





import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


a		:				Any  			=    getLogger(__name__)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]					, _lowercase		:					str					, _lowercase		:					str					, _lowercase		:					int = 8					, _lowercase		:					int = 1024					, _lowercase		:					List[str]="val"					, _lowercase		:					Union[str, Any]=None					, _lowercase		:					str=False					, _lowercase		:					Optional[Any]="summarization"					, _lowercase		:					int=None					, _lowercase		:					Any=1					, _lowercase		:					Dict = None					, _lowercase		:					int=""					, **_lowercase		:					str					, )				->Dict:






	'''simple docstring'''






	a   :	Optional[Any]			=       str(_lowercase      )
	assert local_rank is not None
	torch.distributed.init_process_group(backend="nccl"					, rank=_lowercase      )

	a   :	str			=       Path(_lowercase      )
	a   :	int			=       save_dir.joinpath(F"""rank_{local_rank}_output.json"""      )
	torch.cuda.set_device(_lowercase      )
	a   :	Optional[Any]			=       AutoModelForSeqaSeqLM.from_pretrained(_lowercase      ).cuda()
	if fpaa:
		a   :	Any			=       model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(_lowercase					, _lowercase      )  # update config with task specific params
	a   :	Optional[int]			=       generate_kwargs.pop("num_beams"					, model.config.num_beams      )  # AttributeError risk?
	if num_return_sequences > num_beams:
		a   :	Union[str, Any]			=       num_return_sequences

	a   :	List[Any]			=       AutoTokenizer.from_pretrained(_lowercase      )
	logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""      )  # if this is wrong, check config.model_type.

	if max_source_length is None:
		a   :	Union[str, Any]			=       tokenizer.model_max_length
	if prefix is None:
		a   :	Optional[Any]			=       prefix or getattr(model.config					, "prefix"					, ""      ) or ""
	a   :	List[str]			=       SeqaSeqDataset(
	    _lowercase					, _lowercase					, _lowercase					, max_target_length=1024					, type_path=_lowercase					, n_obs=_lowercase					, prefix=_lowercase					, **_lowercase					, )
	# I set shuffle=True for a more accurate progress bar.
	# If all the longest samples are first, the prog bar estimate is too high at the beginning.
	a   :	Dict			=       ds.make_sortish_sampler(_lowercase					, distributed=_lowercase					, add_extra_examples=_lowercase					, shuffle=_lowercase      )
	a   :	Optional[int]			=       DataLoader(_lowercase					, sampler=_lowercase					, batch_size=_lowercase					, collate_fn=ds.collate_fn      )
	a   :	List[Any]			=       []
	for batch in tqdm(_lowercase      ):
		a   :	List[str]			=       model.generate(
		    input_ids=batch["input_ids"].to(model.device      )					, attention_mask=batch["attention_mask"].to(model.device      )					, num_return_sequences=_lowercase					, num_beams=_lowercase					, **_lowercase					, )
		a   :	Optional[Any]			=       tokenizer.batch_decode(_lowercase					, skip_special_tokens=_lowercase					, clean_up_tokenization_spaces=_lowercase      )
		a   :	Tuple			=       batch["ids"]
		if num_return_sequences > 1:
			a   :	Union[str, Any]			=       chunks(_lowercase					, _lowercase      )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_lowercase      ):
			results.append({"pred": pred, "id": ids[i].item()}      )
	save_json(_lowercase					, _lowercase      )
	return results, sampler.num_replicas






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	List[str]			=       argparse.ArgumentParser(
	    epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"      )
	parser.add_argument("--data_dir"					, type=_lowercase					, help="like cnn_dm/test.source"      )
	parser.add_argument(
	    "--model_name"					, type=_lowercase					, help="like facebook/bart-large-cnn,t5-base, etc."					, default="sshleifer/distilbart-xsum-12-3"					, )
	parser.add_argument("--save_dir"					, type=_lowercase					, help="where to save"					, default="tmp_gen"      )
	parser.add_argument("--max_source_length"					, type=_lowercase					, default=_lowercase      )
	parser.add_argument(
	    "--type_path"					, type=_lowercase					, default="test"					, help="which subset to evaluate typically train/val/test"      )
	parser.add_argument("--task"					, type=_lowercase					, default="summarization"					, help="used for task_specific_params + metrics"      )
	parser.add_argument("--bs"					, type=_lowercase					, default=8					, required=_lowercase					, help="batch size"      )
	parser.add_argument(
	    "--local_rank"					, type=_lowercase					, default=-1					, required=_lowercase					, help="should be passed by distributed.launch"      )

