

"""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  (  UpperCamelCase_					:	Union[str, Any]=None,      UpperCamelCase_					:	int=None)     ->						Tuple:
   '''simple docstring'''


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




@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase     : str       		=     field(
     metadata={"help": "The csv file to plot."}					,)
 __UpperCAmelCase     : bool       		=     field(
     default=lowercase					,metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}					,)
 __UpperCAmelCase     : bool       		=     field(
     default=lowercase					,metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}					,)
 __UpperCAmelCase     : bool       		=     field(
     default=lowercase					,metadata={"help": "Disable logarithmic scale when plotting"}					,)
 __UpperCAmelCase     : bool       		=     field(
     default=lowercase					,metadata={
         "help": "Whether the csv file has training results or inference results. Defaults to inference results."
     }					,)
 __UpperCAmelCase     : Optional[str]       		=     field(
     default=lowercase					,metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}					,)
 __UpperCAmelCase     : 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  (  UpperCamelCase_					:	Any)     ->						Dict:
   '''simple docstring'''


   try:
      int(UpperCamelCase_)
      return True
   except ValueError:
      return False
def   _A  (  UpperCamelCase_					:	Optional[int])     ->						Optional[Any]:
   '''simple docstring'''


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




class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						args
    __lowercase								=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

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



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

       ((__lowercase)		,(__lowercase))								=						(
           (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
       )

       __lowercase								=						(
           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:
             __lowercase								=						np.asarray(
                 [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results], dtype=UpperCAmelCase__, )
          else:
             __lowercase								=						np.asarray(
                 [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results], dtype=np.floataa, )

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

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

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

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

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

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







def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						HfArgumentParser(UpperCamelCase_)
   __lowercase								=						parser.parse_args_into_dataclasses()[0]
   __lowercase								=						Plot(args=UpperCamelCase_)
   plot.plot()


if __name__ == "__main__":
   main()







"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_a							=							5_00_00
_a							=							50_00

_a				,						_a							=							os.path.split(__file__)
_a							=							os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[str])     ->						List[str]:
   '''simple docstring'''


   for i in range(UpperCamelCase_):
      __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	int)     ->						Dict:
   '''simple docstring'''


   for i in range(0,      len(UpperCamelCase_),      UpperCamelCase_):
      __lowercase								=						dataset[i : i + batch_size]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int])     ->						List[str]:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(UpperCamelCase_):
         __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(0,      UpperCamelCase_,      UpperCamelCase_):
         __lowercase								=						dataset[i : i + batch_size]




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						{"num examples": SPEED_TEST_N_EXAMPLES}
   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
       (read_formatted, {"type": "torch", "length": SMALL_TEST}),
       (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]

   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("generating dataset")
      __lowercase								=						datasets.Features(
          {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
      __lowercase								=						generate_example_dataset(
          os.path.join(UpperCamelCase_,      "dataset.arrow"),      UpperCamelCase_,      num_examples=UpperCamelCase_,      seq_shapes={"list": (100,)},      )
      print("first set of iterations")
      for func, kwargs in functions:
         print(func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(UpperCamelCase_,      **UpperCamelCase_)

      print("shuffling dataset")
      __lowercase								=						dataset.shuffle()
      print("Second set of iterations (after shuffling")
      for func, kwargs in functions_shuffled:
         print("shuffled ",      func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(
             UpperCamelCase_,      **UpperCamelCase_)

   with open(UpperCamelCase_,      "wb") as f:
      f.write(json.dumps(UpperCamelCase_).encode("utf-8"))


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()




