



import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    YosoForMaskedLM,
						    YosoForMultipleChoice,
						    YosoForQuestionAnswering,
						    YosoForSequenceClassification,
						    YosoForTokenClassification,
						    YosoModel,
						)
						from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Dict ,			__lowercase						:						str ,			__lowercase						:						int=13 ,			__lowercase						:						Any=7 ,			__lowercase						:						List[str]=True ,			__lowercase						:						int=True ,			__lowercase						:						Dict=True ,			__lowercase						:						Tuple=True ,			__lowercase						:						Tuple=99 ,			__lowercase						:						Optional[int]=32 ,			__lowercase						:						List[str]=5 ,			__lowercase						:						Any=4 ,			__lowercase						:						int=37 ,			__lowercase						:						str="gelu" ,			__lowercase						:						List[str]=0.1 ,			__lowercase						:						Optional[int]=0.1 ,			__lowercase						:						Tuple=512 ,			__lowercase						:						int=16 ,			__lowercase						:						List[str]=2 ,			__lowercase						:						Tuple=0.02 ,			__lowercase						:						int=3 ,			__lowercase						:						Optional[int]=4 ,			__lowercase						:						Dict=None ,			):

											'''simple docstring'''
											__a					       =						parent
											__a					       =						batch_size
											__a					       =						seq_length
											__a					       =						is_training
											__a					       =						use_input_mask
											__a					       =						use_token_type_ids
											__a					       =						use_labels
											__a					       =						vocab_size
											__a					       =						hidden_size
											__a					       =						num_hidden_layers
											__a					       =						num_attention_heads
											__a					       =						intermediate_size
											__a					       =						hidden_act
											__a					       =						hidden_dropout_prob
											__a					       =						attention_probs_dropout_prob
											__a					       =						max_position_embeddings
											__a					       =						type_vocab_size
											__a					       =						type_sequence_label_size
											__a					       =						initializer_range
											__a					       =						num_labels
											__a					       =						num_choices
											__a					       =						scope





							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a					       =						ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size		)

											__a					       =						None
											if self.use_input_mask:
															__a					       =						random_attention_mask([self.batch_size, self.seq_length]		)

											__a					       =						None
											if self.use_token_type_ids:
															__a					       =						ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size		)

											__a					       =						None
											__a					       =						None
											__a					       =						None
											if self.use_labels:
															__a					       =						ids_tensor([self.batch_size] ,			self.type_sequence_label_size		)
															__a					       =						ids_tensor([self.batch_size, self.seq_length] ,			self.num_labels		)
															__a					       =						ids_tensor([self.batch_size] ,			self.num_choices		)

											__a					       =						self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											return YosoConfig(
											    vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			is_decoder=SCREAMING_SNAKE_CASE__ ,			initializer_range=self.initializer_range ,			)





							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											__a					       =						self.get_config()
											__a					       =						300
											return config





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											(
											    __a      
											)					       =						self.prepare_config_and_inputs()

											__a					       =						True
											__a					       =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
											__a					       =						ids_tensor([self.batch_size, self.seq_length] ,			vocab_size=2		)

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_mask,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)





							def   UpperCamelCase_   (				self						:						List[Any] ,			__lowercase						:						Optional[int] ,			__lowercase						:						Optional[Any] ,			__lowercase						:						str ,			__lowercase						:						Optional[Any] ,			__lowercase						:						str ,			__lowercase						:						Optional[Any] ,			__lowercase						:						Any		):

											'''simple docstring'''
											__a					       =						YosoModel(config=SCREAMING_SNAKE_CASE__		)
											model.to(SCREAMING_SNAKE_CASE__		)
											model.eval()
											__a					       =						model(SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__		)
											__a					       =						model(SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__		)
											__a					       =						model(SCREAMING_SNAKE_CASE__		)
											self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)		)





							def   UpperCamelCase_   (				self						:						List[Any] ,			__lowercase						:						str ,			__lowercase						:						str ,			__lowercase						:						Optional[Any] ,			__lowercase						:						Tuple ,			__lowercase						:						str ,			__lowercase						:						Any ,			__lowercase						:						Tuple ,			__lowercase						:						Optional[Any] ,			__lowercase						:						str ,			):

