

"""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       SCREAMING_SNAKE_CASE							( lowercase__=None					,					lowercase__=None   )			->						List[Any]:
 return field(default_factory=lambda: default					,					metadata=lowercase__   )






@dataclass
class 			__a					:





       """simple docstring"""


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




def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						List[Any]:
 try:
  int(lowercase__   )
  return True
 except ValueError:
  return False



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 try:
  float(lowercase__   )
  return True
 except ValueError:
  return False






class 			__a					:





       """simple docstring"""


       def __init__(   self			,					snake_case   ):





        """simple docstring"""

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

         ((lowerCAmelCase__)			,						(lowerCAmelCase__))   :				Any 			=				(
             (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
         )

         lowerCAmelCase__   :				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:
           lowerCAmelCase__   :				Union[str, 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__   :				Any 			=				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__   :				Tuple 			=				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__   :				List[str] 			=				title_str[:-4]
        lowerCAmelCase__   :				Optional[Any] 			=				"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       SCREAMING_SNAKE_CASE							( )			->						Optional[int]:
 lowerCAmelCase__   :				str 			=				HfArgumentParser(lowercase__   )
 lowerCAmelCase__   :				List[Any] 			=				parser.parse_args_into_dataclasses()[0]
 lowerCAmelCase__   :				Optional[int] 			=				Plot(args=lowercase__   )
 plot.plot()


if __name__ == "__main__":
    main()









"""simple docstring"""

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Optional[Any]:
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"					,					[False, True]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Any:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Dict 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowerCAmelCase__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Tuple:
 lowerCAmelCase__   :				Union[str, Any] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Tuple 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				List[Any] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Tuple 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Optional[Any] 			=				{"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				List[str] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 2
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_3", "col_1", "col_2"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Tuple:
 # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				Any 			=				{"col_2": "int64", "col_3": "float64", "col_1": "string"}
 lowerCAmelCase__   :				Union[str, Any] 			=				features.copy()
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[str] 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 2
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_2", "col_3", "col_1"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("split"					,					[None, NamedSplit("train"   ), "train", "test"]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					split=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )
 assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"					,					[str, list]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 if issubclass(lowercase__					,					lowercase__   ):
  lowerCAmelCase__   :				Dict 			=				jsonl_path
 elif issubclass(lowercase__					,					lowercase__   ):
  lowerCAmelCase__   :				Tuple 			=				[jsonl_path]
 lowerCAmelCase__   :				Any 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[str] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				int 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__=("train",)   )			->						List[str]:
 assert isinstance(lowercase__					,					lowercase__   )
 for split in splits:
  lowerCAmelCase__   :				Dict 			=				dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"					,					[False, True]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				int 			=				tmp_path / "cache"
 lowerCAmelCase__   :				str 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowerCAmelCase__   :				Dict 			=				JsonDatasetReader({"train": jsonl_path}					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Union[str, Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				str 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				List[Any] 			=				JsonDatasetReader({"train": jsonl_path}					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



@pytest.mark.parametrize("split"					,					[None, NamedSplit("train"   ), "train", "test"]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Dict:
 if split:
  lowerCAmelCase__   :				int 			=				{split: jsonl_path}
 else:
  lowerCAmelCase__   :				str 			=				"train"
  lowerCAmelCase__   :				Tuple 			=				{"train": jsonl_path, "test": jsonl_path}
 lowerCAmelCase__   :				Any 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Dict 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				int 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__					,					splits=list(path.keys()   )   )
 assert all(dataset[split].split == split for split in path.keys()   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						List[Any]:
 return json.load(lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 return [json.loads(lowercase__   ) for line in buffer]






class 			__a					:





       """simple docstring"""


       @pytest.mark.parametrize("lines, load_json_function"			,					[(True, load_json_lines), (False, load_json)]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				int 			=				load_json_function(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        assert isinstance(exported_content[0]			,					snake_case   )
        assert len(snake_case   ) == 10






       @pytest.mark.parametrize(
           "orient, container, keys, len_at"			,					[
               ("records", list, {"tokens", "labels", "answers", "id"}, None),
               ("split", dict, {"columns", "data"}, "data"),
               ("index", dict, set("0123456789"   ), None),
               ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
               ("values", list, None, None),
               ("table", dict, {"schema", "data"}, "data"),
           ]			,					)
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case			,					orient=snake_case   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				Optional[Any] 			=				load_json(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        if keys:
         if container is dict:
          assert exported_content.keys() == keys
         else:
          assert exported_content[0].keys() == keys
        else:
         assert not hasattr(snake_case			,					"keys"   ) and not hasattr(exported_content[0]			,					"keys"   )
        if len_at:
         assert len(exported_content[len_at]   ) == 10
        else:
         assert len(snake_case   ) == 10






       @pytest.mark.parametrize("lines, load_json_function"			,					[(True, load_json_lines), (False, load_json)]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case			,					num_proc=2   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				int 			=				load_json_function(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        assert isinstance(exported_content[0]			,					snake_case   )
        assert len(snake_case   ) == 10






       @pytest.mark.parametrize(
           "orient, container, keys, len_at"			,					[
               ("records", list, {"tokens", "labels", "answers", "id"}, None),
               ("split", dict, {"columns", "data"}, "data"),
               ("index", dict, set("0123456789"   ), None),
               ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
               ("values", list, None, None),
               ("table", dict, {"schema", "data"}, "data"),
           ]			,					)
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case			,					orient=snake_case			,					num_proc=2   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				Tuple 			=				load_json(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        if keys:
         if container is dict:
          assert exported_content.keys() == keys
         else:
          assert exported_content[0].keys() == keys
        else:
         assert not hasattr(snake_case			,					"keys"   ) and not hasattr(exported_content[0]			,					"keys"   )
        if len_at:
         assert len(exported_content[len_at]   ) == 10
        else:
         assert len(snake_case   ) == 10






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        with pytest.raises(snake_case   ):
         with io.BytesIO() as buffer:
          JsonDatasetWriter(snake_case			,					snake_case			,					num_proc=0   )






       @pytest.mark.parametrize("compression, extension"			,					[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				tmp_path_factory.mktemp("data"   ) / F"""test.json.{extension}"""
        lowerCAmelCase__   :				str 			=				str(shared_datadir / F"""test_file.json.{extension}"""   )
        JsonDatasetWriter(snake_case			,					snake_case			,					compression=snake_case   ).write()

        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        assert exported_content == original_content






