





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_A:					Union[str, Any]							=				logging.get_logger(__name__)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			int						   =       ["""pixel_values"""]



    def __init__(  self     ,			__A = True     ,			__A = None     ,			__A = PILImageResampling.BICUBIC     ,			__A = True     ,			__A = True     ,			__A = 1 / 255     ,			__A = None     ,			__A = True     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     super().__init__(**__A					)
     __UpperCAmelCase					=   size if size is not None else {'height': 224, 'width': 224}
     __UpperCAmelCase					=   get_size_dict(__A					)
     __UpperCAmelCase					=   crop_size if crop_size is not None else {'height': 224, 'width': 224}
     __UpperCAmelCase					=   get_size_dict(__A     ,			default_to_square=__A     ,			param_name='crop_size'					)

     __UpperCAmelCase					=   do_resize
     __UpperCAmelCase					=   do_rescale
     __UpperCAmelCase					=   do_normalize
     __UpperCAmelCase					=   do_center_crop
     __UpperCAmelCase					=   crop_size
     __UpperCAmelCase					=   size
     __UpperCAmelCase					=   resample
     __UpperCAmelCase					=   rescale_factor
     __UpperCAmelCase					=   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     __UpperCAmelCase					=   image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A = PILImageResampling.BILINEAR     ,			__A = None     ,			**__A     ,			):
     __UpperCAmelCase					=   get_size_dict(__A					)

     if "shortest_edge" in size:
      __UpperCAmelCase					=   get_resize_output_image_size(__A     ,			size=size['shortest_edge']     ,			default_to_square=__A					)
      # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
     elif "height" in size and "width" in size:
      __UpperCAmelCase					=   (size['height'], size['width'])
     else:
      raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'					)
     return resize(__A     ,			size=__A     ,			resample=__A     ,			data_format=__A     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A = None     ,			**__A     ,			):
     __UpperCAmelCase					=   get_size_dict(__A					)
     if "height" not in size or "width" not in size:
      raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'					)
     return center_crop(__A     ,			size=(size['height'], size['width'])     ,			data_format=__A     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A = None     ,			**__A					):
     return rescale(__A     ,			scale=__A     ,			data_format=__A     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A = None     ,			**__A     ,			):
     return normalize(__A     ,			mean=__A     ,			std=__A     ,			data_format=__A     ,			**__A					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = ChannelDimension.FIRST     ,			**__A     ,			):
     __UpperCAmelCase					=   do_resize if do_resize is not None else self.do_resize
     __UpperCAmelCase					=   do_rescale if do_rescale is not None else self.do_rescale
     __UpperCAmelCase					=   do_normalize if do_normalize is not None else self.do_normalize
     __UpperCAmelCase					=   do_center_crop if do_center_crop is not None else self.do_center_crop
     __UpperCAmelCase					=   crop_size if crop_size is not None else self.crop_size
     __UpperCAmelCase					=   get_size_dict(__A     ,			param_name='crop_size'     ,			default_to_square=__A					)
     __UpperCAmelCase					=   resample if resample is not None else self.resample
     __UpperCAmelCase					=   rescale_factor if rescale_factor is not None else self.rescale_factor
     __UpperCAmelCase					=   image_mean if image_mean is not None else self.image_mean
     __UpperCAmelCase					=   image_std if image_std is not None else self.image_std

     __UpperCAmelCase					=   size if size is not None else self.size
     __UpperCAmelCase					=   get_size_dict(__A					)

     if not is_batched(__A					):
      __UpperCAmelCase					=   [images]

     if not valid_images(__A					):
      raise ValueError(
          'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
          'torch.Tensor, tf.Tensor or jax.ndarray.'					)

     if do_resize and size is None:
      raise ValueError('Size must be specified if do_resize is True.'					)

     if do_center_crop and crop_size is None:
      raise ValueError('Crop size must be specified if do_center_crop is True.'					)

     if do_rescale and rescale_factor is None:
      raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

     # All transformations expect numpy arrays.
     __UpperCAmelCase					=   [to_numpy_array(__A					) for image in images]

     if do_resize:
      __UpperCAmelCase					=   [self.resize(image=__A     ,			size=__A     ,			resample=__A					) for image in images]

     if do_center_crop:
      __UpperCAmelCase					=   [self.center_crop(image=__A     ,			size=__A					) for image in images]

     if do_rescale:
      __UpperCAmelCase					=   [self.rescale(image=__A     ,			scale=__A					) for image in images]

     if do_normalize:
      __UpperCAmelCase					=   [self.normalize(image=__A     ,			mean=__A     ,			std=__A					) for image in images]

     __UpperCAmelCase					=   [to_channel_dimension_format(__A     ,			__A					) for image in images]

     __UpperCAmelCase					=   {'pixel_values': images}
     return BatchFeature(data=__A     ,			tensor_type=__A					)












'''simple docstring'''




import logging
import random

import ray

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


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



    def 					__lowerCamelCase						(  self					):
     self.retriever.index.init_index()




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



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



    def 					__lowerCamelCase						(  self					):
     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     ,			__A     ,			__A					):
     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					=   ray.get(random_worker.retrieve.remote(__A     ,			__A					)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._main_retrieve(__A     ,			__A					)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A					)



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('config'     ,			__A					) or RagConfig.from_pretrained(__A     ,			**__A					)
     __UpperCAmelCase					=   RagTokenizer.from_pretrained(__A     ,			config=__A					)
     __UpperCAmelCase					=   rag_tokenizer.question_encoder
     __UpperCAmelCase					=   rag_tokenizer.generator
     if indexed_dataset is not None:
      __UpperCAmelCase					=   'custom'
      __UpperCAmelCase					=   CustomHFIndex(config.retrieval_vector_size     ,			__A					)
     else:
      __UpperCAmelCase					=   cls._build_index(__A					)
     return cls(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			retrieval_workers=__A     ,			index=__A     ,			)





