




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




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







def       UpperCAmelCase__  (     lowerCamelCase   ):
  try:
    int(lowerCamelCase   )
    return True
  except ValueError:
    return False



def       UpperCAmelCase__  (     lowerCamelCase   ):
  try:
    float(lowerCamelCase   )
    return True
  except ValueError:
    return False




class    __lowerCAmelCase       :






       def __init__(   self:					Any       ,					_lowerCAmelCase:					List[Any]				):
         lowercase    :List[str]      	=						args
         lowercase    :Tuple      	=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

         with open(self.args.csv_file       ,					newline=""				) as csv_file:
           lowercase    :Optional[Any]      	=						csv.DictReader(_lowerCAmelCase				)
           for row in reader:
             lowercase    :Optional[int]      	=						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
               lowercase    :List[str]      	=						int(row["result"]				)
             elif can_convert_to_float(row["result"]				):
               # value is not None
               lowercase    :Optional[Any]      	=						float(row["result"]				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase	,    lowercase    :List[Any]      	=						plt.subplots()
         lowercase    :List[Any]      	=						"Time usage" if self.args.is_time else "Memory usage"
         lowercase    :List[Any]      	=						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()				):
           lowercase    :Dict      	=						sorted(set(self.result_dict[model_name]["bsz"]				)				)
           lowercase    :Optional[Any]      	=						sorted(set(self.result_dict[model_name]["seq_len"]				)				)
           lowercase    :List[Any]      	=						self.result_dict[model_name]["result"]

           ((lowercase)	,    (lowercase))    :Any      	=						(
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           lowercase    :List[str]      	=						(
               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:
               lowercase    :Optional[Any]      	=						np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,					dtype=_lowerCAmelCase       ,					)
             else:
               lowercase    :List[str]      	=						np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,					dtype=np.floataa       ,					)

             ((lowercase)	,    (lowercase))    :Any      	=						(
                 ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
             )

             lowercase    :Dict      	=						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."

         lowercase    :List[Any]      	=						title_str[:-4]
         lowercase    :Dict      	=						"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__  (     ):
  lowercase    :Optional[int]      	=						HfArgumentParser(lowerCamelCase   )
  lowercase    :List[str]      	=						parser.parse_args_into_dataclasses()[0]
  lowercase    :Any      	=						Plot(args=lowerCamelCase   )
  plot.plot()


if __name__ == "__main__":
     main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase	:						Optional[int]			=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						int			=     [
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Optional[Any]			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



