


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						(							snake_case	:    List[str]=32						,      snake_case	:    Tuple=10						,      snake_case	:    Optional[Any]=100						,      snake_case	:    List[str]=1026						,      snake_case	:    str=True						,      snake_case	:    Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"						,      snake_case	:    Optional[int]="igf_context_pairs.jbl"						,      )       ->							int:
   set_seed(3 )
   # generate train_data and objective_set
   __lowerCamelCase   ,     __lowerCamelCase:				Any     =	generate_datasets(
       snake_case						,      snake_case						,      number=snake_case						,      min_len=1026						,      trim=snake_case )
   # 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?
   __lowerCamelCase:				str     =	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )

   # load pretrained model
   __lowerCamelCase:				Tuple     =	load_gpta("""gpt2""" ).to(snake_case )
   print("""computing perplexity on objective set""" )
   __lowerCamelCase:				int     =	compute_perplexity(snake_case						,      snake_case						,      snake_case ).item()
   print("""perplexity on objective set:"""						,      snake_case )

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

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



def     __lowerCAmelCase						(							snake_case	:    Tuple						,      snake_case	:    Dict=15						,      snake_case	:    List[Any]=128						,      snake_case	:    Optional[Any]=100						,      snake_case	:    Optional[int]="igf_model.pt"						,      )       ->							Dict:
   set_seed(42 )

   # Load pre-trained model
   __lowerCamelCase:				int     =	GPTaLMHeadModel.from_pretrained("""gpt2""" )

   # Initialize secondary learner to use embedding weights of model
   __lowerCamelCase:				List[Any]     =	SecondaryLearner(snake_case )

   # Train secondary learner
   __lowerCamelCase:				Any     =	train_secondary_learner(
       snake_case						,      snake_case						,      max_epochs=snake_case						,      batch_size=snake_case						,      eval_freq=100						,      igf_model_path=snake_case						,      )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner



def     __lowerCAmelCase						(							snake_case	:    Any						,      snake_case	:    Tuple						,      snake_case	:    Optional[int]						,      snake_case	:    Tuple=32						,      snake_case	:    Tuple=1000						,      snake_case	:    List[Any]=16						,      snake_case	:    Optional[int]=1.0						,      snake_case	:    str=recopy_gpta						,      snake_case	:    Union[str, Any]=None						,      snake_case	:    str=10						,      snake_case	:    str="gpt2_finetuned.pt"						,      )       ->							List[Any]:
   __lowerCamelCase:				Union[str, Any]     =	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
   __lowerCamelCase:				Tuple     =	RandomSampler(snake_case )
   __lowerCamelCase:				int     =	DataLoader(snake_case						,      sampler=snake_case )

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

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(snake_case )
      secondary_learner.eval()
   __lowerCamelCase:				Dict     =	[]
   __lowerCamelCase:				List[Any]     =	0

   __lowerCamelCase:				int     =	[]
   __lowerCamelCase:				Union[str, Any]     =	[]

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

         if secondary_learner is not None:
            __lowerCamelCase:				int     =	secondary_learner.forward(
                torch.tensor(snake_case						,      dtype=torch.long						,      device=snake_case ).unsqueeze(0 ) )[0].item()
            observed_qs.append(float(snake_case ) )

            # 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:
               __lowerCamelCase:				Any     =	-1
            if predicted_q < threshold:
               __lowerCamelCase:				Any     =	False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu() ) )
            __lowerCamelCase:				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()
            __lowerCamelCase:				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:
               __lowerCamelCase:				Dict     =	compute_perplexity(snake_case						,      snake_case						,      snake_case )
               test_perps.append(snake_case )

               print("""Test perplexity, step"""						,      snake_case						,      """:"""						,      snake_case )
            # 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()						,      snake_case )
   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						(							)       ->							int:
   __lowerCamelCase:				Optional[Any]     =	argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" )

   # Required parameters
   parser.add_argument(
       """--data_dir"""						,      default=snake_case						,      type=snake_case						,      required=snake_case						,      help="""The input data dir. Should contain data files for WikiText."""						,      )
   parser.add_argument(
       """--model_name_or_path"""						,      default=snake_case						,      type=snake_case						,      required=snake_case						,      help="""Path to pretrained model or model identifier from huggingface.co/models"""						,      )
   parser.add_argument(
       """--data_file"""						,      type=snake_case						,      default=snake_case						,      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=snake_case						,      default=snake_case						,      help="""A jbl file containing the context and information gain pairs to train secondary learner."""						,      )

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

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

   parser.add_argument(
       """--context_len"""						,      default=32						,      type=snake_case						,      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=100						,      type=snake_case						,      help="""number of articles that are long enough to be used as our objective set"""						,      )
   parser.add_argument(
       """--eval_freq"""						,      default=100						,      type=snake_case						,      help="""secondary model evaluation is triggered at eval_freq""" )

   parser.add_argument("""--max_steps"""						,      default=1000						,      type=snake_case						,      help="""To calculate training epochs""" )

   parser.add_argument(
       """--secondary_learner_batch_size"""						,      default=128						,      type=snake_case						,      help="""batch size of training data for secondary learner"""						,      )

