





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__							(   SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None)       ->   int:
  return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE__)







@dataclass
class    __snake_case	:





       '''simple docstring'''







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







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   str:
  try:
    int(SCREAMING_SNAKE_CASE__)
    return True
  except ValueError:
    return False







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Union[str, Any]:
  try:
    float(SCREAMING_SNAKE_CASE__)
    return True
  except ValueError:
    return False







class    __snake_case	:





       '''simple docstring'''







       def __init__(  self			:   Tuple							,    A			:   Union[str, Any]		):
         __snake_case:						Any			  =						args
         __snake_case:						Any			  =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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






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

           ((__snake_case)  ,       (__snake_case)):						str			  =						(
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           __snake_case:						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:
               __snake_case:						Optional[Any]			  =						np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,    dtype=A							,    )
             else:
               __snake_case:						int			  =						np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,    dtype=np.floataa							,    )

             ((__snake_case)  ,       (__snake_case)):						Optional[int]			  =						(
                 ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
             )

             __snake_case:						Union[str, Any]			  =						np.asarray(A							,    A		)[: len(A		)]
             plt.scatter(
                 A							,    A							,    label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''		)
             plt.plot(A							,    A							,    """--"""		)

           title_str += f''' {label_model_name} vs.'''

         __snake_case:						Any			  =						title_str[:-4]
         __snake_case:						List[Any]			  =						"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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







def       A__							(   )       ->   Dict:
  __snake_case:						Union[str, Any]			  =						HfArgumentParser(SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]			  =						parser.parse_args_into_dataclasses()[0]
  __snake_case:						List[Any]			  =						Plot(args=SCREAMING_SNAKE_CASE__)
  plot.plot()


if __name__ == "__main__":
      main()













from typing import TYPE_CHECKING

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


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