	parser.add_argument(
	    "--n_obs"					, type=_lowercase					, default=_lowercase					, required=_lowercase					, help="How many observations. Defaults to all."      )
	parser.add_argument(
	    "--num_return_sequences"					, type=_lowercase					, default=1					, required=_lowercase					, help="How many sequences to return"      )
	parser.add_argument(
	    "--sync_timeout"					, type=_lowercase					, default=600					, required=_lowercase					, help="How long should master process wait for other processes to finish."					, )
	parser.add_argument("--src_lang"					, type=_lowercase					, default=_lowercase					, required=_lowercase      )
	parser.add_argument("--tgt_lang"					, type=_lowercase					, default=_lowercase					, required=_lowercase      )
	parser.add_argument(
	    "--prefix"					, type=_lowercase					, required=_lowercase					, default=_lowercase					, help="will be added to the begininng of src examples"      )
	parser.add_argument("--fp16"					, action="store_true"      )
	parser.add_argument("--debug"					, action="store_true"      )
	a   :	str			=       time.time()
	a   :	Any			=       parser.parse_known_args()
	a   :	Tuple			=       parse_numeric_n_bool_cl_kwargs(_lowercase      )
	if generate_kwargs and args.local_rank <= 0:
		print(F"""parsed the following generate kwargs: {generate_kwargs}"""      )
	a   :	str			=       Path(args.save_dir + "_tmp"      )
	Path(_lowercase      ).mkdir(exist_ok=_lowercase      )  # this handles locking.
	a   :	List[Any]			=       list(json_save_dir.glob("rank_*.json"      )      )
	if intermediate_files:
		raise ValueError(F"""Found files at {json_save_dir} please move or remove them."""      )
		# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
	a   :	str			=       {}
	if args.src_lang is not None:
		a   :	List[Any]			=       args.src_lang
	if args.tgt_lang is not None:
		a   :	Dict			=       args.tgt_lang

	Path(args.save_dir      ).mkdir(exist_ok=_lowercase      )
	a   :	Any			=       eval_data_dir(
	    args.data_dir					, _lowercase					, args.model_name					, type_path=args.type_path					, bs=args.bs					, fpaa=args.fpaa					, task=args.task					, local_rank=args.local_rank					, n_obs=args.n_obs					, max_source_length=args.max_source_length					, num_return_sequences=args.num_return_sequences					, prefix=args.prefix					, dataset_kwargs=_lowercase					, **_lowercase					, )

	if args.local_rank <= 0:
		a   :	List[Any]			=       Path(args.save_dir      )
		save_dir.mkdir(exist_ok=_lowercase      )
		a   :	Union[str, Any]			=       gather_results_from_each_node(_lowercase					, _lowercase					, args.sync_timeout      )
		a   :	str			=       combine_partial_results(_lowercase      )
		if args.num_return_sequences > 1:
			a   :	int			=       save_dir.joinpath("pseudolabel_results.json"      )
			print(F"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""      )
			save_json(_lowercase					, _lowercase      )
			return
		a   :	Tuple			=       Path(args.data_dir      ).joinpath(args.type_path + ".target"      )
		with open(_lowercase      ) as f:
			a   :	str			=       [x.rstrip() for x in f.readlines()][: len(_lowercase      )]

		# Calculate metrics, save metrics,  and save _generations.txt
		a   :	Any			=       "translation" in args.task
		a   :	Tuple			=       calculate_bleu if calc_bleu else calculate_rouge
		a   :	Tuple			=       "bleu" if calc_bleu else "rouge"
		a   :	Dict			=       score_fn(_lowercase					, _lowercase      )
		a   :	Dict			=       len(_lowercase      )
		a   :	Optional[int]			=       time.time() - start_time
		a   :	Tuple			=       round(runtime / metrics["n_obs"]					, 4      )
		a   :	Optional[Any]			=       num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		a   :	Any			=       save_dir.joinpath(F"""{args.type_path}_{metric_name}.json"""      )
		save_json(_lowercase					, _lowercase					, indent=_lowercase      )
		print(_lowercase      )
		write_txt_file(_lowercase					, save_dir.joinpath(F"""{args.type_path}_generations.txt"""      )      )
		if args.debug:
			write_txt_file(_lowercase					, save_dir.joinpath(F"""{args.type_path}.target"""      )      )
		else:
			shutil.rmtree(_lowercase      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->List:






	'''simple docstring'''






	a   :	List[str]			=       []
	for partial_result in partial_results:
		records.extend(_lowercase      )
	a   :	str			=       sorted(_lowercase					, key=lambda _lowercase      : x["id"]      )
	a   :	List[Any]			=       [x["pred"] for x in records]
	return preds






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[int]					, _lowercase		:					str      )				->List[Dict[str, List]]:






