





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     a_       (					lowerCAmelCase_ :     str=None,      lowerCAmelCase_ :     Any=None      ):
   return field(default_factory=lambda: default,      metadata=lowerCAmelCase_      )



@dataclass
class 							_UpperCAmelCase       :

  """simple docstring"""


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





def     a_       (					lowerCAmelCase_ :     Tuple      ):
   try:
      int(lowerCAmelCase_      )
      return True
   except ValueError:
      return False

def     a_       (					lowerCAmelCase_ :     Dict      ):
   try:
      float(lowerCAmelCase_      )
      return True
   except ValueError:
      return False



class 							_UpperCAmelCase       :

  """simple docstring"""


  def __init__(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[Any]	)				->		Union[str, Any]:
     __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					:						List[Any]	)				->		Tuple:
     __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     a_       (					):
   __lowerCAmelCase				      =      HfArgumentParser(lowerCAmelCase_      )
   __lowerCAmelCase				      =      parser.parse_args_into_dataclasses()[0]
   __lowerCAmelCase				      =      Plot(args=lowerCAmelCase_      )
   plot.plot()


if __name__ == "__main__":
     main()



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    List[str]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




