

"""simple docstring"""


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__A						   =			logging.get_logger(__name__)






def     a__    (    __SCREAMING_SNAKE_CASE=None					, __SCREAMING_SNAKE_CASE=None )	->	Tuple:
		return field(default_factory=lambda: default					, metadata=__SCREAMING_SNAKE_CASE )





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      List[str]         =  list_field(
				    default=[],							metadata={
				        """help""": (
				            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
				            """ of all available models"""
				        )
				    },							)

				SCREAMING_SNAKE_CASE_   :      List[int]         =  list_field(
				    default=[8],							metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}  )

				SCREAMING_SNAKE_CASE_   :      List[int]         =  list_field(
				    default=[8, 32, 1_28, 5_12],							metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""},							)

				SCREAMING_SNAKE_CASE_   :      bool         =  field(
				    default=__snake_case,							metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""},							)
				SCREAMING_SNAKE_CASE_   :      bool         =  field(
				    default=__snake_case,							metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""},							)
				SCREAMING_SNAKE_CASE_   :      bool         =  field(
				    default=__snake_case,							metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(default=__snake_case,							metadata={"""help""": """Use FP16 to accelerate inference."""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(default=__snake_case,							metadata={"""help""": """Benchmark training of model"""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(default=__snake_case,							metadata={"""help""": """Verbose memory tracing"""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(
				    default=__snake_case,							metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""},							)
				SCREAMING_SNAKE_CASE_   :      bool         =  field(
				    default=__snake_case,							metadata={
				        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
				    },							)
				SCREAMING_SNAKE_CASE_   :      bool         =  field(default=__snake_case,							metadata={"""help""": """Trace memory line by line"""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(default=__snake_case,							metadata={"""help""": """Save result to a CSV file"""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(default=__snake_case,							metadata={"""help""": """Save all print statements in a log file"""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(default=__snake_case,							metadata={"""help""": """Whether to print environment information"""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(
				    default=__snake_case,							metadata={
				        """help""": (
				            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
				            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
				            """ for debugging / testing and on TPU."""
				        )
				    },							)
				SCREAMING_SNAKE_CASE_   :      str         =  field(
				    default=F"""inference_time_{round(time()  )}.csv""",							metadata={"""help""": """CSV filename used if saving time results to csv."""},							)
				SCREAMING_SNAKE_CASE_   :      str         =  field(
				    default=F"""inference_memory_{round(time()  )}.csv""",							metadata={"""help""": """CSV filename used if saving memory results to csv."""},							)
				SCREAMING_SNAKE_CASE_   :      str         =  field(
				    default=F"""train_time_{round(time()  )}.csv""",							metadata={"""help""": """CSV filename used if saving time results to csv for training."""},							)
				SCREAMING_SNAKE_CASE_   :      str         =  field(
				    default=F"""train_memory_{round(time()  )}.csv""",							metadata={"""help""": """CSV filename used if saving memory results to csv for training."""},							)
				SCREAMING_SNAKE_CASE_   :      str         =  field(
				    default=F"""env_info_{round(time()  )}.csv""",							metadata={"""help""": """CSV filename used if saving environment information."""},							)
				SCREAMING_SNAKE_CASE_   :      str         =  field(
				    default=F"""log_{round(time()  )}.csv""",							metadata={"""help""": """Log filename used if print statements are saved in log."""},							)
				SCREAMING_SNAKE_CASE_   :      int         =  field(default=3,							metadata={"""help""": """Times an experiment will be run."""}  )
				SCREAMING_SNAKE_CASE_   :      bool         =  field(
				    default=__snake_case,							metadata={
				        """help""": (
				            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
				            """ model weights."""
				        )
				    },							)

				def 							lowercase_     (	self							:				str)->   List[str]:



						'''simple docstring'''


						warnings.warn(
						    f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
						    " are deprecated in general and it is advised to use external Benchmarking libraries "
						    " to benchmark Transformer models."     ,  UpperCamelCase__     ,  )

				def 							lowercase_     (	self							:				Dict)->   Union[str, Any]:



						'''simple docstring'''


						return json.dumps(dataclasses.asdict(self)     ,  indent=2)

				@property
				def 							lowercase_     (	self							:				Union[str, Any])->   List[str]:



