



'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__a          =		logging.get_logger(__name__)  # pylint: disable=invalid-name





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""     ,   _lowerCAmelCase     ,   )
      if isinstance(_lowerCAmelCase     ,   torch.Tensor		):
            return image
      elif isinstance(_lowerCAmelCase     ,   PIL.Image.Image		):
            snake_case__  :    Optional[Any]    						=      [image]

      if isinstance(image[0]     ,   PIL.Image.Image		):
            snake_case__						,				snake_case__  :    Tuple    						=      image[0].size
            snake_case__						,				snake_case__  :    List[Any]    						=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            snake_case__  :    List[str]    						=      [np.array(i.resize((w, h)     ,   resample=PIL_INTERPOLATION["""lanczos"""]		)		)[None, :] for i in image]
            snake_case__  :    Optional[int]    						=      np.concatenate(_lowerCAmelCase     ,   axis=0		)
            snake_case__  :    Tuple    						=      np.array(_lowerCAmelCase		).astype(np.floataa		) / 255.0
            snake_case__  :    Tuple    						=      image.transpose(0     ,   3     ,   1     ,   2		)
            snake_case__  :    Any    						=      2.0 * image - 1.0
            snake_case__  :    Optional[int]    						=      torch.from_numpy(_lowerCAmelCase		)
      elif isinstance(image[0]     ,   torch.Tensor		):
            snake_case__  :    Any    						=      torch.cat(_lowerCAmelCase     ,   dim=0		)
      return image





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      if isinstance(_lowerCAmelCase     ,   torch.Tensor		):
            return mask
      elif isinstance(_lowerCAmelCase     ,   PIL.Image.Image		):
            snake_case__  :    Any    						=      [mask]

      if isinstance(mask[0]     ,   PIL.Image.Image		):
            snake_case__						,				snake_case__  :    Optional[int]    						=      mask[0].size
            snake_case__						,				snake_case__  :    List[Any]    						=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
            snake_case__  :    int    						=      [np.array(m.convert("""L"""		).resize((w, h)     ,   resample=PIL_INTERPOLATION["""nearest"""]		)		)[None, :] for m in mask]
            snake_case__  :    Dict    						=      np.concatenate(_lowerCAmelCase     ,   axis=0		)
            snake_case__  :    int    						=      mask.astype(np.floataa		) / 255.0
            snake_case__  :    Optional[Any]    						=      0
            snake_case__  :    str    						=      1
            snake_case__  :    Dict    						=      torch.from_numpy(_lowerCAmelCase		)
      elif isinstance(mask[0]     ,   torch.Tensor		):
            snake_case__  :    Optional[Any]    						=      torch.cat(_lowerCAmelCase     ,   dim=0		)
      return mask







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		42
    lowercase					=		42




    def __init__(    self					:     Union[str, Any] ,     snake_case_					:     Optional[int] ,     snake_case_					:     List[Any]						):
          super().__init__()
          self.register_modules(unet=snake_case_ ,     scheduler=snake_case_						)




    @torch.no_grad()
    def __call__(    self					:     Optional[Any] ,     snake_case_					:     Union[torch.Tensor, PIL.Image.Image] ,     snake_case_					:     Union[torch.Tensor, PIL.Image.Image] ,     snake_case_					:     int = 250 ,     snake_case_					:     float = 0.0 ,     snake_case_					:     int = 10 ,     snake_case_					:     int = 10 ,     snake_case_					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None ,     snake_case_					:     Optional[str] = "pil" ,     snake_case_					:     bool = True ,     ):
          snake_case__  :    Optional[Any]    						=      image

          snake_case__  :    Dict    						=      _preprocess_image(snake_case_						)
          snake_case__  :    int    						=      original_image.to(device=self.device ,     dtype=self.unet.dtype						)
          snake_case__  :    int    						=      _preprocess_mask(snake_case_						)
          snake_case__  :    List[str]    						=      mask_image.to(device=self.device ,     dtype=self.unet.dtype						)

          snake_case__  :    Union[str, Any]    						=      original_image.shape[0]

