
                     elif can_convert_to_float(row['result']			):
                          # value is not None
                          __lowerCamelCase     :			Optional[Any]              =	float(row['result']			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Any:
           __lowerCamelCase       , __lowerCamelCase     :			List[str]              =	plt.subplots()
           __lowerCamelCase     :			int              =	'Time usage' if self.args.is_time else 'Memory usage'
           __lowerCamelCase     :			List[str]              =	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     :			Any              =	sorted(set(self.result_dict[model_name]['bsz']			)			)
                __lowerCamelCase     :			Optional[int]              =	sorted(set(self.result_dict[model_name]['seq_len']			)			)
                __lowerCamelCase     :			Optional[int]              =	self.result_dict[model_name]['result']
  
                ((__lowerCamelCase)       , (__lowerCamelCase))     :			Optional[Any]              =	(
                    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                )
  
                __lowerCamelCase     :			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:
                          __lowerCamelCase     :			Union[str, Any]              =	np.asarray(
                              [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,  dtype=lowerCAmelCase_    ,  )
                     else:
                          __lowerCamelCase     :			int              =	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))     :			str              =	(
                         ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                     )
  
                     __lowerCamelCase     :			Optional[int]              =	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     :			Union[str, Any]              =	title_str[:-4]
           __lowerCamelCase     :			Any              =	'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     UpperCAmelCase__      (			)  ->	str:
       __lowerCamelCase     :			Tuple              =	HfArgumentParser(UpperCAmelCase_							)
       __lowerCamelCase     :			Any              =	parser.parse_args_into_dataclasses()[0]
       __lowerCamelCase     :			Optional[int]              =	Plot(args=UpperCAmelCase_							)
       plot.plot()
  
  
  if __name__ == "__main__":
        main()
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  A__	:      Any				=						logging.get_logger(__name__)
  
  A__	:      str				=						{
      """microsoft/prophetnet-large-uncased""": (
          """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
      ),
  }
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Dict							  =    'prophetnet'
      lowerCamelCase						:      List[str]							  =    ['past_key_values']
      lowerCamelCase						:      List[str]							  =    {
          'num_attention_heads': 'num_encoder_attention_heads',
      }
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           __lowerCamelCase     :			List[Any]              =	vocab_size
           __lowerCamelCase     :			Union[str, Any]              =	hidden_size
           __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
           __lowerCamelCase     :			Any              =	num_encoder_layers
           __lowerCamelCase     :			int              =	num_encoder_attention_heads
           __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
           __lowerCamelCase     :			str              =	num_decoder_layers
           __lowerCamelCase     :			int              =	num_decoder_attention_heads
           __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
           __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
           __lowerCamelCase     :			Union[str, Any]              =	activation_function
  
           # parameters for prophetnet
           __lowerCamelCase     :			int              =	ngram
           __lowerCamelCase     :			Optional[int]              =	num_buckets
           __lowerCamelCase     :			str              =	relative_max_distance
           __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
           __lowerCamelCase     :			str              =	eps
  
           # 3 Types of Dropout
           __lowerCamelCase     :			Optional[Any]              =	attention_dropout
           __lowerCamelCase     :			str              =	activation_dropout
           __lowerCamelCase     :			Optional[Any]              =	dropout
  
           __lowerCamelCase     :			Optional[Any]              =	use_cache
  
           super().__init__(
               pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  
  
  
      @num_hidden_layers.setter
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           raise NotImplementedError(
               'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
               ' `num_decoder_layers`.'			)
  
  
  
  
  
  
  