


"""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__      (    SCREAMING_SNAKE_CASE   :   str=None    ,				SCREAMING_SNAKE_CASE   :   str=None    ):


  '''simple docstring'''







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


@dataclass
class    SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


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


  '''simple docstring'''







  try:
    int(SCREAMING_SNAKE_CASE    )
    return True
  except ValueError:
    return False
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







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


class    SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							args
         lowerCAmelCase					:					List[str]								=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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







       def 	lowercase__							(						self						):






         """simple docstring"""





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

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

           lowerCAmelCase					:					Dict								=							(
               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					:					Optional[Any]								=							np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,					dtype=snake_case__		,					)
             else:
               lowerCAmelCase					:					List[str]								=							np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,					dtype=np.floataa		,					)

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

             lowerCAmelCase					:					int								=							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."""

         lowerCAmelCase					:					Dict								=							title_str[:-4]
         lowerCAmelCase					:					Optional[int]								=							"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  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							HfArgumentParser(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							parser.parse_args_into_dataclasses()[0]
  lowerCAmelCase					:					Dict								=							Plot(args=SCREAMING_SNAKE_CASE    )
  plot.plot()


if __name__ == "__main__":
     main()









"""simple docstring"""







import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = False		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(features=snake_case__		,					cache_dir=snake_case__		,					keep_in_memory=snake_case__		,					**snake_case__						)
         lowerCAmelCase					:					Dict								=							Sql(
             cache_dir=snake_case__		,					features=snake_case__		,					sql=snake_case__		,					con=snake_case__		,					**snake_case__		,					)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							None
         lowerCAmelCase					:					Optional[int]								=							None
         lowerCAmelCase					:					str								=							None
         lowerCAmelCase					:					int								=							None

         self.builder.download_and_prepare(
             download_config=snake_case__		,					download_mode=snake_case__		,					verification_mode=snake_case__		,					base_path=snake_case__		,					)

         # Build dataset for splits
         lowerCAmelCase					:					str								=							self.builder.as_dataset(
             split="train"		,					verification_mode=snake_case__		,					in_memory=self.keep_in_memory						)
         return dataset


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         if num_proc is not None and num_proc <= 0:
           raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""						)

         lowerCAmelCase					:					Tuple								=							dataset
         lowerCAmelCase					:					Tuple								=							name
         lowerCAmelCase					:					List[str]								=							con
         lowerCAmelCase					:					List[Any]								=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         lowerCAmelCase					:					Optional[int]								=							num_proc
         lowerCAmelCase					:					Optional[int]								=							to_sql_kwargs

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.to_sql_kwargs.pop("sql"		,					snake_case__						)
         lowerCAmelCase					:					List[Any]								=							self.to_sql_kwargs.pop("con"		,					snake_case__						)
         lowerCAmelCase					:					Dict								=							self.to_sql_kwargs.pop("index"		,					snake_case__						)

         lowerCAmelCase					:					Dict								=							self._write(index=snake_case__		,					**self.to_sql_kwargs						)
         return written

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					Optional[int]								=							args
         lowerCAmelCase					:					Optional[int]								=							{**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
         lowerCAmelCase					:					List[Any]								=							query_table(
             table=self.dataset.data		,					key=slice(snake_case__		,					offset + self.batch_size						)		,					indices=self.dataset._indices		,					)
         lowerCAmelCase					:					Tuple								=							batch.to_pandas()
         lowerCAmelCase					:					str								=							df.to_sql(self.name		,					self.con		,					index=snake_case__		,					**snake_case__						)
         return num_rows or len(snake_case__						)







       def 	lowercase__							(						self		,					snake_case__		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							0

         if self.num_proc is None or self.num_proc == 1:
           for offset in logging.tqdm(
               range(0		,					len(self.dataset						)		,					self.batch_size						)		,					unit="ba"		,					disable=not logging.is_progress_bar_enabled()		,					desc="Creating SQL from Arrow format"		,					):
             written += self._batch_sql((offset, index, to_sql_kwargs)						)
         else:
           lowerCAmelCase						,  lowerCAmelCase					:					Dict								=							len(self.dataset						), self.batch_size
           with multiprocessing.Pool(self.num_proc						) as pool:
             for num_rows in logging.tqdm(
                 pool.imap(
                     self._batch_sql		,					[(offset, index, to_sql_kwargs) for offset in range(0		,					snake_case__		,					snake_case__						)]		,					)		,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,					unit="ba"		,					disable=not logging.is_progress_bar_enabled()		,					desc="Creating SQL from Arrow format"		,					):
               written += num_rows

         return written





