



'''simple docstring'''


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class 					lowercase__						(		nn.Module      ):





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case=1.0						):
											super().__init__()

											_SCREAMING_SNAKE_CASE				: Any  	=   cut_size
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   cut_power






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   pixel_values.shape[2:4]
											_SCREAMING_SNAKE_CASE				: Any  	=   min(__snake_case					,      __snake_case						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   min(__snake_case					,      __snake_case					,      self.cut_size						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   []
											for _ in range(__snake_case						):
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   int(torch.rand([]						) ** self.cut_power * (max_size - min_size) + min_size						)
															_SCREAMING_SNAKE_CASE				: str  	=   torch.randint(0					,      sideX - size + 1					,      ()						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   torch.randint(0					,      sideY - size + 1					,      ()						)
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(__snake_case					,      self.cut_size						)						)
											return torch.cat(__snake_case						)







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[str]  	=   F.normalize(SCREAMING_SNAKE_CASE__							,   dim=-1  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   F.normalize(SCREAMING_SNAKE_CASE__							,   dim=-1  )
				return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				for param in model.parameters():
								_SCREAMING_SNAKE_CASE				: int  	=   value







class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      ):
											super().__init__()
											self.register_modules(
											    vae=__snake_case					,      text_encoder=__snake_case					,      clip_model=__snake_case					,      tokenizer=__snake_case					,      unet=__snake_case					,      scheduler=__snake_case					,      feature_extractor=__snake_case					,      )

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   transforms.Normalize(mean=feature_extractor.image_mean					,      std=feature_extractor.image_std						)
											_SCREAMING_SNAKE_CASE				: str  	=   (
											    feature_extractor.size
											    if isinstance(feature_extractor.size					,      __snake_case						)
											    else feature_extractor.size["""shortest_edge"""]
											)
											_SCREAMING_SNAKE_CASE				: Dict  	=   MakeCutouts(self.cut_out_size						)

											set_requires_grad(self.text_encoder					,      __snake_case						)
											set_requires_grad(self.clip_model					,      __snake_case						)






							def UpperCAmelCase_			(					self					,      __snake_case = "auto"						):
											if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															_SCREAMING_SNAKE_CASE				: Tuple  	=   self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(__snake_case						)






							def UpperCAmelCase_			(					self						):
											self.enable_attention_slicing(__snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.vae					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.vae					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.unet					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.unet					,      __snake_case						)






							@torch.enable_grad()
							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case=True					,      ):
											_SCREAMING_SNAKE_CASE				: int  	=   latents.detach().requires_grad_()

											_SCREAMING_SNAKE_CASE				: Dict  	=   self.scheduler.scale_model_input(__snake_case					,      __snake_case						)

											# predict the noise residual
											_SCREAMING_SNAKE_CASE				: Tuple  	=   self.unet(__snake_case					,      __snake_case					,      encoder_hidden_states=__snake_case						).sample

											if isinstance(self.scheduler					,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.scheduler.alphas_cumprod[timestep]
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   1 - alpha_prod_t
															# compute predicted original sample from predicted noise also called
															# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
															_SCREAMING_SNAKE_CASE				: Tuple  	=   (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															_SCREAMING_SNAKE_CASE				: List[Any]  	=   torch.sqrt(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Tuple  	=   pred_original_sample * (fac) + latents * (1 - fac)
											elif isinstance(self.scheduler					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.scheduler.sigmas[index]
															_SCREAMING_SNAKE_CASE				: str  	=   latents - sigma * noise_pred
											else:
															raise ValueError(f"""scheduler type {type(self.scheduler						)} not supported"""						)

											_SCREAMING_SNAKE_CASE				: Any  	=   1 / self.vae.config.scaling_factor * sample
											_SCREAMING_SNAKE_CASE				: int  	=   self.vae.decode(__snake_case						).sample
											_SCREAMING_SNAKE_CASE				: str  	=   (image / 2 + 0.5).clamp(0					,      1						)

											if use_cutouts:
															_SCREAMING_SNAKE_CASE				: Any  	=   self.make_cutouts(__snake_case					,      __snake_case						)
											else:
															_SCREAMING_SNAKE_CASE				: Any  	=   transforms.Resize(self.cut_out_size						)(__snake_case						)
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.normalize(__snake_case						).to(latents.dtype						)

