




'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 						lowerCAmelCase_   ( __UpperCAmelCase			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					List[Any]				     =						PaintByExamplePipeline
  lowerCAmelCase_				:					List[Any]				     =						IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  lowerCAmelCase_				:					List[Any]				     =						IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowerCAmelCase_				:					str				     =						frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   torch.manual_seed(0 )
   UpperCAmelCase__										=    UNetaDConditionModel(
       block_out_channels=(32, 64)						,		layers_per_block=2						,		sample_size=32						,		in_channels=9						,		out_channels=4						,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,		cross_attention_dim=32						,		)
   UpperCAmelCase__										=    PNDMScheduler(skip_prk_steps=lowerCamelCase__ )
   torch.manual_seed(0 )
   UpperCAmelCase__										=    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 )
   UpperCAmelCase__										=    CLIPVisionConfig(
       hidden_size=32						,		projection_dim=32						,		intermediate_size=37						,		layer_norm_eps=1E-05						,		num_attention_heads=4						,		num_hidden_layers=5						,		image_size=32						,		patch_size=4						,		)
   UpperCAmelCase__										=    PaintByExampleImageEncoder(lowerCamelCase__						,		proj_size=32 )
   UpperCAmelCase__										=    CLIPImageProcessor(crop_size=32						,		size=32 )

   UpperCAmelCase__										=    {
       """unet""": unet,
       """scheduler""": scheduler,
       """vae""": vae,
       """image_encoder""": image_encoder,
       """safety_checker""": None,
       """feature_extractor""": feature_extractor,
   }
   return components


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    np.array(image.convert("""RGB""" ) )
   UpperCAmelCase__										=    image[None].transpose(0						,		3						,		1						,		2 )
   UpperCAmelCase__										=    torch.from_numpy(lowerCamelCase__ ).to(dtype=torch.floataa ) / 1_27.5 - 1.0
   return image


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	str="cpu"						,		_UpperCAmelCase    :	Any=0 ):






   """simple docstring"""






   UpperCAmelCase__										=    floats_tensor((1, 3, 32, 32)						,		rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
   UpperCAmelCase__										=    image.cpu().permute(0						,		2						,		3						,		1 )[0]
   UpperCAmelCase__										=    Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert("""RGB""" ).resize((64, 64) )
   UpperCAmelCase__										=    Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
   UpperCAmelCase__										=    Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert("""RGB""" ).resize((32, 32) )

   if str(lowerCamelCase__ ).startswith("""mps""" ):
    UpperCAmelCase__										=    torch.manual_seed(lowerCamelCase__ )
   else:
    UpperCAmelCase__										=    torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
   UpperCAmelCase__										=    {
       """example_image""": example_image,
       """image""": init_image,
       """mask_image""": mask_image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
   }
   return inputs


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.get_dummy_components()

   # make sure here that pndm scheduler skips prk
   UpperCAmelCase__										=    PaintByExamplePipeline(**lowerCamelCase__ )
   UpperCAmelCase__										=    pipe.to("""cpu""" )
   pipe.set_progress_bar_config(disable=lowerCamelCase__ )

   UpperCAmelCase__										=    self.get_dummy_inputs()
   UpperCAmelCase__										=    pipe(**lowerCamelCase__ )
   UpperCAmelCase__										=    output.images

   UpperCAmelCase__										=    image[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   UpperCAmelCase__										=    np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503] )

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    """cpu"""
   UpperCAmelCase__										=    self.get_dummy_inputs()
   inputs.pop("""mask_image""" )
   UpperCAmelCase__										=    self.convert_to_pt(inputs.pop("""image""" ) )
   UpperCAmelCase__										=    image.clamp(0						,		1 ) / 2

   # make sure here that pndm scheduler skips prk
   UpperCAmelCase__										=    PaintByExamplePipeline(**self.get_dummy_components() )
   UpperCAmelCase__										=    pipe.to(lowerCamelCase__ )
   pipe.set_progress_bar_config(disable=lowerCamelCase__ )

   UpperCAmelCase__										=    pipe(image=lowerCamelCase__						,		mask_image=mask_image[:, 0]						,		**lowerCamelCase__ )
   UpperCAmelCase__										=    output.images

   UpperCAmelCase__										=    image.cpu().permute(0						,		2						,		3						,		1 )[0]
   UpperCAmelCase__										=    mask_image.cpu().permute(0						,		2						,		3						,		1 )[0]

   UpperCAmelCase__										=    Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert("""RGB""" )
   UpperCAmelCase__										=    Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert("""RGB""" )

   UpperCAmelCase__										=    pipe(**self.get_dummy_inputs() )
   UpperCAmelCase__										=    output.images

   assert out_a.shape == (1, 64, 64, 3)
   assert np.abs(out_a.flatten() - out_a.flatten() ).max() < 5E-2



  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   super().test_inference_batch_single_identical(expected_max_diff=3E-3 )

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







  '''simple docstring'''







  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






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



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/paint_by_example/dog_in_bucket.png""" )
   UpperCAmelCase__										=    load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/paint_by_example/mask.png""" )
   UpperCAmelCase__										=    load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/paint_by_example/panda.jpg""" )

   UpperCAmelCase__										=    PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example""" )
   UpperCAmelCase__										=    pipe.to(lowerCamelCase__ )
   pipe.set_progress_bar_config(disable=lowerCamelCase__ )

