


"""simple docstring"""







import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       VideoMAEConfig()

					set_architecture_configs(lowerCAmelCase_						,		lowerCAmelCase_     )

					if "finetuned" not in model_name:
										__SCREAMING_SNAKE_CASE		     =       False

					if "finetuned" in model_name:
										__SCREAMING_SNAKE_CASE		     =       "huggingface/label-files"
										if "kinetics" in model_name:
															__SCREAMING_SNAKE_CASE		     =       400
															__SCREAMING_SNAKE_CASE		     =       "kinetics400-id2label.json"
										elif "ssv2" in model_name:
															__SCREAMING_SNAKE_CASE		     =       174
															__SCREAMING_SNAKE_CASE		     =       "something-something-v2-id2label.json"
										else:
															raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."     )
										__SCREAMING_SNAKE_CASE		     =       json.load(open(hf_hub_download(lowerCAmelCase_						,		lowerCAmelCase_						,		repo_type="dataset"     )						,		"r"     )     )
										__SCREAMING_SNAKE_CASE		     =       {int(lowerCAmelCase_     ): v for k, v in idalabel.items()}
										__SCREAMING_SNAKE_CASE		     =       idalabel
										__SCREAMING_SNAKE_CASE		     =       {v: k for k, v in idalabel.items()}

					return config




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if "small" in model_name:
										__SCREAMING_SNAKE_CASE		     =       384
										__SCREAMING_SNAKE_CASE		     =       1536
										__SCREAMING_SNAKE_CASE		     =       12
										__SCREAMING_SNAKE_CASE		     =       16
										__SCREAMING_SNAKE_CASE		     =       12
										__SCREAMING_SNAKE_CASE		     =       3
										__SCREAMING_SNAKE_CASE		     =       192
										__SCREAMING_SNAKE_CASE		     =       768
					elif "large" in model_name:
										__SCREAMING_SNAKE_CASE		     =       1024
										__SCREAMING_SNAKE_CASE		     =       4096
										__SCREAMING_SNAKE_CASE		     =       24
										__SCREAMING_SNAKE_CASE		     =       16
										__SCREAMING_SNAKE_CASE		     =       12
										__SCREAMING_SNAKE_CASE		     =       8
										__SCREAMING_SNAKE_CASE		     =       512
										__SCREAMING_SNAKE_CASE		     =       2048
					elif "huge" in model_name:
										__SCREAMING_SNAKE_CASE		     =       1280
										__SCREAMING_SNAKE_CASE		     =       5120
										__SCREAMING_SNAKE_CASE		     =       32
										__SCREAMING_SNAKE_CASE		     =       16
										__SCREAMING_SNAKE_CASE		     =       12
										__SCREAMING_SNAKE_CASE		     =       8
										__SCREAMING_SNAKE_CASE		     =       640
										__SCREAMING_SNAKE_CASE		     =       2560
					elif "base" not in model_name:
										raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					if "encoder." in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("encoder."						,		""     )
					if "cls_token" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("cls_token"						,		"videomae.embeddings.cls_token"     )
					if "decoder_pos_embed" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("decoder_pos_embed"						,		"decoder.decoder_pos_embed"     )
					if "pos_embed" in name and "decoder" not in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("pos_embed"						,		"videomae.embeddings.position_embeddings"     )
					if "patch_embed.proj" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("patch_embed.proj"						,		"videomae.embeddings.patch_embeddings.projection"     )
					if "patch_embed.norm" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("patch_embed.norm"						,		"videomae.embeddings.norm"     )
					if "decoder.blocks" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("decoder.blocks"						,		"decoder.decoder_layers"     )
					if "blocks" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("blocks"						,		"videomae.encoder.layer"     )
					if "attn.proj" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("attn.proj"						,		"attention.output.dense"     )
					if "attn" in name and "bias" not in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("attn"						,		"attention.self"     )
					if "attn" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("attn"						,		"attention.attention"     )
					if "norm1" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("norm1"						,		"layernorm_before"     )
					if "norm2" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("norm2"						,		"layernorm_after"     )
					if "mlp.fc1" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("mlp.fc1"						,		"intermediate.dense"     )
					if "mlp.fc2" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("mlp.fc2"						,		"output.dense"     )
					if "decoder_embed" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("decoder_embed"						,		"decoder.decoder_embed"     )
					if "decoder_norm" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("decoder_norm"						,		"decoder.decoder_norm"     )
					if "decoder_pred" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("decoder_pred"						,		"decoder.decoder_pred"     )
					if "norm.weight" in name and "decoder" not in name and "fc" not in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("norm.weight"						,		"videomae.layernorm.weight"     )
					if "norm.bias" in name and "decoder" not in name and "fc" not in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("norm.bias"						,		"videomae.layernorm.bias"     )
					if "head" in name and "decoder" not in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("head"						,		"classifier"     )

