'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : int=None      ,		__snake_case       : List[Any]=None							):
     return field(default_factory=lambda: default      ,		metadata=__snake_case							)







@dataclass
class lowercase_					:







   """simple docstring"""

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






def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple							):
     try:
          int(__snake_case							)
          return True
     except ValueError:
          return False





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     try:
          float(__snake_case							)
          return True
     except ValueError:
          return False







class lowercase_					:







   """simple docstring"""

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

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

   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> List[str]:
        _A		,					_A								=     plt.subplots()
        _A								=     'Time usage' if self.args.is_time else 'Memory usage'
        _A								=     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()    ):
             _A								=     sorted(set(self.result_dict[model_name]['bsz']    )    )
             _A								=     sorted(set(self.result_dict[model_name]['seq_len']    )    )
             _A								=     self.result_dict[model_name]['result']

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

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

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

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

             title_str += f' {label_model_name} vs.'

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

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

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






def    _SCREAMING_SNAKE_CASE		(			):
     _A								=     HfArgumentParser(__snake_case							)
     _A								=     parser.parse_args_into_dataclasses()[0]
     _A								=     Plot(args=__snake_case							)
     plot.plot()


if __name__ == "__main__":
   main()




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase						:    Optional[Any]     					=					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Dict     					=					[
       '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechSatForAudioFrameClassification''',
       '''UniSpeechSatForCTC''',
       '''UniSpeechSatForPreTraining''',
       '''UniSpeechSatForSequenceClassification''',
       '''UniSpeechSatForXVector''',
       '''UniSpeechSatModel''',
       '''UniSpeechSatPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

   _UpperCAmelCase						:    int     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



