


'''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()





'''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`.'			)