	'''simple docstring'''






	a   :	Optional[Any]			=       time.time()
	logger.info("waiting for all nodes to finish"      )
	a   :	str			=       None
	while (time.time() - start_wait) < timeout:
		a   :	int			=       list(save_dir.glob("rank_*.json"      )      )
		if len(_lowercase      ) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			a   :	Tuple			=       lmap(_lowercase					, _lowercase      )
			return json_data
		except JSONDecodeError:
			continue
	else:
		raise TimeoutError("Rank 0 gave up on waiting for other processes"      )
	# Unreachable


if __name__ == "__main__":
		# Usage for MT:
		run_generate()









"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      Dict         =IFPipeline
							lowerCamelCase     :      int         =TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
							lowerCamelCase     :      int         =TEXT_TO_IMAGE_BATCH_PARAMS
							lowerCamelCase     :      int         =PipelineTesterMixin.required_optional_params - {"""latents"""}






							def 					__a     (     self   )			->				List[str]:
								return self._get_dummy_components()






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0   )			->				Dict:
								if str(lowerCAmelCase__   ).startswith("mps"   ):
									a   :	Tuple			=       torch.manual_seed(lowerCAmelCase__   )
								else:
									a   :	int			=       torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )

								a   :	Optional[Any]			=       {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}

								return inputs






							def 					__a     (     self   )			->				Union[str, Any]:
								self._test_save_load_optional_components()






							@unittest.skipIf(torch_device != "cuda"   ,      reason="float16 requires CUDA"   )
							def 					__a     (     self   )			->				Any:
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1   )






							def 					__a     (     self   )			->				Union[str, Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )






							def 					__a     (     self   )			->				Optional[int]:
								self._test_save_load_local()






							def 					__a     (     self   )			->				Tuple:
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2   ,      )






							@unittest.skipIf(
							    torch_device != "cuda" or not is_xformers_available()   ,      reason="XFormers attention is only available with CUDA and `xformers` installed"   ,      )
							def 					__a     (     self   )			->				str:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )


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






							def 					__a     (     self   )			->				Optional[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def 					__a     (     self   )			->				Tuple:
								# if

								a   :	Tuple			=       IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   )

								a   :	str			=       IFSuperResolutionPipeline.from_pretrained(
								    "DeepFloyd/IF-II-L-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   ,      text_encoder=lowerCAmelCase__   ,      tokenizer=lowerCAmelCase__   )

								# pre compute text embeddings and remove T5 to save memory

								pipe_a.text_encoder.to("cuda"   )

								a, a   :	List[str]			=       pipe_a.encode_prompt("anime turtle"   ,      device="cuda"   )

								del pipe_a.tokenizer
								del pipe_a.text_encoder
								gc.collect()

								a   :	Optional[int]			=       None
								a   :	Optional[int]			=       None

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

								self._test_if(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# img2img

								a   :	Union[str, Any]			=       IFImgaImgPipeline(**pipe_a.components   )
								a   :	List[Any]			=       IFImgaImgSuperResolutionPipeline(**pipe_a.components   )

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

								self._test_if_imgaimg(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# inpainting

								a   :	Union[str, Any]			=       IFInpaintingPipeline(**pipe_a.components   )
								a   :	List[str]			=       IFInpaintingSuperResolutionPipeline(**pipe_a.components   )

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

								self._test_if_inpainting(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
								a   :	Dict			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	List[str]			=       output.images[0]

								assert image.shape == (64, 64, 3)

								a   :	Dict			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 13 * 10**9

								a   :	Optional[Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Union[str, Any]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	List[str]			=       output.images[0]

								assert image.shape == (256, 256, 3)

								a   :	int			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	Union[str, Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				int:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	int			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Tuple			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	List[Any]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	Tuple			=       output.images[0]

								assert image.shape == (64, 64, 3)

								a   :	int			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								a   :	Optional[int]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	List[Any]			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	str			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Dict			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	int			=       output.images[0]

								assert image.shape == (256, 256, 3)

								a   :	str			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	Any			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	Union[str, Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	List[Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

								a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
								a   :	List[str]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	List[Any]			=       output.images[0]

								assert image.shape == (64, 64, 3)

								a   :	Tuple			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								a   :	Union[str, Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	str			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	int			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	Dict			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

								a   :	Optional[int]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	List[str]			=       output.images[0]

								assert image.shape == (256, 256, 3)

								a   :	Tuple			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	Optional[Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






def    _SCREAMING_SNAKE_CASE	(			)				->List[str]:






	'''simple docstring'''






	torch.cuda.empty_cache()
	torch.cuda.reset_max_memory_allocated()
	torch.cuda.reset_peak_memory_stats()


