






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class _lowercase     (    unittest.TestCase		):




							def 		lowerCamelCase_    (							self:    int					):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     int	  =	1
														lowerCamelCase__ :     Union[str, Any]	  =	3
														lowerCamelCase__ :     Tuple	  =	(32, 32)

														lowerCamelCase__ :     List[str]	  =	floats_tensor((batch_size, num_channels) + sizes      ,							rng=random.Random(0					)					).to(UpperCamelCase__					)
														return image




							@property
							def 		lowerCamelCase_    (							self:    int					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Dict	  =	UNetaDConditionModel(
														    block_out_channels=(32, 32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=7      ,							out_channels=4      ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")      ,							up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")      ,							cross_attention_dim=32      ,							attention_head_dim=8      ,							use_linear_projection=UpperCamelCase__      ,							only_cross_attention=(True, True, False)      ,							num_class_embeds=100      ,							)
														return model




							@property
							def 		lowerCamelCase_    (							self:    Tuple					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Optional[Any]	  =	AutoencoderKL(
														    block_out_channels=[32, 32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,							latent_channels=4      ,							)
														return model




							@property
							def 		lowerCamelCase_    (							self:    List[Any]					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Union[str, Any]	  =	CLIPTextConfig(
														    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1_000      ,							hidden_act="""gelu"""      ,							projection_dim=512      ,							)
														return CLIPTextModel(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Any	  =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__ :     Dict	  =	self.dummy_cond_unet_upscale
														lowerCamelCase__ :     Dict	  =	DDPMScheduler()
														lowerCamelCase__ :     Optional[int]	  =	DDIMScheduler(prediction_type="""v_prediction"""					)
														lowerCamelCase__ :     int	  =	self.dummy_vae
														lowerCamelCase__ :     int	  =	self.dummy_text_encoder
														lowerCamelCase__ :     Union[str, Any]	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

														lowerCamelCase__ :     Dict	  =	self.dummy_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
														lowerCamelCase__ :     Optional[int]	  =	Image.fromarray(np.uinta(UpperCamelCase__					)					).convert("""RGB"""					).resize((64, 64)					)

														# make sure here that pndm scheduler skips prk
														lowerCamelCase__ :     Tuple	  =	StableDiffusionUpscalePipeline(
														    unet=UpperCamelCase__      ,							low_res_scheduler=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							max_noise_level=350      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	sd_pipe.to(UpperCamelCase__					)
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     Any	  =	"""A painting of a squirrel eating a burger"""
														lowerCamelCase__ :     Any	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
														lowerCamelCase__ :     Any	  =	sd_pipe(
														    [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							)

														lowerCamelCase__ :     Optional[int]	  =	output.images

														lowerCamelCase__ :     Tuple	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
														lowerCamelCase__ :     Dict	  =	sd_pipe(
														    [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							return_dict=UpperCamelCase__      ,							)[0]

														lowerCamelCase__ :     List[Any]	  =	image[0, -3:, -3:, -1]
														lowerCamelCase__ :     str	  =	image_from_tuple[0, -3:, -3:, -1]

														lowerCamelCase__ :     Optional[int]	  =	low_res_image.size[0] * 4
														assert image.shape == (1, expected_height_width, expected_height_width, 3)
														lowerCamelCase__ :     str	  =	np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]					)

														assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Dict	  =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_cond_unet_upscale
														lowerCamelCase__ :     str	  =	DDPMScheduler()
														lowerCamelCase__ :     List[str]	  =	DDIMScheduler(prediction_type="""v_prediction"""					)
														lowerCamelCase__ :     Optional[Any]	  =	self.dummy_vae
														lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_text_encoder
														lowerCamelCase__ :     Any	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

														lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
														lowerCamelCase__ :     int	  =	Image.fromarray(np.uinta(UpperCamelCase__					)					).convert("""RGB"""					).resize((64, 64)					)

														# make sure here that pndm scheduler skips prk
														lowerCamelCase__ :     Union[str, Any]	  =	StableDiffusionUpscalePipeline(
														    unet=UpperCamelCase__      ,							low_res_scheduler=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							max_noise_level=350      ,							)
														lowerCamelCase__ :     int	  =	sd_pipe.to(UpperCamelCase__					)
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     Any	  =	"""A painting of a squirrel eating a burger"""
														lowerCamelCase__ :     Optional[int]	  =	sd_pipe(
														    2 * [prompt]      ,							image=2 * [low_res_image]      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     Optional[Any]	  =	output.images
														assert image.shape[0] == 2

														lowerCamelCase__ :     Tuple	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
														lowerCamelCase__ :     Tuple	  =	sd_pipe(
														    [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_images_per_prompt=2      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     List[Any]	  =	output.images
														assert image.shape[0] == 2




							@unittest.skipIf(torch_device != """cuda"""      ,							"""This test requires a GPU"""					)
							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Any	  =	self.dummy_cond_unet_upscale
														lowerCamelCase__ :     int	  =	DDPMScheduler()
														lowerCamelCase__ :     str	  =	DDIMScheduler(prediction_type="""v_prediction"""					)
														lowerCamelCase__ :     List[Any]	  =	self.dummy_vae
														lowerCamelCase__ :     List[Any]	  =	self.dummy_text_encoder
														lowerCamelCase__ :     Dict	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

														lowerCamelCase__ :     Optional[Any]	  =	self.dummy_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
														lowerCamelCase__ :     List[Any]	  =	Image.fromarray(np.uinta(UpperCamelCase__					)					).convert("""RGB"""					).resize((64, 64)					)