						'''simple docstring'''


						if len(self.models) <= 0:
								raise ValueError(
								    "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
								    " bert-base-cased` or `args.models = ['bert-base-cased'].")
						return self.models




				@property
				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


						if not self.multi_process:
								return False
						elif self.is_tpu:
								logger.info("Multiprocessing is currently not possible on TPU.")
								return False
						else:
								return True





"""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     snake_case	(				unittest.TestCase  ):

				def 							lowercase_     (	self							:				Optional[int])->   int:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

				@property
				def 							lowercase_     (	self							:				int)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			1
						__lowerCAmelCase:       Union[str, Any]          =			3
						__lowerCAmelCase:       Union[str, Any]          =			(3_2, 3_2)

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

				@property
				def 							lowercase_     (	self							:				Tuple)->   str:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:       Optional[Any]          =			UNetaDConditionModel(
						    block_out_channels=(3_2, 3_2, 6_4)     ,  layers_per_block=2     ,  sample_size=3_2     ,  in_channels=7     ,  out_channels=4     ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")     ,  up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")     ,  cross_attention_dim=3_2     ,  attention_head_dim=8     ,  use_linear_projection=UpperCamelCase__     ,  only_cross_attention=(True, True, False)     ,  num_class_embeds=1_0_0     ,  )
						return model

				@property
				def 							lowercase_     (	self							:				Any)->   Optional[Any]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:       Tuple          =			AutoencoderKL(
						    block_out_channels=[3_2, 3_2, 6_4]     ,  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 							lowercase_     (	self							:				Any)->   Optional[Any]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:       List[str]          =			CLIPTextConfig(
						    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=3_2     ,  intermediate_size=3_7     ,  layer_norm_eps=1e-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=1_0_0_0     ,  hidden_act="gelu"     ,  projection_dim=5_1_2     ,  )
						return CLIPTextModel(UpperCamelCase__)

				def 							lowercase_     (	self							:				List[str])->   Dict:



						'''simple docstring'''


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

						__lowerCAmelCase:       Tuple          =			self.dummy_image.cpu().permute(0     ,  2     ,  3     ,  1)[0]
						__lowerCAmelCase:       List[Any]          =			Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((6_4, 6_4))

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase:       Optional[int]          =			StableDiffusionUpscalePipeline(
						    unet=UpperCamelCase__     ,  low_res_scheduler=UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  vae=UpperCamelCase__     ,  text_encoder=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     ,  max_noise_level=3_5_0     ,  )
						__lowerCAmelCase:       Tuple          =			sd_pipe.to(UpperCamelCase__)
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:       Any          =			"A painting of a squirrel eating a burger"
						__lowerCAmelCase:       str          =			torch.Generator(device=UpperCamelCase__).manual_seed(0)
						__lowerCAmelCase:       Optional[int]          =			sd_pipe(
						    [prompt]     ,  image=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  guidance_scale=6.0     ,  noise_level=2_0     ,  num_inference_steps=2     ,  output_type="np"     ,  )

						__lowerCAmelCase:       List[str]          =			output.images

						__lowerCAmelCase:       Union[str, Any]          =			torch.Generator(device=UpperCamelCase__).manual_seed(0)
						__lowerCAmelCase:       List[str]          =			sd_pipe(
						    [prompt]     ,  image=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  guidance_scale=6.0     ,  noise_level=2_0     ,  num_inference_steps=2     ,  output_type="np"     ,  return_dict=UpperCamelCase__     ,  )[0]

						__lowerCAmelCase:       int          =			image[0, -3:, -3:, -1]
						__lowerCAmelCase:       Dict          =			image_from_tuple[0, -3:, -3:, -1]

						__lowerCAmelCase:       Dict          =			low_res_image.size[0] * 4
						assert image.shape == (1, expected_height_width, expected_height_width, 3)
						__lowerCAmelCase:       List[Any]          =			np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

						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 							lowercase_     (	self							:				List[str])->   Optional[Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:       List[Any]          =			self.dummy_image.cpu().permute(0     ,  2     ,  3     ,  1)[0]
						__lowerCAmelCase:       str          =			Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((6_4, 6_4))

