


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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__magic_name__       					=      logging.get_logger(__name__)





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		int  					=	['''pixel_values''']




						def __init__(   self				,							a_ = True				,							a_ = None				,							a_ = PIL.Image.NEAREST				,							a_ = False				,							a_ = None				,							a_ = 1 / 255				,							a_ = False				,							a_ = True				,							a_ = True				,							a_ = None				,							a_ = None				,							a_ = True				,							**a_				,							):
							super().__init__(**a_     )
							lowerCamelCase_			:							Dict      =       size if size is not None else {"height": 346, "width": 346}
							lowerCamelCase_			:							Union[str, Any]      =       get_size_dict(a_     )
							lowerCamelCase_			:							Tuple      =       crop_size if crop_size is not None else {"height": 289, "width": 289}
							lowerCamelCase_			:							Union[str, Any]      =       get_size_dict(a_				,							param_name="crop_size"     )

							lowerCamelCase_			:							Any      =       do_resize
							lowerCamelCase_			:							Optional[Any]      =       size
							lowerCamelCase_			:							str      =       resample
							lowerCamelCase_			:							Any      =       do_center_crop
							lowerCamelCase_			:							Any      =       crop_size
							lowerCamelCase_			:							Dict      =       do_rescale
							lowerCamelCase_			:							str      =       rescale_factor
							lowerCamelCase_			:							str      =       rescale_offset
							lowerCamelCase_			:							List[Any]      =       do_normalize
							lowerCamelCase_			:							Optional[Any]      =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase_			:							Tuple      =       image_std if image_std is not None else IMAGENET_STANDARD_STD
							lowerCamelCase_			:							Optional[Any]      =       include_top




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_ = PIL.Image.NEAREST				,							a_ = None				,							**a_				,							):
							lowerCamelCase_			:							Union[str, Any]      =       get_size_dict(a_     )
							if "height" not in size or "width" not in size:
								raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
							return resize(
							    a_				,							size=(size["height"], size["width"])				,							resample=a_				,							data_format=a_				,							**a_     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_ = None				,							**a_				,							):
							lowerCamelCase_			:							Dict      =       get_size_dict(a_     )
							if "height" not in size or "width" not in size:
								raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
							return center_crop(a_				,							size=(size["height"], size["width"])				,							data_format=a_				,							**a_     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_ = True				,							a_ = None				,							**a_				,							):
							if offset:
								lowerCamelCase_			:							Optional[int]      =       (image - 1_27.5) * scale
								if data_format is not None:
									lowerCamelCase_			:							Dict      =       to_channel_dimension_format(a_				,							a_     )
								lowerCamelCase_			:							Optional[Any]      =       rescaled_image.astype(np.floataa     )
							else:
								lowerCamelCase_			:							Optional[int]      =       rescale(a_				,							scale=a_				,							data_format=a_				,							**a_     )
							return rescaled_image




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_ = None				,							**a_				,							):
							return normalize(a_				,							mean=a_				,							std=a_				,							data_format=a_				,							**a_     )




						def 					_UpperCamelCase			(   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_ = None				,							a_ = None				,							a_ = ChannelDimension.FIRST				,							**a_				,							):
							lowerCamelCase_			:							Optional[Any]      =       do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_			:							Any      =       resample if resample is not None else self.resample
							lowerCamelCase_			:							Optional[int]      =       do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase_			:							List[str]      =       do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_			:							str      =       rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_			:							int      =       rescale_offset if rescale_offset is not None else self.rescale_offset
							lowerCamelCase_			:							Optional[int]      =       do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_			:							Tuple      =       image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_			:							List[str]      =       image_std if image_std is not None else self.image_std
							lowerCamelCase_			:							Dict      =       include_top if include_top is not None else self.include_top

							lowerCamelCase_			:							int      =       size if size is not None else self.size
							lowerCamelCase_			:							Any      =       get_size_dict(a_     )
							lowerCamelCase_			:							str      =       crop_size if crop_size is not None else self.crop_size
							lowerCamelCase_			:							int      =       get_size_dict(a_				,							param_name="crop_size"     )

							lowerCamelCase_			:							Optional[int]      =       make_list_of_images(a_     )

