





'''simple docstring'''






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :       Tuple              ={
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Union[str, Any]              =[
          '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''InformerForPrediction''',
          '''InformerModel''',
          '''InformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

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













'''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__				(		lowerCAmelCase=None							,     lowerCAmelCase=None  ):





      """simple docstring"""


      return field(default_factory=lambda: default							,     metadata=lowerCAmelCase  )




@dataclass
class 			UpperCAmelCase					:
      _lowercase:      str											=       field(
          metadata={'''help''': '''The csv file to plot.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }							,		)
      _lowercase:      Optional[str]											=       field(
          default=snake_case_							,		metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,		)
      _lowercase:      Optional[List[str]]											=       list_field(
          default=snake_case_							,		metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}       )




def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      try:
            int(lowerCAmelCase  )
            return True
      except ValueError:
            return False
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      try:
            float(lowerCAmelCase  )
            return True
      except ValueError:
            return False




class 			UpperCAmelCase					:




      def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							args
            _lowerCAmelCase												=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

            with open(self.args.csv_file      ,      newline=""""""  ) as csv_file:
                  _lowerCAmelCase												=							csv.DictReader(__snake_case  )
                  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      :			Dict  )    ->				str:
            _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=__snake_case      ,      )
                        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(__snake_case      ,      __snake_case  )[: len(__snake_case  )]
                        plt.scatter(
                            __snake_case      ,      __snake_case      ,      label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"  )
                        plt.plot(__snake_case      ,      __snake_case      ,      """--"""  )

                  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(__snake_case  )
            plt.xlabel(__snake_case  )
            plt.ylabel(__snake_case  )
            plt.legend()

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




def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							HfArgumentParser(lowerCAmelCase  )
      _lowerCAmelCase												=							parser.parse_args_into_dataclasses()[0]
      _lowerCAmelCase												=							Plot(args=lowerCAmelCase  )
      plot.plot()


if __name__ == "__main__":
      main()






