



import logging
import random

import ray

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


lowerCamelCase__			= logging.getLogger(__name__)






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		False





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Any      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Any	):

											'''simple docstring'''
											if not self.initialized:
															__a       =		RagRetriever(
															    __lowercase      ,				question_encoder_tokenizer=__lowercase      ,				generator_tokenizer=__lowercase      ,				index=__lowercase      ,				init_retrieval=__lowercase      ,				)
															__a       =		True





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											self.retriever.index.init_index()







							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											__a     ,					__a       =		self.retriever._main_retrieve(__lowercase      ,				__lowercase	)
											return doc_ids, retrieved_doc_embeds






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def __init__(   self		:		Dict      ,				__lowercase		:		Tuple      ,				__lowercase		:		Tuple      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Any      ,				__lowercase		:		Tuple=None	):

											'''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       =		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 						UpperCamelCase_					(   self		:		List[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 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											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(__lowercase      ,				__lowercase	)	)
											else:
															__a     ,					__a       =		self._main_retrieve(__lowercase      ,				__lowercase	)
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowercase	)





							@classmethod
							def 						UpperCamelCase_					(   cls		:		Union[str, Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		str=None      ,				**__lowercase		:		str	):

											'''simple docstring'''
											return super(__lowercase      ,				cls	).get_tokenizers(__lowercase      ,				__lowercase      ,				**__lowercase	)







							@classmethod
							def 						UpperCamelCase_					(   cls		:		Any      ,				__lowercase		:		List[str]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		List[Any]=None      ,				**__lowercase		:		Tuple	):

											'''simple docstring'''
											__a       =		kwargs.pop("""config"""      ,				__lowercase	) or RagConfig.from_pretrained(__lowercase      ,				**__lowercase	)
											__a       =		RagTokenizer.from_pretrained(__lowercase      ,				config=__lowercase	)
											__a       =		rag_tokenizer.question_encoder
											__a       =		rag_tokenizer.generator
											if indexed_dataset is not None:
															__a       =		"""custom"""
															__a       =		CustomHFIndex(config.retrieval_vector_size      ,				__lowercase	)
											else:
															__a       =		cls._build_index(__lowercase	)
											return cls(
											    __lowercase      ,				question_encoder_tokenizer=__lowercase      ,				generator_tokenizer=__lowercase      ,				retrieval_workers=__lowercase      ,				index=__lowercase      ,				)





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


lowerCamelCase__			= logging.get_logger(__name__)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    =['pixel_values']





							def __init__(   self		:		str      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Union[int, float] = 1 / 255      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)
											__a       =		size if size is not None else {"""height""": 256, """width""": 256}
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		do_resize
											__a       =		size
											__a       =		resample
											__a       =		do_center_crop
											__a       =		crop_size
											__a       =		do_rescale
											__a       =		rescale_factor
											__a       =		do_normalize
											__a       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__a       =		image_std if image_std is not None else IMAGENET_STANDARD_STD





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		Union[str, Any]      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											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(
											    __lowercase      ,				size=(size["""height"""], size["""width"""])      ,				resample=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											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(__lowercase      ,				size=(size["""height"""], size["""width"""])      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[int, float]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		List[str]      ,				):

											'''simple docstring'''
											return rescale(__lowercase      ,				scale=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											return normalize(__lowercase      ,				mean=__lowercase      ,				std=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)







							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		ImageInput      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Dict=None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		float = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[str, TensorType]] = None      ,				__lowercase		:		ChannelDimension = ChannelDimension.FIRST      ,				**__lowercase		:		Optional[Any]      ,				):

											'''simple docstring'''
											__a       =		do_resize if do_resize is not None else self.do_resize
											__a       =		resample if resample is not None else self.resample
											__a       =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__a       =		do_rescale if do_rescale is not None else self.do_rescale
											__a       =		rescale_factor if rescale_factor is not None else self.rescale_factor
											__a       =		do_normalize if do_normalize is not None else self.do_normalize
											__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(__lowercase	)
											__a       =		crop_size if crop_size is not None else self.crop_size
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		make_list_of_images(__lowercase	)

											if not valid_images(__lowercase	):
															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.
											__a       =		[to_numpy_array(__lowercase	) for image in images]

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

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

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

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

											__a       =		[to_channel_dimension_format(__lowercase      ,				__lowercase	) for image in images]

											__a       =		{"""pixel_values""": images}
											return BatchFeature(data=__lowercase      ,				tensor_type=__lowercase	)
