
"""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       __a	(						__lowerCamelCase=None,						__lowerCamelCase=None    ):
   return field(default_factory=lambda: default,						metadata=__lowerCamelCase    )


@dataclass
class A_      :




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







def       __a	(						__lowerCamelCase    ):
   try:
      int(__lowerCamelCase    )
      return True
   except ValueError:
      return False







def       __a	(						__lowerCamelCase    ):
   try:
      float(__lowerCamelCase    )
      return True
   except ValueError:
      return False


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       args
     UpperCAmelCase_   :   Optional[Any]     	=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       plt.subplots()
     UpperCAmelCase_   :   List[Any]     	=       "Time usage" if self.args.is_time else "Memory usage"
     UpperCAmelCase_   :   int     	=       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()    ):
        UpperCAmelCase_   :   Union[str, Any]     	=       sorted(set(self.result_dict[model_name]["bsz"]    )    )
        UpperCAmelCase_   :   Any     	=       sorted(set(self.result_dict[model_name]["seq_len"]    )    )
        UpperCAmelCase_   :   Optional[int]     	=       self.result_dict[model_name]["result"]

        ((UpperCAmelCase_)	,  (UpperCAmelCase_))   :   Dict     	=       (
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

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

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

           UpperCAmelCase_   :   Dict     	=       np.asarray(lowercase_							,  lowercase_    )[: len(lowercase_    )]
           plt.scatter(
               lowercase_							,  lowercase_							,  label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""    )
           plt.plot(lowercase_							,  lowercase_							,  "--"    )

        title_str += F""" {label_model_name} vs."""

     UpperCAmelCase_   :   Optional[int]     	=       title_str[:-4]
     UpperCAmelCase_   :   int     	=       "Time in s" if self.args.is_time else "Memory in MB"

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

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







def       __a	(						):
   UpperCAmelCase_   :   List[str]     	=       HfArgumentParser(__lowerCamelCase    )
   UpperCAmelCase_   :   Any     	=       parser.parse_args_into_dataclasses()[0]
   UpperCAmelCase_   :   Any     	=       Plot(args=__lowerCamelCase    )
   plot.plot()


if __name__ == "__main__":
    main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