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase:       Optional[int]          =			StableDiffusionUpscalePipeline(
						    unet=UpperCamelCase__     ,  low_res_scheduler=UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  vae=UpperCamelCase__     ,  text_encoder=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     ,  max_noise_level=3_5_0     ,  )
						__lowerCAmelCase:       Optional[int]          =			sd_pipe.to(UpperCamelCase__)
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			"A painting of a squirrel eating a burger"
						__lowerCAmelCase:       List[Any]          =			sd_pipe(
						    2 * [prompt]     ,  image=2 * [low_res_image]     ,  guidance_scale=6.0     ,  noise_level=2_0     ,  num_inference_steps=2     ,  output_type="np"     ,  )
						__lowerCAmelCase:       List[Any]          =			output.images
						assert image.shape[0] == 2

						__lowerCAmelCase:       Dict          =			torch.Generator(device=UpperCamelCase__).manual_seed(0)
						__lowerCAmelCase:       Optional[Any]          =			sd_pipe(
						    [prompt]     ,  image=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  num_images_per_prompt=2     ,  guidance_scale=6.0     ,  noise_level=2_0     ,  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 							lowercase_     (	self							:				Tuple)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			self.dummy_cond_unet_upscale
						__lowerCAmelCase:       int          =			DDPMScheduler()
						__lowerCAmelCase:       int          =			DDIMScheduler(prediction_type="v_prediction")
						__lowerCAmelCase:       Dict          =			self.dummy_vae
						__lowerCAmelCase:       int          =			self.dummy_text_encoder
						__lowerCAmelCase:       List[Any]          =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__lowerCAmelCase:       List[Any]          =			self.dummy_image.cpu().permute(0     ,  2     ,  3     ,  1)[0]
						__lowerCAmelCase:       Optional[int]          =			Image.fromarray(np.uinta(UpperCamelCase__)).convert("RGB").resize((6_4, 6_4))

						# put models in fp16, except vae as it overflows in fp16
						__lowerCAmelCase:       List[Any]          =			unet.half()
						__lowerCAmelCase:       List[str]          =			text_encoder.half()

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase:       List[Any]          =			StableDiffusionUpscalePipeline(
						    unet=UpperCamelCase__     ,  low_res_scheduler=UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  vae=UpperCamelCase__     ,  text_encoder=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     ,  max_noise_level=3_5_0     ,  )
						__lowerCAmelCase:       str          =			sd_pipe.to(UpperCamelCase__)
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__)

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

						__lowerCAmelCase:       Optional[Any]          =			low_res_image.size[0] * 4
						assert image.shape == (1, expected_height_width, expected_height_width, 3)





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

				def 							lowercase_     (	self							:				Tuple)->   Tuple:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

				def 							lowercase_     (	self							:				List[Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-upscale/low_res_cat.png")
						__lowerCAmelCase:       Optional[Any]          =			load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
						    "/upsampled_cat.npy")

						__lowerCAmelCase:       str          =			"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:       Tuple          =			"a cat sitting on a park bench"

						__lowerCAmelCase:       int          =			torch.manual_seed(0)
						__lowerCAmelCase:       List[Any]          =			pipe(
						    prompt=UpperCamelCase__     ,  image=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  output_type="np"     ,  )
						__lowerCAmelCase:       Dict          =			output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image).max() < 1e-3

				def 							lowercase_     (	self							:				Optional[int])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-upscale/low_res_cat.png")
						__lowerCAmelCase:       Tuple          =			load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
						    "/upsampled_cat_fp16.npy")

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

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

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

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image).max() < 5e-1




				def 							lowercase_     (	self							:				Optional[int])->   Dict:



						'''simple docstring'''


						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:       Union[str, Any]          =			"stabilityai/stable-diffusion-x4-upscaler"
						__lowerCAmelCase:       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:       int          =			"a cat sitting on a park bench"

						__lowerCAmelCase:       Dict          =			torch.manual_seed(0)
						__lowerCAmelCase:       Dict          =			pipe(
						    prompt=UpperCamelCase__     ,  image=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  num_inference_steps=5     ,  output_type="np"     ,  )

						__lowerCAmelCase:       Optional[int]          =			torch.cuda.max_memory_allocated()
						# make sure that less than 2.9 GB is allocated
						assert mem_bytes < 2.9 * 1_0**9


