





"""simple docstring"""
import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel



def 	__lowerCamelCase  (				a_	:						int=32					,  a_	:						Tuple=10					,  a_	:						Union[str, Any]=1_00					,  a_	:						Optional[Any]=10_26					,  a_	:						Tuple=True					,  a_	:						Tuple="data/tokenized_stories_train_wikitext103.jbl"					,  a_	:						List[str]="igf_context_pairs.jbl"					,  )     -> Union[str, Any]:
      set_seed(3				)
      # generate train_data and objective_set
      __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :Optional[int]													=    generate_datasets(
          UpperCamelCase__					,  UpperCamelCase__					,  number=UpperCamelCase__					,  min_len=10_26					,  trim=UpperCamelCase__				)
      # keeps model same across runs
      set_seed(4				)
      # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
      # can we train on GPU?
      __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''				)

      # load pretrained model
      __SCREAMING_SNAKE_CASE  :Optional[Any]													=    load_gpta('''gpt2'''				).to(UpperCamelCase__				)
      print('''computing perplexity on objective set'''				)
      __SCREAMING_SNAKE_CASE  :Dict													=    compute_perplexity(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__				).item()
      print('''perplexity on objective set:'''					,  UpperCamelCase__				)

      # collect igf pairs and save to file demo.jbl
      collect_objective_set(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__				)

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()



def 	__lowerCamelCase  (				a_	:						Dict					,  a_	:						Optional[int]=15					,  a_	:						List[Any]=1_28					,  a_	:						Tuple=1_00					,  a_	:						Dict="igf_model.pt"					,  )     -> Any:
      set_seed(42				)

      # Load pre-trained model
      __SCREAMING_SNAKE_CASE  :Optional[int]													=    GPTaLMHeadModel.from_pretrained('''gpt2'''				)

      # Initialize secondary learner to use embedding weights of model
      __SCREAMING_SNAKE_CASE  :List[str]													=    SecondaryLearner(UpperCamelCase__				)

      # Train secondary learner
      __SCREAMING_SNAKE_CASE  :Optional[Any]													=    train_secondary_learner(
          UpperCamelCase__					,  UpperCamelCase__					,  max_epochs=UpperCamelCase__					,  batch_size=UpperCamelCase__					,  eval_freq=1_00					,  igf_model_path=UpperCamelCase__					,  )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner



def 	__lowerCamelCase  (				a_	:						Any					,  a_	:						Union[str, Any]					,  a_	:						Union[str, Any]					,  a_	:						Union[str, Any]=32					,  a_	:						Optional[int]=10_00					,  a_	:						List[str]=16					,  a_	:						Union[str, Any]=1.0					,  a_	:						Tuple=recopy_gpta					,  a_	:						str=None					,  a_	:						Optional[int]=10					,  a_	:						Union[str, Any]="gpt2_finetuned.pt"					,  )     -> Any:
      __SCREAMING_SNAKE_CASE  :List[str]													=    torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''				)
      __SCREAMING_SNAKE_CASE  :Optional[Any]													=    RandomSampler(UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE  :List[str]													=    DataLoader(UpperCamelCase__					,  sampler=UpperCamelCase__				)

      __SCREAMING_SNAKE_CASE  :Optional[Any]													=    max_steps // (len(UpperCamelCase__				)) + 1
      __SCREAMING_SNAKE_CASE  :int													=    0
      __SCREAMING_SNAKE_CASE  :List[str]													=    torch.zeros((1, context_len)					,  dtype=torch.long					,  device=UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :List[str]													=    recopy_model(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__				)

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(UpperCamelCase__				)
            secondary_learner.eval()
      __SCREAMING_SNAKE_CASE  :Any													=    []
      __SCREAMING_SNAKE_CASE  :Optional[int]													=    0

      __SCREAMING_SNAKE_CASE  :Optional[Any]													=    []
      __SCREAMING_SNAKE_CASE  :List[Any]													=    []

      # Compute the performance of the transformer model at the beginning
      __SCREAMING_SNAKE_CASE  :Optional[Any]													=    compute_perplexity(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__				)
      test_perps.append(UpperCamelCase__				)
      print('''Test perplexity, step'''					,  UpperCamelCase__					,  ''':'''					,  UpperCamelCase__				)
      for epoch in range(int(UpperCamelCase__				)				):
            for step, example in enumerate(UpperCamelCase__				):
                  torch.cuda.empty_cache()
                  __SCREAMING_SNAKE_CASE  :Any													=    random.randint(0					,  example.size(2				) - context_len - 1				)
                  __SCREAMING_SNAKE_CASE  :Tuple													=    example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  __SCREAMING_SNAKE_CASE  :int													=    model(UpperCamelCase__					,  labels=UpperCamelCase__				)
                  __SCREAMING_SNAKE_CASE  :Dict													=    True

                  if secondary_learner is not None:
                        __SCREAMING_SNAKE_CASE  :Optional[Any]													=    secondary_learner.forward(
                            torch.tensor(UpperCamelCase__					,  dtype=torch.long					,  device=UpperCamelCase__				).unsqueeze(0				)				)[0].item()
                        observed_qs.append(float(UpperCamelCase__				)				)

                        # Here we implement the simple non-constant threshold for the predicted IG(X) value
                        # We will decay the selectivity of our secondary learner filter from
                        # 1 standard deviation above average to 1 below average after 10 batches.