					return name




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										__SCREAMING_SNAKE_CASE		     =       orig_state_dict.pop(lowerCAmelCase_     )

										if key.startswith("encoder."     ):
															__SCREAMING_SNAKE_CASE		     =       key.replace("encoder."						,		""     )

										if "qkv" in key:
															__SCREAMING_SNAKE_CASE		     =       key.split("."     )
															if key.startswith("decoder.blocks"     ):
																				__SCREAMING_SNAKE_CASE		     =       config.decoder_hidden_size
																				__SCREAMING_SNAKE_CASE		     =       int(key_split[2]     )
																				__SCREAMING_SNAKE_CASE		     =       "decoder.decoder_layers."
																				if "weight" in key:
																									__SCREAMING_SNAKE_CASE		     =       val[:dim, :]
																									__SCREAMING_SNAKE_CASE		     =       val[dim : dim * 2, :]
																									__SCREAMING_SNAKE_CASE		     =       val[-dim:, :]
															else:
																				__SCREAMING_SNAKE_CASE		     =       config.hidden_size
																				__SCREAMING_SNAKE_CASE		     =       int(key_split[1]     )
																				__SCREAMING_SNAKE_CASE		     =       "videomae.encoder.layer."
																				if "weight" in key:
																									__SCREAMING_SNAKE_CASE		     =       val[:dim, :]
																									__SCREAMING_SNAKE_CASE		     =       val[dim : dim * 2, :]
																									__SCREAMING_SNAKE_CASE		     =       val[-dim:, :]
										else:
															__SCREAMING_SNAKE_CASE		     =       val

					return orig_state_dict




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       hf_hub_download(
					    repo_id="hf-internal-testing/spaghetti-video"						,		filename="eating_spaghetti.npy"						,		repo_type="dataset"     )
					__SCREAMING_SNAKE_CASE		     =       np.load(lowerCAmelCase_     )
					return list(lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       get_videomae_config(lowerCAmelCase_     )

					if "finetuned" in model_name:
										__SCREAMING_SNAKE_CASE		     =       VideoMAEForVideoClassification(lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE		     =       VideoMAEForPreTraining(lowerCAmelCase_     )

					# download original checkpoint, hosted on Google Drive
					__SCREAMING_SNAKE_CASE		     =       "pytorch_model.bin"
					gdown.cached_download(lowerCAmelCase_						,		lowerCAmelCase_						,		quiet=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_						,		map_location="cpu"     )
					if "model" in files:
										__SCREAMING_SNAKE_CASE		     =       files["model"]
					else:
										__SCREAMING_SNAKE_CASE		     =       files["module"]
					__SCREAMING_SNAKE_CASE		     =       convert_state_dict(lowerCAmelCase_						,		lowerCAmelCase_     )

					model.load_state_dict(lowerCAmelCase_     )
					model.eval()

					# verify model on basic input
					__SCREAMING_SNAKE_CASE		     =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,		image_std=[0.5, 0.5, 0.5]     )
					__SCREAMING_SNAKE_CASE		     =       prepare_video()
					__SCREAMING_SNAKE_CASE		     =       image_processor(lowerCAmelCase_						,		return_tensors="pt"     )

					if "finetuned" not in model_name:
										__SCREAMING_SNAKE_CASE		     =       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"						,		filename="bool_masked_pos.pt"     )
										__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       model(**lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       outputs.logits