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

   parser.add_argument(
       """--eval_interval"""						,      default=10						,      type=snake_case						,      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=100						,      type=snake_case						,      help="""The number of examples split to be used as objective_set/test_data""" )

   parser.add_argument(
       """--min_len"""						,      default=1026						,      type=snake_case						,      help="""The minimum length of the article to be used as objective set""" )

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

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

   parser.add_argument(
       """--threshold"""						,      default=1.0						,      type=snake_case						,      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=snake_case						,      help="""finetuned_model_name""" )

   parser.add_argument(
       """--recopy_model"""						,      default=snake_case						,      type=snake_case						,      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=100						,      min_len=1026						,      trim=snake_case						,      data_file="""data/tokenized_stories_train_wikitext103.jbl"""						,      igf_data_file="""igf_context_pairs.jbl"""						,      )

   # Load train data for secondary learner
   __lowerCamelCase:				Optional[Any]     =	joblib.load("""data/IGF_values.jbl""" )

   # Train secondary learner
   __lowerCamelCase:				Any     =	training_secondary_learner(
       snake_case						,      secondary_learner_max_epochs=15						,      secondary_learner_batch_size=128						,      eval_freq=100						,      igf_model_path="""igf_model.pt"""						,      )

   # load pretrained gpt2 model
   __lowerCamelCase:				Optional[Any]     =	GPTaLMHeadModel.from_pretrained("""gpt2""" )
   set_seed(42 )

   # Generate train and test data to train and evaluate gpt2 model
   __lowerCamelCase   ,     __lowerCamelCase:				List[Any]     =	generate_datasets(
       context_len=32						,      file="""data/tokenized_stories_train_wikitext103.jbl"""						,      number=100						,      min_len=1026						,      trim=snake_case )

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       snake_case						,      snake_case						,      snake_case						,      context_len=32						,      max_steps=1000						,      batch_size=16						,      threshold=1.0						,      recopy_model=snake_case						,      secondary_learner=snake_case						,      eval_interval=10						,      finetuned_model_name="""gpt2_finetuned.pt"""						,      )


if __name__ == "__main__":
   main()






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	CycleDiffusionPipeline
     UpperCAmelCase__ :  Optional[Any]						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
     }
     UpperCAmelCase__ :  Union[str, Any]						      =	PipelineTesterMixin.required_optional_params - {"latents"}
     UpperCAmelCase__ :  Optional[int]						      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
     UpperCAmelCase__ :  Optional[int]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	UNetaDConditionModel(
            block_out_channels=(32, 64)    ,	layers_per_block=2    ,	sample_size=32    ,	in_channels=4    ,	out_channels=4    ,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,	cross_attention_dim=32    ,	)
        __lowerCamelCase:				Dict     =	DDIMScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""    ,	num_train_timesteps=1000    ,	clip_sample=SCREAMING_SNAKE_CASE_    ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	AutoencoderKL(
            block_out_channels=[32, 64]    ,	in_channels=3    ,	out_channels=3    ,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,	latent_channels=4    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				str     =	CLIPTextConfig(
            bos_token_id=0    ,	eos_token_id=2    ,	hidden_size=32    ,	intermediate_size=37    ,	layer_norm_eps=1E-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=1000    ,	)
        __lowerCamelCase:				int     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        __lowerCamelCase:				Optional[int]     =	{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str=0							):
        __lowerCamelCase:				Optional[Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	image / 2 + 0.5
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Tuple     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	{
            """prompt""": """An astronaut riding an elephant""",
            """source_prompt""": """An astronaut riding a horse""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """eta""": 0.1,
            """strength""": 0.8,
            """guidance_scale""": 3,
            """source_guidance_scale""": 1,
            """output_type""": """numpy""",
        }
        return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				Tuple     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images

        __lowerCamelCase:				int     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]							)

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





     @unittest.skipIf(torch_device != """cuda"""    ,	"""This test requires a GPU"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.get_dummy_components()
        for name, module in components.items():
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""half"""							):
              __lowerCamelCase:				Tuple     =	module.half()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	output.images

        __lowerCamelCase:				Optional[int]     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				List[str]     =	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]							)

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





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_save_load_local()





     @unittest.skip("""non-deterministic pipeline"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return super().test_inference_batch_single_identical()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        return super().test_save_load_optional_components()







     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_attention_slicing_forward_pass()



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





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Any     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""							)
        __lowerCamelCase:				str     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				Dict     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[int]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				List[Any]     =	CycleDiffusionPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa    ,	revision="""fp16"""							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				List[Any]     =	"""A black colored car"""
        __lowerCamelCase:				List[Any]     =	"""A blue colored car"""

        __lowerCamelCase:				List[Any]     =	torch.manual_seed(0							)
        __lowerCamelCase:				Any     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Dict     =	output.images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(image - expected_image							).max() < 5E-1







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""							)
        __lowerCamelCase:				List[str]     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				List[Any]     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				Dict     =	CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Optional[int]     =	"""A black colored car"""
        __lowerCamelCase:				int     =	"""A blue colored car"""

        __lowerCamelCase:				str     =	torch.manual_seed(0							)
        __lowerCamelCase:				Optional[Any]     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Any     =	output.images

        assert np.abs(image - expected_image							).max() < 2E-2


