
"""simple docstring"""




import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_A     :			Tuple  						=			logging.getLogger(__name__)

_A     :			Any  						=			"""pytorch_model.bin"""

@dataclasses.dataclass
class 						a__		:
   __lowerCAmelCase				=							dataclasses.field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}		)
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""},     )


@dataclasses.dataclass
class 						a__		:
   __lowerCAmelCase				=							dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}		)
   __lowerCAmelCase				=							dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}		)
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """A csv or a json file containing the validation data."""}		)
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """The name of the task to train on."""},     )
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """The list of labels for the task."""}		)


@dataclasses.dataclass
class 						a__		:
   __lowerCAmelCase				=							dataclasses.field(
       metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}		)
   __lowerCAmelCase				=							dataclasses.field(
       default="""accuracy""",     metadata={"""help""": """The evaluation metric used for the task."""}		)
   __lowerCAmelCase				=							dataclasses.field(
       default="""no""",     metadata={
           """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
       },     )
   __lowerCAmelCase				=							dataclasses.field(
       default=10,     metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""},     )
   __lowerCAmelCase				=							dataclasses.field(
       default=0.0,     metadata={
           """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
       },     )
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""},     )
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""},     )
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""},     )
   __lowerCAmelCase				=							dataclasses.field(
       default=0.0,     metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""},     )
   __lowerCAmelCase				=							dataclasses.field(
       default=100,     metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""},     )
   __lowerCAmelCase				=							dataclasses.field(
       default=a_,     metadata={"""help""": """Random seed for initialization."""},     )


def     __magic_name__      ( __snake_case							:			Optional[int]	,					__snake_case							:			Tuple	,					__snake_case							:			List[Any]	,					__snake_case							:			List[str]	,					__snake_case							:			Tuple	,					__snake_case							:			Union[str, Any]    )					->							int:
       lowercase       :							Any									=				datasets.concatenate_datasets([infer_input, infer_output]	,					axis=1    )

       if args.do_filter_by_confidence:
              lowercase       :							List[Any]									=				dataset.filter(lambda __snake_case    : example["probability"] > args.confidence_threshold    )

       if args.do_filter_by_val_performance:
              assert eval_result >= 0.0 and eval_result <= 1.0
              lowercase       :							Union[str, Any]									=				int(eval_result * len(__snake_case    )    )
              print(__snake_case    )
              lowercase       :							Dict									=				dataset.sort("probability"	,					reverse=__snake_case    )
              lowercase       :							Dict									=				dataset.select(range(__snake_case    )    )

       lowercase       :							Any									=				dataset.remove_columns(["label", "probability"]    )
       lowercase       :							Any									=				dataset.rename_column("prediction"	,					"label"    )
       lowercase       :							Union[str, Any]									=				dataset.map(lambda __snake_case    : {"label": idalabel[example["label"]]}    )
       lowercase       :							Dict									=				dataset.shuffle(seed=args.seed    )

       lowercase       :							Optional[int]									=				os.path.join(__snake_case	,					f"""train_pseudo.{args.data_file_extension}"""    )
       if args.data_file_extension == "csv":
              dataset.to_csv(__snake_case	,					index=__snake_case    )
       else:
              dataset.to_json(__snake_case    )


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			List[str]	,					__snake_case							:			List[Any]	,					__snake_case							:			Union[str, Any]	,					**__snake_case							:			Tuple    )					->							int:
       lowercase       :							Union[str, Any]									=				Accelerator()
       # Make one log on every process with the configuration for debugging.
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,					datefmt="%m/%d/%Y %H:%M:%S"	,					level=logging.INFO	,					)
       logger.info(accelerator.state    )

       # Setup logging, we only want one process per machine to log things on the
       # screen. accelerator.is_local_main_process is only True for one process per
       # machine.
       logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR    )

       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_info()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()

       lowercase       :							List[str]									=				STModelArguments(model_name_or_path=__snake_case    )
       lowercase       :							List[Any]									=				STDataArguments(train_file=__snake_case	,					infer_file=__snake_case    )
       lowercase       :							Union[str, Any]									=				STTrainingArguments(output_dir=__snake_case    )
       lowercase       :							str									=				argparse.Namespace()

       for arg_class in (model_args, data_args, training_args):
              for key, value in vars(__snake_case    ).items():
                     setattr(__snake_case	,					__snake_case	,					__snake_case    )

       for key, value in kwargs.items():
              if hasattr(__snake_case	,					__snake_case    ):
                     setattr(__snake_case	,					__snake_case	,					__snake_case    )

    # Sanity checks
       lowercase       :							Any									=				{}
       lowercase       :							Optional[Any]									=				None

       # You need to provide the training data and the data to predict on
       assert args.train_file is not None
       assert args.infer_file is not None
       lowercase       :							Optional[Any]									=				args.train_file
       lowercase       :							int									=				args.infer_file

       if args.evaluation_strategy != IntervalStrategy.NO.value:
              assert args.eval_file is not None
              lowercase       :							List[Any]									=				args.eval_file

       for key in data_files:
              lowercase       :							Optional[Any]									=				data_files[key].split("."    )[-1]
              assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
              if args.data_file_extension is None:
                     lowercase       :							Any									=				extension
              else:
                     assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

       assert (
           args.eval_metric in datasets.list_metrics()
       ), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