														# put models in fp16, except vae as it overflows in fp16
														lowerCamelCase__ :     Optional[int]	  =	unet.half()
														lowerCamelCase__ :     int	  =	text_encoder.half()

														# make sure here that pndm scheduler skips prk
														lowerCamelCase__ :     List[str]	  =	StableDiffusionUpscalePipeline(
														    unet=UpperCamelCase__      ,							low_res_scheduler=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							max_noise_level=350      ,							)
														lowerCamelCase__ :     Any	  =	sd_pipe.to(UpperCamelCase__					)
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     List[str]	  =	"""A painting of a squirrel eating a burger"""
														lowerCamelCase__ :     Dict	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Any	  =	sd_pipe(
														    [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=2      ,							output_type="""np"""      ,							).images

														lowerCamelCase__ :     Dict	  =	low_res_image.size[0] * 4
														assert image.shape == (1, expected_height_width, expected_height_width, 3)



@slow
@require_torch_gpu
class _lowercase     (    unittest.TestCase		):




							def 		lowerCamelCase_    (							self:    Tuple					):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Union[str, Any]	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/sd2-upscale/low_res_cat.png"""					)
														lowerCamelCase__ :     Optional[int]	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
														    """/upsampled_cat.npy"""					)

														lowerCamelCase__ :     List[Any]	  =	"""stabilityai/stable-diffusion-x4-upscaler"""
														lowerCamelCase__ :     Optional[int]	  =	StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase__					)
														pipe.to(UpperCamelCase__					)
														pipe.set_progress_bar_config(disable=UpperCamelCase__					)
														pipe.enable_attention_slicing()

														lowerCamelCase__ :     Union[str, Any]	  =	"""a cat sitting on a park bench"""

														lowerCamelCase__ :     Optional[int]	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Any	  =	pipe(
														    prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     str	  =	output.images[0]

														assert image.shape == (512, 512, 3)
														assert np.abs(expected_image - image					).max() < 1e-3




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     List[Any]	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/sd2-upscale/low_res_cat.png"""					)
														lowerCamelCase__ :     Any	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
														    """/upsampled_cat_fp16.npy"""					)

														lowerCamelCase__ :     Union[str, Any]	  =	"""stabilityai/stable-diffusion-x4-upscaler"""
														lowerCamelCase__ :     List[Any]	  =	StableDiffusionUpscalePipeline.from_pretrained(
														    UpperCamelCase__      ,							torch_dtype=torch.floataa      ,							)
														pipe.to(UpperCamelCase__					)
														pipe.set_progress_bar_config(disable=UpperCamelCase__					)
														pipe.enable_attention_slicing()

														lowerCamelCase__ :     Optional[int]	  =	"""a cat sitting on a park bench"""

														lowerCamelCase__ :     Tuple	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Tuple	  =	pipe(
														    prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     int	  =	output.images[0]

														assert image.shape == (512, 512, 3)
														assert np.abs(expected_image - image					).max() < 5e-1




							def 		lowerCamelCase_    (							self:    int					):
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														lowerCamelCase__ :     Tuple	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/sd2-upscale/low_res_cat.png"""					)

														lowerCamelCase__ :     Optional[int]	  =	"""stabilityai/stable-diffusion-x4-upscaler"""
														lowerCamelCase__ :     List[Any]	  =	StableDiffusionUpscalePipeline.from_pretrained(
														    UpperCamelCase__      ,							torch_dtype=torch.floataa      ,							)
														pipe.to(UpperCamelCase__					)
														pipe.set_progress_bar_config(disable=UpperCamelCase__					)
														pipe.enable_attention_slicing(1					)
														pipe.enable_sequential_cpu_offload()

														lowerCamelCase__ :     str	  =	"""a cat sitting on a park bench"""

														lowerCamelCase__ :     str	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Union[str, Any]	  =	pipe(
														    prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=5      ,							output_type="""np"""      ,							)

														lowerCamelCase__ :     int	  =	torch.cuda.max_memory_allocated()
														# make sure that less than 2.9 GB is allocated
														assert mem_bytes < 2.9 * 10**9









'''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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

@dataclass
class _lowercase     :
							a    				=	field(
							    metadata={"""help""": """The csv file to plot."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Disable logarithmic scale when plotting"""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}    ,				)
							a    				=	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				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	args
														lowerCamelCase__ :     Optional[int]	  =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

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




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	plt.subplots()
														lowerCamelCase__ :     Any	  =	"""Time usage""" if self.args.is_time else """Memory usage"""
														lowerCamelCase__ :     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()					):
																					lowerCamelCase__ :     Any	  =	sorted(set(self.result_dict[model_name]["""bsz"""]					)					)
																					lowerCamelCase__ :     int	  =	sorted(set(self.result_dict[model_name]["""seq_len"""]					)					)
																					lowerCamelCase__ :     Any	  =	self.result_dict[model_name]["""result"""]

																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Dict	  =	(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

																					lowerCamelCase__ :     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:
																																			lowerCamelCase__ :     int	  =	np.asarray(
																																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=UpperCamelCase__      ,							)
																												else:
																																			lowerCamelCase__ :     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      ,							)

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

																												lowerCamelCase__ :     int	  =	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.'''

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""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 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

