code: 
  
  
  
  
  """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__     (		lowercase__  :     Any=None       ,     lowercase__  :     Union[str, Any]=None   ):
  		return field(default_factory=lambda: default       ,     metadata=lowercase__   )
  @dataclass
  class 			lowerCamelCase__    :
  			a__ :      str								=     field(
  			    metadata={"""help""": """The csv file to plot."""}				,  )
  			a__ :      bool								=     field(
  			    default=lowerCamelCase_				,  metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}				,  )
  			a__ :      bool								=     field(
  			    default=lowerCamelCase_				,  metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}				,  )
  			a__ :      bool								=     field(
  			    default=lowerCamelCase_				,  metadata={"""help""": """Disable logarithmic scale when plotting"""}				,  )
  			a__ :      bool								=     field(
  			    default=lowerCamelCase_				,  metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }				,  )
  			a__ :      Optional[str]								=     field(
  			    default=lowerCamelCase_				,  metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}				,  )
  			a__ :      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   UpperCamelCase__     (		lowercase__  :     int   ):
  		try:
  				int(lowercase__   )
  				return True
  		except ValueError:
  				return False
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     str   ):
  		try:
  				float(lowercase__   )
  				return True
  		except ValueError:
  				return False
  class 			lowerCamelCase__    :
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				args
  					snake_case		:						Optional[int]								=				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)
  
  					with open(self.args.csv_file				, newline=""							) as csv_file:
  							snake_case		:						str								=				csv.DictReader(SCREAMING_SNAKE_CASE							)
  							for row in reader:
  									snake_case		:						List[str]								=				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
  											snake_case		:						List[str]								=				int(row["result"]							)
  									elif can_convert_to_float(row["result"]							):
  											# value is not None
  											snake_case		:						Optional[Any]								=				float(row["result"]							)
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case			,				snake_case		:						Optional[Any]								=				plt.subplots()
  					snake_case		:						List[str]								=				"Time usage" if self.args.is_time else "Memory usage"
  					snake_case		:						List[str]								=				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()							):
  							snake_case		:						Dict								=				sorted(set(self.result_dict[model_name]["bsz"]							)							)
  							snake_case		:						List[Any]								=				sorted(set(self.result_dict[model_name]["seq_len"]							)							)
  							snake_case		:						Tuple								=				self.result_dict[model_name]["result"]
  
  							((snake_case)			,				(snake_case))		:						List[Any]								=				(
  							    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  							)
  
  							snake_case		:						int								=				(
  							    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:
  											snake_case		:						Optional[Any]								=				np.asarray(
  											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				, dtype=SCREAMING_SNAKE_CASE				, )
  									else:
  											snake_case		:						List[Any]								=				np.asarray(
  											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				, dtype=np.floataa				, )
  
  									((snake_case)			,				(snake_case))		:						Any								=				(
  									    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
  									)
  
  									snake_case		:						Union[str, Any]								=				np.asarray(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)[: len(SCREAMING_SNAKE_CASE							)]
  									plt.scatter(
  									    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''							)
  									plt.plot(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, "--"							)
  
  							title_str += F''' {label_model_name} vs.'''
  
  					snake_case		:						Dict								=				title_str[:-4]
  					snake_case		:						Optional[int]								=				"Time in s" if self.args.is_time else "Memory in MB"
  
  					# plot
  					plt.title(SCREAMING_SNAKE_CASE							)
  					plt.xlabel(SCREAMING_SNAKE_CASE							)
  					plt.ylabel(SCREAMING_SNAKE_CASE							)
  					plt.legend()
  
  					if self.args.figure_png_file is not None:
  							plt.savefig(self.args.figure_png_file							)
  					else:
  							plt.show()
  
  
  
  
  
  
  
  def   UpperCamelCase__     (		):
  		snake_case		:						Dict								=				HfArgumentParser(lowercase__   )
  		snake_case		:						Tuple								=				parser.parse_args_into_dataclasses()[0]
  		snake_case		:						Union[str, Any]								=				Plot(args=lowercase__   )
  		plot.plot()
  
  
  if __name__ == "__main__":
  							main()
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      load_image,
      load_numpy,
      nightly,
      require_torch_gpu,
      skip_mps,
      torch_device,
  )
  
  from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
  			a__ :      Optional[Any]								=     RePaintPipeline
  			a__ :      List[Any]								=     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  			a__ :      List[Any]								=     PipelineTesterMixin.required_optional_params - {
  			    """latents""",
  			    """num_images_per_prompt""",
  			    """callback""",
  			    """callback_steps""",
  			}
  			a__ :      List[Any]								=     IMAGE_INPAINTING_BATCH_PARAMS
  			a__ :      List[str]								=     False
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					torch.manual_seed(0							)
  					torch.manual_seed(0							)
  					snake_case		:						Union[str, Any]								=				UNetaDModel(
  					    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=3				, out_channels=3				, down_block_types=("DownBlock2D", "AttnDownBlock2D")				, up_block_types=("AttnUpBlock2D", "UpBlock2D")				, )
  					snake_case		:						Union[str, Any]								=				RePaintScheduler()
  					snake_case		:						Optional[Any]								=				{"unet": unet, "scheduler": scheduler}
  					return components
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
  							snake_case		:						str								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
  					else:
  							snake_case		:						List[Any]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Any								=				np.random.RandomState(SCREAMING_SNAKE_CASE							).standard_normal((1, 3, 32, 32)							)
  					snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						List[str]								=				(image > 0).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						int								=				{
  					    "image": image,
  					    "mask_image": mask,
  					    "generator": generator,
  					    "num_inference_steps": 5,
  					    "eta": 0.0,
  					    "jump_length": 2,
  					    "jump_n_sample": 2,
  					    "output_type": "numpy",
  					}
  					return inputs
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				"cpu"  # ensure determinism for the device-dependent torch.Generator
  					snake_case		:						Any								=				self.get_dummy_components()
  					snake_case		:						Optional[int]								=				RePaintPipeline(**SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				sd_pipe.to(SCREAMING_SNAKE_CASE							)
  					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Dict								=				sd_pipe(**SCREAMING_SNAKE_CASE							).images
  					snake_case		:						Optional[int]								=				image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 32, 32, 3)
  					snake_case		:						List[str]								=				np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]							)
  
  					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_local()
  
  
  			@unittest.skip("non-deterministic pipeline"							)
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_inference_batch_single_identical()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_dict_tuple_outputs_equivalent()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_optional_components()
  
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_attention_slicing_forward_pass()
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256.png"							)
  					snake_case		:						Any								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"							)
  					snake_case		:						List[Any]								=				load_numpy(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256_result.npy"							)
  
  					snake_case		:						str								=				"google/ddpm-ema-celebahq-256"
  					snake_case		:						Optional[Any]								=				UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				RePaintPipeline(unet=SCREAMING_SNAKE_CASE				, scheduler=SCREAMING_SNAKE_CASE							).to(SCREAMING_SNAKE_CASE							)
  					repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  					repaint.enable_attention_slicing()
  
  					snake_case		:						Union[str, Any]								=				torch.manual_seed(0							)
  					snake_case		:						int								=				repaint(
  					    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, num_inference_steps=250				, eta=0.0				, jump_length=10				, jump_n_sample=10				, generator=SCREAMING_SNAKE_CASE				, output_type="np"				, )
  					snake_case		:						List[Any]								=				output.images[0]
  
  					assert image.shape == (256, 256, 3)
  					assert np.abs(expected_image - image							).mean() < 1E-2
  
  