                        if global_step == 10:
                              __SCREAMING_SNAKE_CASE  :str													=    -1
                        if predicted_q < threshold:
                              __SCREAMING_SNAKE_CASE  :Tuple													=    False

                                     # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()				)				)
                        __SCREAMING_SNAKE_CASE  :Optional[Any]													=    outputs[0]
                        lm_loss.backward()
                        examples += 1

                  del outputs

                  # Once the batch is filled with enough contexts, backprop on the batch.
                  if examples == batch_size:
                        torch.cuda.empty_cache()
                        __SCREAMING_SNAKE_CASE  :Dict													=    0
                        # Do LM backprop
                        torch.nn.utils.clip_grad_norm_(model.parameters()					,  3.0				)
                        lm_optimizer.step()
                        lm_scheduler.step()  # Update learning rate schedule
                        global_step += 1
                        # Compute the performance of the transformer model at this batch
                        if global_step % eval_interval == 0:
                              __SCREAMING_SNAKE_CASE  :int													=    compute_perplexity(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__				)
                              test_perps.append(UpperCamelCase__				)

                              print('''Test perplexity, step'''					,  UpperCamelCase__					,  ''':'''					,  UpperCamelCase__				)
                                     # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 60:
                        break
            if max_steps > 0 and global_step > 60:
                  break

                   # save finetuned transformer model
      torch.save(model.state_dict()					,  UpperCamelCase__				)
      torch.cuda.empty_cache()
      # Do some cleaning up so we can reinitialize for the next run of this function
      del lm_optimizer
      del lm_scheduler
      return model



def 	__lowerCamelCase  (				)     -> List[str]:
      __SCREAMING_SNAKE_CASE  :str													=    argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''				)

      # Required parameters
      parser.add_argument(
          '''--data_dir'''					,  default=UpperCamelCase__					,  type=UpperCamelCase__					,  required=UpperCamelCase__					,  help='''The input data dir. Should contain data files for WikiText.'''					,  )
      parser.add_argument(
          '''--model_name_or_path'''					,  default=UpperCamelCase__					,  type=UpperCamelCase__					,  required=UpperCamelCase__					,  help='''Path to pretrained model or model identifier from huggingface.co/models'''					,  )
      parser.add_argument(
          '''--data_file'''					,  type=UpperCamelCase__					,  default=UpperCamelCase__					,  help=(
              '''A jbl file containing tokenized data which can be split as objective dataset, '''
              '''train_dataset and test_dataset.'''
          )					,  )

      parser.add_argument(
          '''--igf_data_file'''					,  type=UpperCamelCase__					,  default=UpperCamelCase__					,  help='''A jbl file containing the context and information gain pairs to train secondary learner.'''					,  )

      parser.add_argument(
          '''--output_dir'''					,  default=UpperCamelCase__					,  type=UpperCamelCase__					,  required=UpperCamelCase__					,  help='''The output directory where the final fine-tuned model is stored.'''					,  )

      parser.add_argument(
          '''--tokenizer_name'''					,  default=UpperCamelCase__					,  type=UpperCamelCase__					,  help='''Pretrained tokenizer name or path if not the same as model_name'''					,  )
      parser.add_argument('''--seed'''					,  type=UpperCamelCase__					,  default=UpperCamelCase__					,  help='''A seed for reproducible training.'''				)

      parser.add_argument(
          '''--context_len'''					,  default=32					,  type=UpperCamelCase__					,  help=(
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )					,  )

      parser.add_argument(
          '''--size_objective_set'''					,  default=1_00					,  type=UpperCamelCase__					,  help='''number of articles that are long enough to be used as our objective set'''					,  )
      parser.add_argument(
          '''--eval_freq'''					,  default=1_00					,  type=UpperCamelCase__					,  help='''secondary model evaluation is triggered at eval_freq'''				)

      parser.add_argument('''--max_steps'''					,  default=10_00					,  type=UpperCamelCase__					,  help='''To calculate training epochs'''				)

      parser.add_argument(
          '''--secondary_learner_batch_size'''					,  default=1_28					,  type=UpperCamelCase__					,  help='''batch size of training data for secondary learner'''					,  )

      parser.add_argument(
          '''--batch_size'''					,  default=16					,  type=UpperCamelCase__					,  help='''batch size of training data of language model(gpt2) '''				)

