


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			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


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



def  _a			(						__UpperCamelCase ):
     try:
          int(__UpperCamelCase )
          return True
     except ValueError:
          return False



def  _a			(						__UpperCamelCase ):
     try:
          float(__UpperCamelCase )
          return True
     except ValueError:
          return False


class   a__	:

       def __init__(   self      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     Union[str, Any]										=    args
            a_    :     Optional[int]										=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

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

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

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

                 a_    :     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:
                           a_    :     Optional[int]										=    np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=lowerCamelCase_      ,							)
                      else:
                           a_    :     int										=    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_))    :     List[str]										=    (
                          ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                      )

                      a_    :     List[Any]										=    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.'''

            a_    :     int										=    title_str[:-4]
            a_    :     Tuple										=    """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  _a			(						):
     a_    :     Tuple										=    HfArgumentParser(__UpperCamelCase )
     a_    :     Dict										=    parser.parse_args_into_dataclasses()[0]
     a_    :     Dict										=    Plot(args=__UpperCamelCase )
     plot.plot()


if __name__ == "__main__":
       main()







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowerCamelCase        =     logging.get_logger(__name__)



def  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


@dataclass
class   a__	:
       lowerCamelCase__:     List[str]							    =     list_field(
           default=[]						,       metadata={
               """help""": (
                   """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                   """ of all available models"""
               )
           }						,       )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8]						,       metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}    )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8, 32, 128, 512]						,       metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}						,       )

       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Use FP16 to accelerate inference."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Benchmark training of model"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Verbose memory tracing"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
           }						,       )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Trace memory line by line"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save result to a CSV file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save all print statements in a log file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Whether to print environment information"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                   """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                   """ for debugging / testing and on TPU."""
               )
           }						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""env_info_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving environment information."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""log_{round(time()    )}.csv"""						,       metadata={"""help""": """Log filename used if print statements are saved in log."""}						,       )
       lowerCamelCase__:     int							    =     field(default=3						,       metadata={"""help""": """Times an experiment will be run."""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                   """ model weights."""
               )
           }						,       )

       def 						UpperCAmelCase(   self      :     Any    ):
            warnings.warn(
                F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
                """ are deprecated in general and it is advised to use external Benchmarking libraries """
                """ to benchmark Transformer models."""      ,							lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return json.dumps(dataclasses.asdict(self    )      ,							indent=2    )

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            if len(self.models    ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""    )
            return self.models

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            if not self.multi_process:
                 return False
            elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""    )
                 return False
            else:
                 return True