          # sample gaussian noise to begin the loop
          if isinstance(snake_case_ ,     snake_case_						) and len(snake_case_						) != batch_size:
                raise ValueError(
                    f"You have passed a list of generators of length {len(snake_case_						)}, but requested an effective batch"
                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

          snake_case__  :    Any    						=      original_image.shape
          snake_case__  :    Any    						=      randn_tensor(snake_case_ ,     generator=snake_case_ ,     device=self.device ,     dtype=self.unet.dtype						)

          # set step values
          self.scheduler.set_timesteps(snake_case_ ,     snake_case_ ,     snake_case_ ,     self.device						)
          snake_case__  :    Dict    						=      eta

          snake_case__  :    Optional[int]    						=      self.scheduler.timesteps[0] + 1
          snake_case__  :    List[str]    						=      generator[0] if isinstance(snake_case_ ,     snake_case_						) else generator
          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
                if t < t_last:
                      # predict the noise residual
                      snake_case__  :    Optional[int]    						=      self.unet(snake_case_ ,     snake_case_						).sample
                      # compute previous image: x_t -> x_t-1
                      snake_case__  :    Optional[int]    						=      self.scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_						).prev_sample

                else:
                      # compute the reverse: x_t-1 -> x_t
                      snake_case__  :    str    						=      self.scheduler.undo_step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Optional[Any]    						=      t

          snake_case__  :    Optional[Any]    						=      (image / 2 + 0.5).clamp(0 ,     1						)
          snake_case__  :    Dict    						=      image.cpu().permute(0 ,     2 ,     3 ,     1						).numpy()
          if output_type == "pil":
                snake_case__  :    str    						=      self.numpy_to_pil(snake_case_						)

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=snake_case_						)

"""simple docstring"""
import logging
import random

import ray

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


lowercase__					       =	logging.getLogger(__name__)




class    __snake_case       :



      def __init__(						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	False



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





        if not self.initialized:
          a__:     Optional[int]										=	RagRetriever(
              lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
          a__:     Optional[int]										=	True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        self.retriever.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__							,    a__:     str										=	self.retriever._main_retrieve(lowercase    ,			lowercase)
        return doc_ids, retrieved_doc_embeds







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			int:





        '''simple docstring'''





        if index is not None and index.is_initialized() and len(lowercase) > 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__(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
        a__:     Any										=	retrieval_workers
        if len(self.retrieval_workers) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(lowercase    ,			lowercase    ,			lowercase    ,			lowercase)
                  for worker in self.retrieval_workers
              ])



      def   lowerCamelCase_   (						self)	->			Any:





        '''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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        if len(self.retrieval_workers) > 0:
          # Select a random retrieval actor.
          a__:     int										=	self.retrieval_workers[random.randint(0    ,			len(self.retrieval_workers) - 1)]
          a__							,    a__:     List[Any]										=	ray.get(random_worker.retrieve.remote(lowercase    ,			lowercase))
        else:
          a__							,    a__:     Dict										=	self._main_retrieve(lowercase    ,			lowercase)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





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



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	kwargs.pop('config'    ,			lowercase) or RagConfig.from_pretrained(lowercase    ,			**lowercase)
        a__:     Union[str, Any]										=	RagTokenizer.from_pretrained(lowercase    ,			config=lowercase)
        a__:     int										=	rag_tokenizer.question_encoder
        a__:     Any										=	rag_tokenizer.generator
        if indexed_dataset is not None:
          a__:     List[Any]										=	'custom'
          a__:     Optional[Any]										=	CustomHFIndex(config.retrieval_vector_size    ,			lowercase)
        else:
          a__:     Dict										=	cls._build_index(lowercase)
        return cls(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			retrieval_workers=lowercase    ,			index=lowercase    ,			)