   UpperCAmelCase__										=    torch.manual_seed(3_21 )
   UpperCAmelCase__										=    pipe(
       image=lowerCamelCase__						,		mask_image=lowerCamelCase__						,		example_image=lowerCamelCase__						,		generator=lowerCamelCase__						,		guidance_scale=5.0						,		num_inference_steps=50						,		output_type="""np"""						,		)

   UpperCAmelCase__										=    output.images

   UpperCAmelCase__										=    image[0, -3:, -3:, -1]

   assert image.shape == (1, 5_12, 5_12, 3)
   UpperCAmelCase__										=    np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374] )

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






'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


UpperCAmelCase_       				=      logging.getLogger(__name__)

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Tuple						,		_UpperCAmelCase    :	Optional[Any] ):






   """simple docstring"""






   if not self.initialized:
    UpperCAmelCase__										=    RagRetriever(
        _UpperCAmelCase						,		question_encoder_tokenizer=_UpperCAmelCase						,		generator_tokenizer=_UpperCAmelCase						,		index=_UpperCAmelCase						,		init_retrieval=_UpperCAmelCase						,		)
    UpperCAmelCase__										=    True


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   self.retriever.index.init_index()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   del self.retriever
   UpperCAmelCase__										=    False



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.retriever._main_retrieve(_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__										=    self.retriever.index.get_doc_dicts(_UpperCAmelCase )
   return doc_ids, retrieved_doc_embeds, doc_dicts

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def __init__(						self    :	Optional[Any]						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	Dict						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	List[Any]=None ):






   """simple docstring"""






   if index is not None and index.is_initialized() and len(_UpperCAmelCase ) > 0:
    raise ValueError(
        """When using Ray for distributed fine-tuning, """
        """you'll need to provide the paths instead, """
        """as the dataset and the index are loaded """
        """separately. More info in examples/rag/use_own_knowledge_dataset.py """ )

   super().__init__(
       _UpperCAmelCase						,		question_encoder_tokenizer=_UpperCAmelCase						,		generator_tokenizer=_UpperCAmelCase						,		index=_UpperCAmelCase						,		init_retrieval=_UpperCAmelCase						,		)

   UpperCAmelCase__										=    retrieval_workers
   UpperCAmelCase__										=    question_encoder_tokenizer
   UpperCAmelCase__										=    generator_tokenizer
   if len(self.retrieval_workers ) > 0:
    ray.get(
        [
            worker.create_rag_retriever.remote(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )
            for worker in self.retrieval_workers
        ] )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   logger.info("""initializing retrieval""" )

   if len(self.retrieval_workers ) > 0:
    ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
   else:
    # Non-distributed training. Load index into this same process.
    self.index.init_index()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Dict ):






   """simple docstring"""






   if len(self.retrieval_workers ) > 0:
    # Select a random retrieval actor.
    UpperCAmelCase__										=    self.retrieval_workers[random.randint(0						,		len(self.retrieval_workers ) - 1 )]
    UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    ray.get(
        random_worker.retrieve.remote(_UpperCAmelCase						,		_UpperCAmelCase ) )
   else:
    UpperCAmelCase__							,			UpperCAmelCase__										=    self._main_retrieve(_UpperCAmelCase						,		_UpperCAmelCase )
    UpperCAmelCase__										=    self.index.get_doc_dicts(_UpperCAmelCase )
   return retrieved_doc_embeds, doc_ids, doc_dicts


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	str						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str=None						,		**_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   return super(_UpperCAmelCase						,		cls ).get_tokenizers(_UpperCAmelCase						,		_UpperCAmelCase						,		**_UpperCAmelCase )


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	int						,		_UpperCAmelCase    :	Optional[int]						,		_UpperCAmelCase    :	Optional[int]						,		_UpperCAmelCase    :	Union[str, Any]=None						,		**_UpperCAmelCase    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    kwargs.pop("""config"""						,		_UpperCAmelCase ) or RagConfig.from_pretrained(_UpperCAmelCase						,		**_UpperCAmelCase )
   UpperCAmelCase__										=    RagTokenizer.from_pretrained(_UpperCAmelCase						,		config=_UpperCAmelCase )
   UpperCAmelCase__										=    rag_tokenizer.question_encoder
   UpperCAmelCase__										=    rag_tokenizer.generator

   if indexed_dataset is not None:
    UpperCAmelCase__										=    """custom"""
    UpperCAmelCase__										=    CustomHFIndex(config.retrieval_vector_size						,		_UpperCAmelCase )
   else:
    UpperCAmelCase__										=    cls._build_index(_UpperCAmelCase )

   return cls(
       _UpperCAmelCase						,		question_encoder_tokenizer=_UpperCAmelCase						,		generator_tokenizer=_UpperCAmelCase						,		retrieval_workers=_UpperCAmelCase						,		index=_UpperCAmelCase						,		)



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   logger.info("""re-loading the new dataset with embeddings""" )
   # access from the training loop

   ray.get([worker.clear_object.remote() for worker in self.retrieval_workers] )

   # build the index object again
   UpperCAmelCase__										=    self._build_index(self.config )

   ray.get(
       [
           worker.create_rag_retriever.remote(
               self.config						,		self.question_encoder_tokenizer						,		self.generator_tokenizer						,		_UpperCAmelCase )
           for worker in self.retrieval_workers
       ] )