											'''simple docstring'''
											__a					       =						True
											__a					       =						YosoModel(SCREAMING_SNAKE_CASE__		)
											model.to(SCREAMING_SNAKE_CASE__		)
											model.eval()
											__a					       =						model(
											    SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__ ,			encoder_hidden_states=SCREAMING_SNAKE_CASE__ ,			encoder_attention_mask=SCREAMING_SNAKE_CASE__ ,			)
											__a					       =						model(
											    SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__ ,			encoder_hidden_states=SCREAMING_SNAKE_CASE__ ,			)
											__a					       =						model(SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__		)
											self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)		)





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						List[str] ,			__lowercase						:						Dict ,			__lowercase						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						Any ,			__lowercase						:						str ,			__lowercase						:						Union[str, Any]		):

											'''simple docstring'''
											__a					       =						YosoForMaskedLM(config=SCREAMING_SNAKE_CASE__		)
											model.to(SCREAMING_SNAKE_CASE__		)
											model.eval()
											__a					       =						model(SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__ ,			labels=SCREAMING_SNAKE_CASE__		)
											self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)		)





							def   UpperCamelCase_   (				self						:						Any ,			__lowercase						:						List[Any] ,			__lowercase						:						int ,			__lowercase						:						Dict ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						Optional[Any] ,			__lowercase						:						Tuple ,			__lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a					       =						YosoForQuestionAnswering(config=SCREAMING_SNAKE_CASE__		)
											model.to(SCREAMING_SNAKE_CASE__		)
											model.eval()
											__a					       =						model(
											    SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__ ,			start_positions=SCREAMING_SNAKE_CASE__ ,			end_positions=SCREAMING_SNAKE_CASE__ ,			)
											self.parent.assertEqual(result.start_logits.shape ,			(self.batch_size, self.seq_length)		)
											self.parent.assertEqual(result.end_logits.shape ,			(self.batch_size, self.seq_length)		)





							def   UpperCamelCase_   (				self						:						str ,			__lowercase						:						Optional[Any] ,			__lowercase						:						int ,			__lowercase						:						Tuple ,			__lowercase						:						Optional[int] ,			__lowercase						:						Optional[int] ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.num_labels
											__a					       =						YosoForSequenceClassification(SCREAMING_SNAKE_CASE__		)
											model.to(SCREAMING_SNAKE_CASE__		)
											model.eval()
											__a					       =						model(SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__ ,			labels=SCREAMING_SNAKE_CASE__		)
											self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)		)





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						str ,			__lowercase						:						Optional[int] ,			__lowercase						:						Tuple ,			__lowercase						:						List[str] ,			__lowercase						:						List[Any] ,			__lowercase						:						Tuple ,			__lowercase						:						Any		):

											'''simple docstring'''
											__a					       =						self.num_labels
											__a					       =						YosoForTokenClassification(config=SCREAMING_SNAKE_CASE__		)
											model.to(SCREAMING_SNAKE_CASE__		)
											model.eval()
											__a					       =						model(SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__ ,			labels=SCREAMING_SNAKE_CASE__		)
											self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)		)





							def   UpperCamelCase_   (				self						:						List[Any] ,			__lowercase						:						Tuple ,			__lowercase						:						Tuple ,			__lowercase						:						Dict ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						Optional[int] ,			__lowercase						:						List[str] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.num_choices
											__a					       =						YosoForMultipleChoice(config=SCREAMING_SNAKE_CASE__		)
											model.to(SCREAMING_SNAKE_CASE__		)
											model.eval()
											__a					       =						input_ids.unsqueeze(1		).expand(-1 ,			self.num_choices ,			-1		).contiguous()
											__a					       =						token_type_ids.unsqueeze(1		).expand(-1 ,			self.num_choices ,			-1		).contiguous()
											__a					       =						input_mask.unsqueeze(1		).expand(-1 ,			self.num_choices ,			-1		).contiguous()
											__a					       =						model(
											    SCREAMING_SNAKE_CASE__ ,			attention_mask=SCREAMING_SNAKE_CASE__ ,			token_type_ids=SCREAMING_SNAKE_CASE__ ,			labels=SCREAMING_SNAKE_CASE__ ,			)
											self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_choices)		)







