





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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase=None		,      _UpperCAmelCase=None					)     ->	Any:
    return field(default_factory=lambda: default		,      metadata=_UpperCAmelCase					)

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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Union[str, Any]:
    try:
        int(_UpperCAmelCase					)
        return True
    except ValueError:
        return False


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	List[str]:
    try:
        float(_UpperCAmelCase					)
        return True
    except ValueError:
        return False

class   lowerCAmelCase				:
 def __init__( self    : List[str]     ,	UpperCAmelCase    : Dict      )     ->      Optional[Any]:
     lowerCamelCase__       : Tuple   					=						args
     lowerCamelCase__       : Any   					=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

     with open(self.args.csv_file     ,	newline=''      ) as csv_file:
         lowerCamelCase__       : Any   					=						csv.DictReader(UpperCAmelCase      )
         for row in reader:
             lowerCamelCase__       : int   					=						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__       : Dict   					=						int(row['result']      )
             elif can_convert_to_float(row['result']      ):
                 # value is not None
                 lowerCamelCase__       : str   					=						float(row['result']      )







 def 	A_      ( self    : Optional[Any]      )     ->      Any:
     lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						plt.subplots()
     lowerCamelCase__       : Optional[int]   					=						'Time usage' if self.args.is_time else 'Memory usage'
     lowerCamelCase__       : List[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__       : str   					=						sorted(set(self.result_dict[model_name]['seq_len']      )      )
         lowerCamelCase__       : Optional[Any]   					=						self.result_dict[model_name]['result']

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

         lowerCamelCase__       : List[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:
                 lowerCamelCase__       : Optional[int]   					=						np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,	dtype=UpperCAmelCase     ,	)
             else:
                 lowerCamelCase__       : Dict   					=						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__))       : Dict   					=						(
                 ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
             )

             lowerCamelCase__       : Optional[int]   					=						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."""

     lowerCamelCase__       : List[str]   					=						title_str[:-4]
     lowerCamelCase__       : Dict   					=						'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 			SCREAMING_SNAKE_CASE (			)     ->	int:
    lowerCamelCase__       : List[Any]   					=						HfArgumentParser(_UpperCAmelCase					)
    lowerCamelCase__       : Optional[int]   					=						parser.parse_args_into_dataclasses()[0]
    lowerCamelCase__       : Dict   					=						Plot(args=_UpperCAmelCase					)
    plot.plot()


if __name__ == "__main__":
    main()












import pytest


_UpperCAmelCase							:			Optional[int]        =							"""__dummy_dataset1__"""

_UpperCAmelCase							:			Dict        =							"""
import json
import os

import datasets


REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),
                \"ner_tags\": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            \"O\",
                            \"B-PER\",
                            \"I-PER\",
                            \"B-ORG\",
                            \"I-ORG\",
                            \"B-LOC\",
                            \"I-LOC\",
                        ]
                    )
                ),
                \"langs\": datasets.Sequence(datasets.Value(\"string\")),
                \"spans\": datasets.Sequence(datasets.Value(\"string\")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, \"r\", encoding=\"utf-8\") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
"""


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    return DATASET_LOADING_SCRIPT_NAME


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	List[Any]:
    return DATASET_LOADING_SCRIPT_CODE


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[Any]:
    lowerCamelCase__       : int   					=						dataset_loading_script_name
    lowerCamelCase__       : Any   					=						tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						script_dir / F"""{script_name}.py"""
    with open(_UpperCAmelCase		,      'w'					) as f:
        f.write(_UpperCAmelCase					)
    return str(_UpperCAmelCase					)





