


import logging
import random

import ray

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


snake_case							:       Any     			=      logging.getLogger(__name__)







class        snake_case_   :
					def __init__(      self   :int      )					->    str:
												a__             =     False
					def  lowerCamelCase__(      self   :Optional[Any]			,__snake_case   :List[str]			,__snake_case   :Dict			,__snake_case   :int			,__snake_case   :Any      )					->    int:
												if not self.initialized:
																			a__             =     RagRetriever(
																			    __snake_case			,question_encoder_tokenizer=__snake_case			,generator_tokenizer=__snake_case			,index=__snake_case			,init_retrieval=__snake_case			,)
																			a__             =     True
					def  lowerCamelCase__(      self   :int      )					->    Optional[int]:
												self.retriever.index.init_index()



					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :Dict			,__snake_case   :int      )					->    str:
												a__      ,					a__             =     self.retriever._main_retrieve(__snake_case			,__snake_case      )
												return doc_ids, retrieved_doc_embeds







class        snake_case_   (lowerCamelCase_      ):
					def __init__(      self   :List[str]			,__snake_case   :List[str]			,__snake_case   :Optional[Any]			,__snake_case   :List[str]			,__snake_case   :str			,__snake_case   :Any=None      )					->    int:
												if index is not None and index.is_initialized() and len(__snake_case      ) > 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__(
												    __snake_case			,question_encoder_tokenizer=__snake_case			,generator_tokenizer=__snake_case			,index=__snake_case			,init_retrieval=__snake_case			,)
												a__             =     retrieval_workers
												if len(self.retrieval_workers      ) > 0:
																			ray.get(
																			    [
																			        worker.create_rag_retriever.remote(__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			        for worker in self.retrieval_workers
																			    ]      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[int]:
												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   :int			,__snake_case   :List[Any]			,__snake_case   :str      )					->    List[str]:
												if len(self.retrieval_workers      ) > 0:
																			# Select a random retrieval actor.
																			a__             =     self.retrieval_workers[random.randint(0			,len(self.retrieval_workers      ) - 1      )]
																			a__      ,					a__             =     ray.get(random_worker.retrieve.remote(__snake_case			,__snake_case      )      )
												else:
																			a__      ,					a__             =     self._main_retrieve(__snake_case			,__snake_case      )
												return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Any			,__snake_case   :List[Any]			,__snake_case   :Any=None			,**__snake_case   :Optional[int]      )					->    Optional[Any]:
												return super(__snake_case			,cls      ).get_tokenizers(__snake_case			,__snake_case			,**__snake_case      )



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








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


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







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			['''pixel_values''']
					def __init__(      self   :Optional[int]			,__snake_case   :bool = True			,__snake_case   :Optional[Dict[str, int]] = None			,__snake_case   :PILImageResampling = PILImageResampling.BICUBIC			,__snake_case   :bool = True			,__snake_case   :bool = True			,__snake_case   :Union[int, float] = 1 / 2_55			,__snake_case   :Dict[str, int] = None			,__snake_case   :bool = True			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,**__snake_case   :Dict			,)					->    None:
												super().__init__(**__snake_case      )
												a__             =     size if size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case      )
												a__             =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case			,default_to_square=__snake_case			,param_name='crop_size'      )

												a__             =     do_resize
												a__             =     do_rescale
												a__             =     do_normalize
												a__             =     do_center_crop
												a__             =     crop_size
												a__             =     size
												a__             =     resample
												a__             =     rescale_factor
												a__             =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__             =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :PILImageResampling = PILImageResampling.BILINEAR			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :List[Any]			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )

												if "shortest_edge" in size:
																			a__             =     get_resize_output_image_size(__snake_case			,size=size['shortest_edge']			,default_to_square=__snake_case      )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__             =     (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
												return resize(__snake_case			,size=__snake_case			,resample=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )
												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(__snake_case			,size=(size['height'], size['width'])			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :np.ndarray			,__snake_case   :float			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :int      )					->    np.ndarray:
												return rescale(__snake_case			,scale=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.ndarray			,__snake_case   :Union[float, List[float]]			,__snake_case   :Union[float, List[float]]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												return normalize(__snake_case			,mean=__snake_case			,std=__snake_case			,data_format=__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :Any			,__snake_case   :ImageInput			,__snake_case   :Optional[bool] = None			,__snake_case   :Dict[str, int] = None			,__snake_case   :PILImageResampling = None			,__snake_case   :bool = None			,__snake_case   :int = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[float] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Union[str, ChannelDimension] = ChannelDimension.FIRST			,**__snake_case   :Optional[int]			,)					->    BatchFeature:
												a__             =     do_resize if do_resize is not None else self.do_resize
												a__             =     do_rescale if do_rescale is not None else self.do_rescale
												a__             =     do_normalize if do_normalize is not None else self.do_normalize
												a__             =     do_center_crop if do_center_crop is not None else self.do_center_crop
												a__             =     crop_size if crop_size is not None else self.crop_size
												a__             =     get_size_dict(__snake_case			,param_name='crop_size'			,default_to_square=__snake_case      )
												a__             =     resample if resample is not None else self.resample
												a__             =     rescale_factor if rescale_factor is not None else self.rescale_factor
												a__             =     image_mean if image_mean is not None else self.image_mean
												a__             =     image_std if image_std is not None else self.image_std

												a__             =     size if size is not None else self.size
												a__             =     get_size_dict(__snake_case      )

												if not is_batched(__snake_case      ):
																			a__             =     [images]

												if not valid_images(__snake_case      ):
																			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.
												a__             =     [to_numpy_array(__snake_case      ) for image in images]

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

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

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

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

												a__             =     [to_channel_dimension_format(__snake_case			,__snake_case      ) for image in images]

												a__             =     {'pixel_values': images}
												return BatchFeature(data=__snake_case			,tensor_type=__snake_case      )




