

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 	_snake_case		(    SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None				)       ->      List[Any]:

     """simple docstring"""







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





@dataclass
class A__    :



 """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     try:
          int(SCREAMING_SNAKE_CASE				)
          return True
     except ValueError:
          return False







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







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





class A__    :



 """simple docstring"""







 def __init__(		self :	int     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    args
      _lowerCAmelCase   :					Tuple        =    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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


 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




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

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

           _lowerCAmelCase   :					List[Any]        =    (
               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   :					Tuple        =    np.asarray(
                         [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,						dtype=A_     ,						)
                else:
                     _lowerCAmelCase   :					Optional[Any]        =    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))   :					List[str]        =    (
                    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                )

                _lowerCAmelCase   :					List[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.'''

      _lowerCAmelCase   :					Dict        =    title_str[:-4]
      _lowerCAmelCase   :					Union[str, 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 	_snake_case		(    )       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    HfArgumentParser(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    parser.parse_args_into_dataclasses()[0]
     _lowerCAmelCase   :					str        =    Plot(args=SCREAMING_SNAKE_CASE				)
     plot.plot()


if __name__ == "__main__":
  main()









from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


__UpperCAmelCase    				=     logging.get_logger(__name__)





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						[
     '''no_inference''',
     '''no_cuda''',
     '''no_tpu''',
     '''no_speed''',
     '''no_memory''',
     '''no_env_print''',
     '''no_multi_process''',
 ]

 def __init__(		self :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                _lowerCAmelCase   :					str        =    deprecated_arg[3:]
                _lowerCAmelCase   :					List[Any]        =    not kwargs.pop(A_       )
                logger.warning(
                    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                    F''' {positive_arg}={kwargs[positive_arg]}'''       )
      _lowerCAmelCase   :					Any        =    kwargs.pop("tpu_name"     ,						self.tpu_name       )
      _lowerCAmelCase   :					str        =    kwargs.pop("device_idx"     ,						self.device_idx       )
      _lowerCAmelCase   :					str        =    kwargs.pop("eager_mode"     ,						self.eager_mode       )
      _lowerCAmelCase   :					Tuple        =    kwargs.pop("use_xla"     ,						self.use_xla       )
      super().__init__(**A_       )

 _lowercase     :					str      =						field(
     default=A  ,		metadata={'''help''': '''Name of TPU'''}  ,		)
 _lowercase     :					int      =						field(
     default=0  ,		metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}  ,		)
 _lowercase     :					bool      =						field(default=A  ,		metadata={'''help''': '''Benchmark models in eager model.'''} )
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={
         '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
     }  ,		)

 @cached_property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      _lowerCAmelCase   :					Any        =    None
      if self.tpu:
           try:
                if self.tpu_name:
                     _lowerCAmelCase   :					Union[str, Any]        =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
                else:
                     _lowerCAmelCase   :					List[str]        =    tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
                _lowerCAmelCase   :					Optional[int]        =    None
      return tpu

 @cached_property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu       )
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

           _lowerCAmelCase   :					List[Any]        =    tf.distribute.TPUStrategy(self._setup_tpu       )
      else:
           # currently no multi gpu is allowed
           if self.is_gpu:
                # TODO: Currently only single GPU is supported
                tf.config.set_visible_devices(self.gpu_list[self.device_idx]     ,						"GPU"       )
                _lowerCAmelCase   :					Dict        =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
           else:
                tf.config.set_visible_devices([]     ,						"GPU"       )  # disable GPU
                _lowerCAmelCase   :					Optional[Any]        =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

      return strategy

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_tpu is not None

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_strategy

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return tf.config.list_physical_devices("GPU"       )

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.cuda:
           return len(self.gpu_list       )
      return 0


 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.n_gpu > 0