					__SCREAMING_SNAKE_CASE		     =       [
					    "videomae-small-finetuned-kinetics",
					    "videomae-small-finetuned-ssv2",
					    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
					    "videomae-base-short",
					    "videomae-base-short-finetuned-kinetics",
					    "videomae-base",
					    "videomae-base-finetuned-kinetics",
					    "videomae-large",
					    "videomae-large-finetuned-kinetics",
					    "videomae-huge-finetuned-kinetics",
					    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
					    "videomae-base-short-ssv2",
					    "videomae-base-short-finetuned-ssv2",
					    "videomae-base-ssv2",
					    "videomae-base-finetuned-ssv2",
					]

					# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
					if model_name == "videomae-small-finetuned-kinetics":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 400]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([-0.9291, -0.4061, -0.9307]     )
					elif model_name == "videomae-small-finetuned-ssv2":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 174]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([0.2671, -0.4689, -0.8235]     )
					elif model_name == "videomae-base":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 1408, 1536]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]     )
					elif model_name == "videomae-base-short":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 1408, 1536]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]     )
										# we verified the loss both for normalized and unnormalized targets for this one
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([0.5142]     ) if config.norm_pix_loss else torch.tensor([0.6469]     )
					elif model_name == "videomae-large":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 1408, 1536]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]     )
					elif model_name == "videomae-large-finetuned-kinetics":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 400]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([0.0771, 0.0011, -0.3625]     )
					elif model_name == "videomae-huge-finetuned-kinetics":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 400]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([0.2433, 0.1632, -0.4894]     )
					elif model_name == "videomae-base-short-finetuned-kinetics":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 400]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([0.6588, 0.0990, -0.2493]     )
					elif model_name == "videomae-base-finetuned-kinetics":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 400]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([0.3669, -0.0688, -0.2421]     )
					elif model_name == "videomae-base-short-ssv2":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 1408, 1536]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]     )
					elif model_name == "videomae-base-short-finetuned-ssv2":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 174]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([-0.0537, -0.1539, -0.3266]     )
					elif model_name == "videomae-base-ssv2":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 1408, 1536]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]     )
					elif model_name == "videomae-base-finetuned-ssv2":
										__SCREAMING_SNAKE_CASE		     =       torch.Size([1, 174]     )
										__SCREAMING_SNAKE_CASE		     =       torch.tensor([0.1961, -0.8337, -0.6389]     )
					else:
										raise ValueError(f"""Model name not supported. Should be one of {model_names}"""     )

					# verify logits
					assert logits.shape == expected_shape
					if "finetuned" in model_name:
										assert torch.allclose(logits[0, :3]						,		lowerCAmelCase_						,		atol=1E-4     )
					else:
										print("Logits:"						,		logits[0, :3, :3]     )
										assert torch.allclose(logits[0, :3, :3]						,		lowerCAmelCase_						,		atol=1E-4     )
					print("Logits ok!"     )

					# verify loss, if applicable
					if model_name == "videomae-base-short":
										__SCREAMING_SNAKE_CASE		     =       outputs.loss
										assert torch.allclose(lowerCAmelCase_						,		lowerCAmelCase_						,		atol=1E-4     )
										print("Loss ok!"     )

					if pytorch_dump_folder_path is not None:
										print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
										image_processor.save_pretrained(lowerCAmelCase_     )
										model.save_pretrained(lowerCAmelCase_     )

					if push_to_hub:
										print("Pushing to the hub..."     )
										model.push_to_hub(lowerCAmelCase_						,		organization="nielsr"     )


if __name__ == "__main__":
		a__       :							List[Any]        =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
		    type=str,
		    help=(
		        '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
		        ''' download link.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		a__       :							Optional[Any]        =  parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




"""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      UpperCAmelCase__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


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




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						__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(UpperCAmelCase__     )
											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 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__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=UpperCAmelCase__    ,    )
																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(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."""

						__SCREAMING_SNAKE_CASE		     =       title_str[:-4]
						__SCREAMING_SNAKE_CASE		     =       "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      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