							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 or resample is None:
								raise ValueError("Size and resample 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."     )

							if do_normalize and (image_mean is None or image_std is None):
								raise ValueError("Image mean and std must be specified if do_normalize is True."     )

							# All transformations expect numpy arrays.
							lowerCamelCase_			:							Tuple      =       [to_numpy_array(a_     ) for image in images]

							if do_resize:
								lowerCamelCase_			:							str      =       [self.resize(image=a_				,							size=a_				,							resample=a_     ) for image in images]

							if do_center_crop:
								lowerCamelCase_			:							int      =       [self.center_crop(image=a_				,							size=a_     ) for image in images]

							if do_rescale:
								lowerCamelCase_			:							int      =       [self.rescale(image=a_				,							scale=a_				,							offset=a_     ) for image in images]

							if do_normalize:
								lowerCamelCase_			:							int      =       [self.normalize(image=a_				,							mean=a_				,							std=a_     ) for image in images]

							if include_top:
								lowerCamelCase_			:							Optional[Any]      =       [self.normalize(image=a_				,							mean=[0, 0, 0]				,							std=a_     ) for image in images]

							lowerCamelCase_			:							Union[str, Any]      =       [to_channel_dimension_format(a_				,							a_     ) for image in images]

							lowerCamelCase_			:							List[Any]      =       {"pixel_values": images}
							return BatchFeature(data=a_				,							tensor_type=a_     )





import logging
import random

import ray

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


__magic_name__       					=      logging.getLogger(__name__)





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self     ):
							lowerCamelCase_			:							List[Any]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							if not self.initialized:
								lowerCamelCase_			:							Optional[int]      =       RagRetriever(
								    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)
								lowerCamelCase_			:							Any      =       True




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




						def 					_UpperCamelCase			(   self     ):
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							lowerCamelCase_			:							Optional[int]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       self.retriever._main_retrieve(a_				,							a_     )
							lowerCamelCase_			:							str      =       self.retriever.index.get_doc_dicts(a_     )
							return doc_ids, retrieved_doc_embeds, doc_dicts





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						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_				,							)

							lowerCamelCase_			:							List[str]      =       retrieval_workers
							lowerCamelCase_			:							str      =       question_encoder_tokenizer
							lowerCamelCase_			:							Optional[int]      =       generator_tokenizer
							if len(self.retrieval_workers     ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(a_				,							a_				,							a_				,							a_     )
								        for worker in self.retrieval_workers
								    ]     )




						def 					_UpperCamelCase			(   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 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if len(self.retrieval_workers     ) > 0:
								# Select a random retrieval actor.
								lowerCamelCase_			:							List[Any]      =       self.retrieval_workers[random.randint(0				,							len(self.retrieval_workers     ) - 1     )]
								lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							str      =       ray.get(
								    random_worker.retrieve.remote(a_				,							a_     )     )
							else:
								lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       self._main_retrieve(a_				,							a_     )
								lowerCamelCase_			:							Any      =       self.index.get_doc_dicts(a_     )
							return retrieved_doc_embeds, doc_ids, doc_dicts




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_=None				,							**a_     ):
							return super(a_				,							cls     ).get_tokenizers(a_				,							a_				,							**a_     )




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_				,							a_=None				,							**a_     ):
							lowerCamelCase_			:							str      =       kwargs.pop("config"				,							a_     ) or RagConfig.from_pretrained(a_				,							**a_     )
							lowerCamelCase_			:							Tuple      =       RagTokenizer.from_pretrained(a_				,							config=a_     )
							lowerCamelCase_			:							str      =       rag_tokenizer.question_encoder
							lowerCamelCase_			:							int      =       rag_tokenizer.generator

							if indexed_dataset is not None:
								lowerCamelCase_			:							int      =       "custom"
								lowerCamelCase_			:							List[str]      =       CustomHFIndex(config.retrieval_vector_size				,							a_     )
							else:
								lowerCamelCase_			:							str      =       cls._build_index(a_     )

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




						def 					_UpperCamelCase			(   self     ):
							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
							lowerCamelCase_			:							Optional[int]      =       self._build_index(self.config     )

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

