
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


lowercase_ 				=			logging.get_logger(__name__)





@dataclass
class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			[
         '''no_inference''',
         '''no_cuda''',
         '''no_tpu''',
         '''no_speed''',
         '''no_memory''',
         '''no_env_print''',
         '''no_multi_process''',
     ]



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







       """simple docstring"""




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

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



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







       """simple docstring"""




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



     @cached_property
     def  UpperCAmelCase__							(				self							:							str ):







       """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 )

         __SCREAMING_SNAKE_CASE      :	List[str]					=		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''' )
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}''' )
         else:
           tf.config.set_visible_devices([]     ,						'''GPU''' )  # disable GPU
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}''' )

       return strategy



     @property
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




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



     @property
     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




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



     @property
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




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



     @property
     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




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




     @property
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       return self.n_gpu > 0







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    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



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




def   __snake_case    (       _lowerCAmelCase			:			int						)			->    Optional[Any]:
   try:
      int(_lowerCAmelCase						)
      return True
   except ValueError:
      return False

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


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Any      ,   snake_case							:List[Any]	):





        '''simple docstring'''




        A_       :					Any        =      args
        A_       :					Dict        =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




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

           ((A_)   ,    (A_))       :					Optional[Any]        =      (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           A_       :					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:
                 A_       :					Dict        =      np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,   dtype=snake_case      ,   )
              else:
                 A_       :					Union[str, Any]        =      np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,   dtype=np.floataa      ,   )

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

              A_       :					List[Any]        =      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."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					List[str]        =      "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   __snake_case    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