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.prepare_config_and_inputs()
											(
											    __a      
											)					       =						config_and_inputs
											__a					       =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_torch
class 				SCREAMING_SNAKE_CASE     (							_a     , _a     , unittest.TestCase   ):
							__lowerCamelCase		:		List[Any]      =(
							    (
							        YosoModel,
							        YosoForMaskedLM,
							        YosoForMultipleChoice,
							        YosoForQuestionAnswering,
							        YosoForSequenceClassification,
							        YosoForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowerCamelCase		:		Any      =False
							__lowerCamelCase		:		Tuple      =False
							__lowerCamelCase		:		Dict      =False

							__lowerCamelCase		:		Any      =()
							__lowerCamelCase		:		int      =(
							    {
							        '''feature-extraction''': YosoModel,
							        '''fill-mask''': YosoForMaskedLM,
							        '''question-answering''': YosoForQuestionAnswering,
							        '''text-classification''': YosoForSequenceClassification,
							        '''token-classification''': YosoForTokenClassification,
							        '''zero-shot''': YosoForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											__a					       =						YosoModelTester(self		)
											__a					       =						ConfigTester(self ,			config_class=SCREAMING_SNAKE_CASE__ ,			hidden_size=37		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											self.config_tester.run_common_tests()





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)





							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
															__a					       =						type
															self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)





							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__		)





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__		)





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__		)





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__		)





							@slow
							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a					       =						YosoModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
															self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)







							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											return






@require_torch
class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							@slow
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a					       =						YosoModel.from_pretrained("""uw-madison/yoso-4096"""		)
											__a					       =						torch.tensor([[0, 1, 2, 3, 4, 5]]		)

											with torch.no_grad():
															__a					       =						model(SCREAMING_SNAKE_CASE__		)[0]

											__a					       =						torch.Size((1, 6, 768)		)
											self.assertEqual(output.shape ,			SCREAMING_SNAKE_CASE__		)

											__a					       =						torch.tensor(
											    [[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]		)

											self.assertTrue(torch.allclose(output[:, :3, :3] ,			SCREAMING_SNAKE_CASE__ ,			atol=1E-4		)		)





							@slow
							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											__a					       =						YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""		)
											__a					       =						torch.tensor([[0, 1, 2, 3, 4, 5]]		)

											with torch.no_grad():
															__a					       =						model(SCREAMING_SNAKE_CASE__		)[0]

											__a					       =						50265

											__a					       =						torch.Size((1, 6, vocab_size)		)
											self.assertEqual(output.shape ,			SCREAMING_SNAKE_CASE__		)

											__a					       =						torch.tensor(
											    [[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]		)

											self.assertTrue(torch.allclose(output[:, :3, :3] ,			SCREAMING_SNAKE_CASE__ ,			atol=1E-4		)		)







							@slow
							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""		)
											__a					       =						torch.arange(4096		).unsqueeze(0		)

											with torch.no_grad():
															__a					       =						model(SCREAMING_SNAKE_CASE__		)[0]

											__a					       =						50265

											__a					       =						torch.Size((1, 4096, vocab_size)		)
											self.assertEqual(output.shape ,			SCREAMING_SNAKE_CASE__		)

											__a					       =						torch.tensor(
											    [[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]		)

											self.assertTrue(torch.allclose(output[:, :3, :3] ,			SCREAMING_SNAKE_CASE__ ,			atol=1E-4		)		)





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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict=None			,       _SCREAMING_SNAKE_CASE    :     List[str]=None      ):
				"""simple docstring"""







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






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



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







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






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						args
											__a					       =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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







							def   UpperCamelCase_   (				self						:						List[Any]		):

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

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

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

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

																			__a					       =						np.asarray(__lowercase ,			__lowercase		)[: len(__lowercase		)]
																			plt.scatter(
																			    __lowercase ,			__lowercase ,			label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
																			plt.plot(__lowercase ,			__lowercase ,			"""--"""		)

															title_str += F" {label_model_name} vs."

											__a					       =						title_str[:-4]
											__a					       =						"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser(_SCREAMING_SNAKE_CASE      )
				__a					       =						parser.parse_args_into_dataclasses()[0]
				__a					       =						Plot(args=_SCREAMING_SNAKE_CASE      )
				plot.plot()


if __name__ == "__main__":
						main()