       # If passed along, set the training seed now.
       if args.seed is not None:
              set_seed(args.seed    )

       logger.info("Creating the initial data directory for self-training..."    )
       lowercase       :							List[str]									=				f"""{args.output_dir}/self-train_iter-{{}}""".format
       lowercase       :							Dict									=				data_dir_format(0    )

       if accelerator.is_main_process:
              if args.output_dir is not None:
                     os.makedirs(args.output_dir	,					exist_ok=__snake_case    )
                     os.makedirs(__snake_case	,					exist_ok=__snake_case    )
       accelerator.wait_for_everyone()

       lowercase       :							Tuple									=				None
       lowercase       :							List[Any]									=				None
       lowercase       :							Any									=				0
       lowercase       :							int									=				False
       # Show the progress bar
       lowercase       :							int									=				tqdm(range(args.max_selftrain_iterations    )	,					disable=not accelerator.is_local_main_process    )

       # Self-train
       for iteration in range(0	,					int(args.max_selftrain_iterations    )    ):
              lowercase       :							int									=				data_dir_format(__snake_case    )
              assert os.path.exists(__snake_case    )

              # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
              # iteration > 0
              lowercase       :							Optional[int]									=				os.path.join(__snake_case	,					"stage-1"    )
              lowercase       :							List[Any]									=				{
                  "accelerator": accelerator,
                  "model_name_or_path": args.model_name_or_path,
                  "cache_dir": args.cache_dir,
                  "do_train": True,
                  "train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
                  "do_eval": True if args.eval_file is not None else False,
                  "eval_file": data_files["eval"],
                  "do_predict": True,
                  "infer_file": data_files["infer"],
                  "task_name": args.task_name,
                  "label_list": args.label_list,
                  "output_dir": current_output_dir,
                  "eval_metric": args.eval_metric,
                  "evaluation_strategy": args.evaluation_strategy,
                  "early_stopping_patience": args.early_stopping_patience,
                  "early_stopping_threshold": args.early_stopping_threshold,
                  "seed": args.seed,
              }
              # Add additional training arguments
              for key, value in kwargs.items():
                     if key not in arguments_dict and not hasattr(__snake_case	,					__snake_case    ):
                            arguments_dict.update({key: value}    )

              lowercase       :							Union[str, Any]									=				os.path.join(__snake_case	,					"best-checkpoint"	,					__snake_case    )
              if os.path.exists(__snake_case    ):
                     logger.info(
                         "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."	,					__snake_case	,					__snake_case	,					)
              else:
                     logger.info("***** Running self-training: iteration: %d, stage: 1 *****"	,					__snake_case    )
                     finetune(**__snake_case    )
                     accelerator.wait_for_everyone()
                     assert os.path.exists(__snake_case    )
                     logger.info("Self-training job completed: iteration: %d, stage: 1."	,					__snake_case    )

              if iteration > 0 and args.finetune_on_labeled_data:
                     # Stage 2 (optional): fine-tuning on the original labeled data
                     lowercase       :							int									=				os.path.join(__snake_case	,					"best-checkpoint"    )
                     lowercase       :							Tuple									=				os.path.join(__snake_case	,					"stage-2"    )
                     # Update arguments_dict
                     lowercase       :							Any									=				model_path
                     lowercase       :							List[Any]									=				data_files["train"]
                     lowercase       :							Tuple									=				current_output_dir

                     lowercase       :							Optional[int]									=				os.path.join(__snake_case	,					"best-checkpoint"	,					__snake_case    )
                     if os.path.exists(__snake_case    ):
                            logger.info(
                                "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."	,					__snake_case	,					__snake_case	,					)
                     else:
                            logger.info("***** Running self-training: iteration: %d, stage: 2 *****"	,					__snake_case    )
                            finetune(**__snake_case    )
                            accelerator.wait_for_everyone()
                            assert os.path.exists(__snake_case    )
                            logger.info("Self-training job completed: iteration: %d, stage: 2."	,					__snake_case    )

              lowercase       :							List[Any]									=				iteration
              lowercase       :							Union[str, Any]									=				data_dir_format(iteration + 1    )

              lowercase       :							int									=				AutoConfig.from_pretrained(os.path.join(__snake_case	,					"best-checkpoint"    )    )
              lowercase       :							str									=				config.idalabel
              lowercase       :							List[str]									=				os.path.join(__snake_case	,					"eval_results_best-checkpoint.json"    )
              lowercase       :							int									=				os.path.join(__snake_case	,					"test_results_best-checkpoint.json"    )
              assert os.path.exists(__snake_case    )

