





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__:							Union[str, Any]=None					,   UpperCamelCase__:							str=None    ):
		return field(default_factory=lambda: default					,   metadata=UpperCamelCase__    )




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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):






		try:
				int(UpperCamelCase__    )
				return True
		except ValueError:
				return False






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):






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




class   UpperCamelCase_       :


							def __init__(							self    :str							,				__A    :Any		)   ->   str:



									"""simple docstring"""



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

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


							def      _snake_case  (							self    :Optional[Any]		)   ->   Optional[Any]:



									"""simple docstring"""



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

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

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

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

													SCREAMING_SNAKE_CASE__			=      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.'''

									SCREAMING_SNAKE_CASE__			=      title_str[:-4]
									SCREAMING_SNAKE_CASE__			=      """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 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      HfArgumentParser(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      parser.parse_args_into_dataclasses()[0]
		SCREAMING_SNAKE_CASE__			=      Plot(args=UpperCamelCase__    )
		plot.plot()


if __name__ == "__main__":
	main()






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__    )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					field(default="image-classification"				,  metadata={"include_in_asdict_even_if_is_default": True}    )
							lowerCamelCase_           =					Features({"image": Image()}    )
							lowerCamelCase_           =					Features({"labels": ClassLabel}    )
							lowerCamelCase_           =					"image"
							lowerCamelCase_           =					"labels"


							def      _snake_case  (							self    :List[str]							,				__A    :Tuple		)   ->   Tuple:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]							,				__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__			=      self.label_schema.copy()
									SCREAMING_SNAKE_CASE__			=      features[self.label_column]
									SCREAMING_SNAKE_CASE__			=      label_schema
									return task_template


							@property
							def      _snake_case  (							self    :Dict		)   ->   Dict[str, str]:



									"""simple docstring"""



									return {
									    self.image_column: "image",
									    self.label_column: "labels",
									}