											_SCREAMING_SNAKE_CASE				: str  	=   self.clip_model.get_image_features(__snake_case						)
											_SCREAMING_SNAKE_CASE				: Tuple  	=   image_embeddings_clip / image_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=__snake_case						)

											if use_cutouts:
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   spherical_dist_loss(__snake_case					,      __snake_case						)
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   dists.view([num_cutouts, sample.shape[0], -1]						)
															_SCREAMING_SNAKE_CASE				: Any  	=   dists.sum(2						).mean(0						).sum() * clip_guidance_scale
											else:
															_SCREAMING_SNAKE_CASE				: Any  	=   spherical_dist_loss(__snake_case					,      __snake_case						).mean() * clip_guidance_scale

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   -torch.autograd.grad(__snake_case					,      __snake_case						)[0]

											if isinstance(self.scheduler					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: Any  	=   latents.detach() + grads * (sigma**2)
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   noise_pred_original
											else:
															_SCREAMING_SNAKE_CASE				: Tuple  	=   noise_pred_original - torch.sqrt(__snake_case						) * grads
											return noise_pred, latents






							@torch.no_grad()
							def __call__(					self					,      __snake_case					,      __snake_case = 512					,      __snake_case = 512					,      __snake_case = 50					,      __snake_case = 7.5					,      __snake_case = 1					,      __snake_case = 0.0					,      __snake_case = 100					,      __snake_case = None					,      __snake_case = 4					,      __snake_case = True					,      __snake_case = None					,      __snake_case = None					,      __snake_case = "pil"					,      __snake_case = True					,      ):
											if isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: str  	=   1
											elif isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   len(__snake_case						)
											else:
															raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__snake_case						)}"""						)

											if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""						)

											# get prompt text embeddings
											_SCREAMING_SNAKE_CASE				: Dict  	=   self.tokenizer(
											    __snake_case					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=__snake_case					,      return_tensors="""pt"""					,      )
											_SCREAMING_SNAKE_CASE				: List[str]  	=   self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
											# duplicate text embeddings for each generation per prompt
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   text_embeddings.repeat_interleave(__snake_case					,      dim=0						)

											if clip_guidance_scale > 0:
															if clip_prompt is not None:
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   self.tokenizer(
																			    __snake_case					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=__snake_case					,      return_tensors="""pt"""					,      ).input_ids.to(self.device						)
															else:
																			_SCREAMING_SNAKE_CASE				: int  	=   text_input.input_ids.to(self.device						)
															_SCREAMING_SNAKE_CASE				: str  	=   self.clip_model.get_text_features(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Dict  	=   text_embeddings_clip / text_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=__snake_case						)
															# duplicate text embeddings clip for each generation per prompt
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   text_embeddings_clip.repeat_interleave(__snake_case					,      dim=0						)

											# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
											# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
											# corresponds to doing no classifier free guidance.
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
															_SCREAMING_SNAKE_CASE				: Dict  	=   text_input.input_ids.shape[-1]
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   self.tokenizer([""""""]					,      padding="""max_length"""					,      max_length=__snake_case					,      return_tensors="""pt"""						)
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
															# duplicate unconditional embeddings for each generation per prompt
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   uncond_embeddings.repeat_interleave(__snake_case					,      dim=0						)

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   torch.cat([uncond_embeddings, text_embeddings]						)