              with open(__snake_case	,					"r"    ) as f:
                     lowercase       :							Any									=				float(json.load(__snake_case    )[args.eval_metric]    )
              lowercase       :							int									=				os.path.join(__snake_case	,					"infer_output_best-checkpoint.csv"    )
              assert os.path.exists(__snake_case    )
              # Loading the dataset from local csv or json files.
              lowercase       :							Optional[int]									=				load_dataset(args.data_file_extension	,					data_files={"data": data_files["infer"]}    )["data"]
              lowercase       :							Any									=				load_dataset("csv"	,					data_files={"data": infer_output_file}    )["data"]

              if accelerator.is_main_process:
                     os.makedirs(__snake_case	,					exist_ok=__snake_case    )
                     shutil.copy(__snake_case	,					os.path.join(__snake_case	,					f"""eval_results_iter-{iteration}.json"""    )    )
                     if os.path.exists(__snake_case    ):
                            shutil.copy(__snake_case	,					os.path.join(__snake_case	,					f"""test_results_iter-{iteration}.json"""    )    )
                     create_pseudo_labeled_data(__snake_case	,					__snake_case	,					__snake_case	,					__snake_case	,					__snake_case	,					__snake_case    )
              accelerator.wait_for_everyone()

              lowercase       :							int									=				os.path.join(__snake_case	,					f"""train_pseudo.{args.data_file_extension}"""    )

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     lowercase       :							Union[str, Any]									=				eval_result

                     if best_iteration is None:
                            lowercase       :							str									=				new_iteration
                            lowercase       :							Optional[Any]									=				new_eval_result
                     else:
                            if new_eval_result - best_eval_result > args.early_stopping_threshold:
                                   lowercase       :							Any									=				new_iteration
                                   lowercase       :							Dict									=				new_eval_result
                                   lowercase       :							int									=				0
                            else:
                                   if new_eval_result == best_eval_result:
                                          lowercase       :							Tuple									=				new_iteration
                                          lowercase       :							str									=				new_eval_result
                                   early_stopping_patience_counter += 1

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   lowercase       :							Union[str, Any]									=				True

              progress_bar.update(1    )

              if should_training_stop:
                     break

       if best_iteration is not None:
              # Save the best iteration
              logger.info("Best iteration: %d"	,					__snake_case    )
              logger.info("Best evaluation result: %s = %f"	,					args.eval_metric	,					__snake_case    )
              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     shutil.copy(
                         os.path.join(__snake_case	,					f"""eval_results_iter-{iteration}.json"""    )	,					os.path.join(__snake_case	,					"eval_results_best-iteration.json"    )	,					)
       else:
              # Assume that the last iteration is the best
              logger.info("Best iteration: %d"	,					args.max_selftrain_iterations - 1    )
              logger.info("Best evaluation result: %s = %f"	,					args.eval_metric	,					__snake_case    )
              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     shutil.copy(
                         os.path.join(__snake_case	,					f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""    )	,					os.path.join(__snake_case	,					"eval_results_best-iteration.json"    )	,					)




"""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 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							IFPipeline
   __lowerCAmelCase				=							TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
   __lowerCAmelCase				=							TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase				=							PipelineTesterMixin.required_optional_params - {"""latents"""}
   def      __magic_name__     (     self							):
          return self._get_dummy_components()
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          if str(_a							).startswith("mps"							):
                 lowercase       :							List[str]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							Dict									=				torch.Generator(device=_a							).manual_seed(_a							)

          lowercase       :							str									=				{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs
   def      __magic_name__     (     self							):
          self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != "cuda"      ,       reason="float16 requires CUDA"							)
   def      __magic_name__     (     self							):
          # 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      __magic_name__     (     self							):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)
   def      __magic_name__     (     self							):
          self._test_save_load_local()
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self							):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self							):
          # if

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

          lowercase       :							List[str]									=				IFSuperResolutionPipeline.from_pretrained(
              "DeepFloyd/IF-II-L-v1.0"      ,       variant="fp16"      ,       torch_dtype=torch.floataa      ,       text_encoder=_a      ,       tokenizer=_a							)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to("cuda"							)

          lowercase						,       lowercase       :							int									=				pipe_a.encode_prompt("anime turtle"      ,       device="cuda"							)

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

          lowercase       :							List[str]									=				None
          lowercase       :							Union[str, Any]									=				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(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          lowercase       :							Optional[int]									=				IFImgaImgPipeline(**pipe_a.components							)
          lowercase       :							Dict									=				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(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          lowercase       :							List[str]									=				IFInpaintingPipeline(**pipe_a.components							)
          lowercase       :							Optional[Any]									=				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(_a      ,       _a      ,       _a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Optional[Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Optional[Any]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

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

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

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

          # pipeline 2

          _start_torch_memory_measurement()

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

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

          lowercase       :							Optional[int]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

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

          lowercase       :							Union[str, Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							Tuple									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

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

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

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

          lowercase       :							Optional[Any]									=				output.images[0]

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

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          lowercase       :							Union[str, Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Dict									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

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

          lowercase       :							int									=				output.images[0]

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

          lowercase       :							List[str]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							int									=				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(_a      ,       _a							)

   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Dict									=				output.images[0]

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

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

          lowercase       :							str									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Optional[Any]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       original_image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

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

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

          lowercase       :							Tuple									=				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(_a      ,       _a							)


def     __magic_name__      ( )					->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()