      parser.add_argument(
          '''--eval_interval'''					,  default=10					,  type=UpperCamelCase__					,  help=(
              '''decay the selectivity of our secondary learner filter from'''
              '''1 standard deviation above average to 1 below average after 10 batches'''
          )					,  )

      parser.add_argument(
          '''--number'''					,  default=1_00					,  type=UpperCamelCase__					,  help='''The number of examples split to be used as objective_set/test_data'''				)

      parser.add_argument(
          '''--min_len'''					,  default=10_26					,  type=UpperCamelCase__					,  help='''The minimum length of the article to be used as objective set'''				)

      parser.add_argument(
          '''--secondary_learner_max_epochs'''					,  default=15					,  type=UpperCamelCase__					,  help='''number of epochs to train secondary learner'''				)

      parser.add_argument('''--trim'''					,  default=UpperCamelCase__					,  type=UpperCamelCase__					,  help='''truncate the example if it exceeds context length'''				)

      parser.add_argument(
          '''--threshold'''					,  default=1.0					,  type=UpperCamelCase__					,  help=(
              '''The threshold value used by secondary learner to filter the train_data and allow only'''
              ''' informative data as input to the model'''
          )					,  )

      parser.add_argument('''--finetuned_model_name'''					,  default='''gpt2_finetuned.pt'''					,  type=UpperCamelCase__					,  help='''finetuned_model_name'''				)

      parser.add_argument(
          '''--recopy_model'''					,  default=UpperCamelCase__					,  type=UpperCamelCase__					,  help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''					,  )

      # function calls
      # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
      generate_n_pairs(
          context_len=32					,  max_steps=10					,  size_objective_set=1_00					,  min_len=10_26					,  trim=UpperCamelCase__					,  data_file='''data/tokenized_stories_train_wikitext103.jbl'''					,  igf_data_file='''igf_context_pairs.jbl'''					,  )

      # Load train data for secondary learner
      __SCREAMING_SNAKE_CASE  :Tuple													=    joblib.load('''data/IGF_values.jbl'''				)

      # Train secondary learner
      __SCREAMING_SNAKE_CASE  :Dict													=    training_secondary_learner(
          UpperCamelCase__					,  secondary_learner_max_epochs=15					,  secondary_learner_batch_size=1_28					,  eval_freq=1_00					,  igf_model_path='''igf_model.pt'''					,  )

      # load pretrained gpt2 model
      __SCREAMING_SNAKE_CASE  :int													=    GPTaLMHeadModel.from_pretrained('''gpt2'''				)
      set_seed(42				)

      # Generate train and test data to train and evaluate gpt2 model
      __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :Dict													=    generate_datasets(
          context_len=32					,  file='''data/tokenized_stories_train_wikitext103.jbl'''					,  number=1_00					,  min_len=10_26					,  trim=UpperCamelCase__				)

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  context_len=32					,  max_steps=10_00					,  batch_size=16					,  threshold=1.0					,  recopy_model=UpperCamelCase__					,  secondary_learner=UpperCamelCase__					,  eval_interval=10					,  finetuned_model_name='''gpt2_finetuned.pt'''					,  )


if __name__ == "__main__":
  main()



"""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  lowerCamelCase__    (		_a      , _a      , unittest.TestCase     ):
      a			:  Any           =		IFPipeline
      a			:  str           =		TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
      a			:  Dict           =		TEXT_TO_IMAGE_BATCH_PARAMS
      a			:  int           =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self._get_dummy_components()
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						int   ,							A_						:						Dict=0				):






       '''simple docstring'''


       if str(A_				).startswith("""mps"""				):
        __lowercase						=						torch.manual_seed(A_				)
       else:
        __lowercase						=						torch.Generator(device=A_				).manual_seed(A_				)

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

       return inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != """cuda"""   ,							reason="""float16 requires CUDA"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().test_save_load_floataa(expected_max_diff=1e-1				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       self._test_save_load_local()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)






@slow
@require_torch_gpu
class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


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

       __lowercase						=						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						=						pipe_a.encode_prompt("""anime turtle"""   ,							device="""cuda"""				)

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

       __lowercase						=						None
       __lowercase						=						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						=						IFImgaImgPipeline(**pipe_a.components				)
       __lowercase						=						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						=						IFInpaintingPipeline(**pipe_a.components				)
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Any   ,							A_						:						int   ,							A_						:						str   ,							A_						:						Dict				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_3 * 1_0**9

       __lowercase						=						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						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Tuple   ,							A_						:						List[Any]   ,							A_						:						List[Any]   ,							A_						:						Any				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

       __lowercase						=						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						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							original_image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						List[Any]   ,							A_						:						str   ,							A_						:						List[Any]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							mask_image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

       __lowercase						=						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						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						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						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						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 lowerCAmelCase_   (					):






 """simple docstring"""







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