											# get the initial random noise unless the user supplied it

											# Unlike in other pipelines, latents need to be generated in the target device
											# for 1-to-1 results reproducibility with the CompVis implementation.
											# However this currently doesn't work in `mps`.
											_SCREAMING_SNAKE_CASE				: Tuple  	=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   text_embeddings.dtype
											if latents is None:
															if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			_SCREAMING_SNAKE_CASE				: Any  	=   torch.randn(__snake_case					,      generator=__snake_case					,      device="""cpu"""					,      dtype=__snake_case						).to(
																			    self.device						)
															else:
																			_SCREAMING_SNAKE_CASE				: Dict  	=   torch.randn(__snake_case					,      generator=__snake_case					,      device=self.device					,      dtype=__snake_case						)
											else:
															if latents.shape != latents_shape:
																			raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   latents.to(self.device						)

											# set timesteps
											_SCREAMING_SNAKE_CASE				: Tuple  	=   """offset""" in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   {}
											if accepts_offset:
															_SCREAMING_SNAKE_CASE				: int  	=   1

											self.scheduler.set_timesteps(__snake_case					,      **__snake_case						)

											# Some schedulers like PNDM have timesteps as arrays
											# It's more optimized to move all timesteps to correct device beforehand
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.scheduler.timesteps.to(self.device						)

											# scale the initial noise by the standard deviation required by the scheduler
											_SCREAMING_SNAKE_CASE				: Dict  	=   latents * self.scheduler.init_noise_sigma

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
											# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
											# and should be between [0, 1]
											_SCREAMING_SNAKE_CASE				: str  	=   """eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {}
											if accepts_eta:
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   eta

											# check if the scheduler accepts generator
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """generator""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
											if accepts_generator:
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   generator

											for i, t in enumerate(self.progress_bar(__snake_case						)						):
															# expand the latents if we are doing classifier free guidance
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
															_SCREAMING_SNAKE_CASE				: List[str]  	=   self.scheduler.scale_model_input(__snake_case					,      __snake_case						)

															# predict the noise residual
															_SCREAMING_SNAKE_CASE				: Dict  	=   self.unet(__snake_case					,      __snake_case					,      encoder_hidden_states=__snake_case						).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																			_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   noise_pred.chunk(2						)
																			_SCREAMING_SNAKE_CASE				: Any  	=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																			_SCREAMING_SNAKE_CASE				: int  	=   (
																			    text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
																			)
																			_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   self.cond_fn(
																			    __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      )

															# compute the previous noisy sample x_t -> x_t-1
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.scheduler.step(__snake_case					,      __snake_case					,      __snake_case					,      **__snake_case						).prev_sample

											# scale and decode the image latents with vae
											_SCREAMING_SNAKE_CASE				: Dict  	=   1 / self.vae.config.scaling_factor * latents
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.vae.decode(__snake_case						).sample

											_SCREAMING_SNAKE_CASE				: List[str]  	=   (image / 2 + 0.5).clamp(0					,      1						)
											_SCREAMING_SNAKE_CASE				: str  	=   image.cpu().permute(0					,      2					,      3					,      1						).numpy()

											if output_type == "pil":
															_SCREAMING_SNAKE_CASE				: Dict  	=   self.numpy_to_pil(__snake_case						)

											if not return_dict:
															return (image, None)

											return StableDiffusionPipelineOutput(images=__snake_case					,      nsfw_content_detected=__snake_case						)








'''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     snake_case_     (						SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=None  ):







				"""simple docstring"""





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





@dataclass
class 					lowercase__						:





							'''simple docstring'''


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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args
											_SCREAMING_SNAKE_CASE				: Dict  	=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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






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

															((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: Tuple  	=   (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															_SCREAMING_SNAKE_CASE				: 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:
																							_SCREAMING_SNAKE_CASE				: 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:
																							_SCREAMING_SNAKE_CASE				: Dict  	=   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))				: List[Any]  	=   (
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			_SCREAMING_SNAKE_CASE				: str  	=   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."""

											_SCREAMING_SNAKE_CASE				: str  	=   title_str[:-4]
											_SCREAMING_SNAKE_CASE				: int  	=   """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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   HfArgumentParser(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE				: int  	=   Plot(args=SCREAMING_SNAKE_CASE__  )
				plot.plot()


if __name__ == "__main__":
				main()




