





'''simple docstring'''






from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


lowerCAmelCase       		=      logging.get_logger(__name__)


lowerCAmelCase       		=      TypeVar("""DatasetType""", Dataset, IterableDataset)






def      __A						(							a_						:  List[DatasetType]      ,a_						:  Optional[List[float]] = None      ,a_						:  Optional[int] = None      ,a_						:  Optional[DatasetInfo] = None      ,a_						:  Optional[NamedSplit] = None      ,a_						:  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"      ,):
		from .arrow_dataset import Dataset
		from .iterable_dataset import IterableDataset

		if not datasets:
				raise ValueError("Unable to interleave an empty list of datasets."	)
		for i, dataset in enumerate(a_	):
				if not isinstance(a_      ,(Dataset, IterableDataset)	):
						if isinstance(a_      ,(DatasetDict, IterableDatasetDict)	):
								if not dataset:
										raise ValueError(
										    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
										    "is an empty dataset dictionary."	)
								raise ValueError(
								    f'''Dataset at position {i} has at least one split: {list(a_	)}\n'''
								    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(a_	)	)}\']'''	)
						raise ValueError(
						    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_	).__name__}.'''	)
				if i == 0:
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   (
						    (Dataset, IterableDataset) if isinstance(a_      ,a_	) else (IterableDataset, Dataset)
						)
				elif not isinstance(a_      ,a_	):
						raise ValueError(
						    f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''	)
		if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
				raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'''	)
		if dataset_type is Dataset:
				return _interleave_map_style_datasets(
				    a_      ,a_      ,a_      ,info=a_      ,split=a_      ,stopping_strategy=a_	)
		else:
				return _interleave_iterable_datasets(
				    a_      ,a_      ,a_      ,info=a_      ,split=a_      ,stopping_strategy=a_	)






def      __A						(							a_						:  List[DatasetType]      ,a_						:  Optional[DatasetInfo] = None      ,a_						:  Optional[NamedSplit] = None      ,a_						:  int = 0      ,):

		if not dsets:
				raise ValueError("Unable to concatenate an empty list of datasets."	)
		for i, dataset in enumerate(a_	):
				if not isinstance(a_      ,(Dataset, IterableDataset)	):
						if isinstance(a_      ,(DatasetDict, IterableDatasetDict)	):
								if not dataset:
										raise ValueError(
										    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
										    "is an empty dataset dictionary."	)
								raise ValueError(
								    f'''Dataset at position {i} has at least one split: {list(a_	)}\n'''
								    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(a_	)	)}\']'''	)
						raise ValueError(
						    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_	).__name__}.'''	)
				if i == 0:
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   (
						    (Dataset, IterableDataset) if isinstance(a_      ,a_	) else (IterableDataset, Dataset)
						)
				elif not isinstance(a_      ,a_	):
						raise ValueError(
						    f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''	)
		if dataset_type is Dataset:
				return _concatenate_map_style_datasets(a_      ,info=a_      ,split=a_      ,axis=a_	)
		else:
				return _concatenate_iterable_datasets(a_      ,info=a_      ,split=a_      ,axis=a_	)










'''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						(							a_						:  Any=None      ,a_						:  List[Any]=None	):
		return field(default_factory=lambda: default      ,metadata=a_	)
@dataclass
class 		lowerCamelCase  :
	snake_case_      =   field(
	    metadata={"help": "The csv file to plot."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Disable logarithmic scale when plotting"}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,   )
	snake_case_      =   list_field(
	    default=_A      ,   metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )






def      __A						(							a_						:  Tuple	):
		try:
				int(a_	)
				return True
		except ValueError:
				return False






def      __A						(							a_						:  int	):
		try:
				float(a_	)
				return True
		except ValueError:
				return False
class 		lowerCamelCase  :



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

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





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

					((lowerCAmelCase)	,		(lowerCAmelCase))     :    str						=   (
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

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

							lowerCAmelCase     :    Union[str, Any]						=   np.asarray(a_		,      a_	)[: len(a_	)]
							plt.scatter(
							    a_		,      a_		,      label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
							plt.plot(a_		,      a_		,      "--"	)

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

			lowerCAmelCase     :    List[str]						=   title_str[:-4]
			lowerCAmelCase     :    List[Any]						=   "Time in s" if self.args.is_time else "Memory in MB"

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

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






def      __A						(							):
		lowerCAmelCase     :    Optional[Any]						=   HfArgumentParser(a_	)
		lowerCAmelCase     :    List[Any]						=   parser.parse_args_into_dataclasses()[0]
		lowerCAmelCase     :    str						=   Plot(args=a_	)
		plot.plot()


if __name__ == "__main__":
					main()



