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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_				:		Dict       						=       logging.get_logger(__name__)


if is_vision_available():
	import PIL


class 						a__							(   __snake_case		):
			A__				:   Optional[Any]								=			['pixel_values']



			def __init__(						self      ,						UpperCAmelCase = True      ,						UpperCAmelCase = None      ,						UpperCAmelCase = PILImageResampling.BICUBIC      ,						UpperCAmelCase = True      ,						UpperCAmelCase = None      ,						UpperCAmelCase = True      ,						UpperCAmelCase = 1 / 2_5_5      ,						UpperCAmelCase = True      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = True      ,						**UpperCAmelCase      ,						) ->    None:
								super().__init__(**UpperCAmelCase    )
								__a							=   size if size is not None else {'shortest_edge': 2_2_4}
								__a							=   get_size_dict(UpperCAmelCase      ,						default_to_square=UpperCAmelCase    )
								__a							=   crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__a							=   get_size_dict(UpperCAmelCase    )

								__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 OPENAI_CLIP_MEAN
								__a							=   image_std if image_std is not None else OPENAI_CLIP_STD
								__a							=   do_convert_rgb



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase = PILImageResampling.BICUBIC      ,						UpperCAmelCase = None      ,						**UpperCAmelCase      ,						) ->    np.ndarray:
								__a							=   get_size_dict(UpperCAmelCase      ,						default_to_square=UpperCAmelCase    )
								__a							=   get_resize_output_image_size(
								    UpperCAmelCase      ,						size=(size['height'], size['width'])      ,						default_to_square=UpperCAmelCase    )
								return resize(UpperCAmelCase      ,						size=UpperCAmelCase      ,						resample=UpperCAmelCase      ,						data_format=UpperCAmelCase      ,						**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase = None      ,						**UpperCAmelCase      ,						) ->    np.ndarray:
								__a							=   get_size_dict(UpperCAmelCase    )
								return center_crop(UpperCAmelCase      ,						size=(size['height'], size['width'])      ,						data_format=UpperCAmelCase      ,						**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase = None      ,						**UpperCAmelCase      ,						) ->    Dict:
								return rescale(UpperCAmelCase      ,						scale=UpperCAmelCase      ,						data_format=UpperCAmelCase      ,						**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase = None      ,						**UpperCAmelCase      ,						) ->    np.ndarray:
								return normalize(UpperCAmelCase      ,						mean=UpperCAmelCase      ,						std=UpperCAmelCase      ,						data_format=UpperCAmelCase      ,						**UpperCAmelCase    )







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = ChannelDimension.FIRST      ,						**UpperCAmelCase      ,						) ->    PIL.Image.Image:
								__a							=   do_resize if do_resize is not None else self.do_resize
								__a							=   size if size is not None else self.size
								__a							=   get_size_dict(UpperCAmelCase      ,						default_to_square=UpperCAmelCase    )
								__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							=   crop_size if crop_size is not None else self.crop_size
								__a							=   get_size_dict(UpperCAmelCase    )
								__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							=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__a							=   make_list_of_images(UpperCAmelCase    )

								if not valid_images(UpperCAmelCase    ):
													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.'    )

								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.'    )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
													__a							=   [convert_to_rgb(UpperCAmelCase    ) for image in images]

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

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

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

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

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

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

								__a							=   {'pixel_values': images}
								return BatchFeature(data=UpperCAmelCase      ,						tensor_type=UpperCAmelCase    )





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase_				:		Union[str, Any]       						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_				:		List[Any]       						=       """cuda""" if torch.cuda.is_available() else """cpu"""
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=100      ,				__lowerCamelCase=" "						):
					__a							=   text.split(__lowerCamelCase						)
					return [character.join(text[i : i + n]						).strip() for i in range(0      ,				len(__lowerCamelCase						)      ,				__lowerCamelCase						)]
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a					,      __a							=   [], []
					for title, text in zip(documents['title']      ,				documents['text']						):
										if text is not None:
															for passage in split_text(__lowerCamelCase						):
																				titles.append(title if title is not None else ''						)
																				texts.append(__lowerCamelCase						)
					return {"title": titles, "text": texts}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   ctx_tokenizer(
					    documents['title']      ,				documents['text']      ,				truncation=__lowerCamelCase      ,				padding='longest'      ,				return_tensors='pt'						)['input_ids']
					__a							=   ctx_encoder(input_ids.to(device=__lowerCamelCase						)      ,				return_dict=__lowerCamelCase						).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				):
					######################################
					logger.info('Step 1 - Create the dataset'						)
					######################################

					# The dataset needed for RAG must have three columns:
					# - title (string): title of the document
					# - text (string): text of a passage of the document
					# - embeddings (array of dimension d): DPR representation of the passage

					# Let's say you have documents in tab-separated csv files with columns "title" and "text"
					assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

					# You can load a Dataset object this way
					__a							=   load_dataset(
					    'csv'      ,				data_files=[rag_example_args.csv_path]      ,				split='train'      ,				delimiter='\t'      ,				column_names=['title', 'text']						)

					# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

					# Then split the documents into passages of 100 words
					__a							=   dataset.map(__lowerCamelCase      ,				batched=__lowerCamelCase      ,				num_proc=processing_args.num_proc						)

					# And compute the embeddings
					__a							=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowerCamelCase						)
					__a							=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
					__a							=   Features(
					    {'text': Value('string'						), 'title': Value('string'						), 'embeddings': Sequence(Value('float32'						)						)}						)  # optional, save as float32 instead of float64 to save space
					__a							=   dataset.map(
					    partial(__lowerCamelCase      ,				ctx_encoder=__lowerCamelCase      ,				ctx_tokenizer=__lowerCamelCase						)      ,				batched=__lowerCamelCase      ,				batch_size=processing_args.batch_size      ,				features=__lowerCamelCase      ,				)

					# And finally save your dataset
					__a							=   os.path.join(rag_example_args.output_dir      ,				'my_knowledge_dataset'						)
					dataset.save_to_disk(__lowerCamelCase						)
					# from datasets import load_from_disk
					# dataset = load_from_disk(passages_path)  # to reload the dataset

					######################################
					logger.info('Step 2 - Index the dataset'						)
					######################################

					# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
					__a							=   faiss.IndexHNSWFlat(index_hnsw_args.d      ,				index_hnsw_args.m      ,				faiss.METRIC_INNER_PRODUCT						)
					dataset.add_faiss_index('embeddings'      ,				custom_index=__lowerCamelCase						)

					# And save the index
					__a							=   os.path.join(rag_example_args.output_dir      ,				'my_knowledge_dataset_hnsw_index.faiss'						)
					dataset.get_index('embeddings'						).save(__lowerCamelCase						)
					# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

					######################################
					logger.info('Step 3 - Load RAG'						)
					######################################

					# Easy way to load the model
					__a							=   RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name      ,				index_name='custom'      ,				indexed_dataset=__lowerCamelCase						)
					__a							=   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,				retriever=__lowerCamelCase						)
					__a							=   RagTokenizer.from_pretrained(rag_example_args.rag_model_name						)

					# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
					# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

					######################################
					logger.info('Step 4 - Have fun'						)
					######################################

					__a							=   rag_example_args.question or 'What does Moses\' rod turn into ?'
					__a							=   tokenizer.question_encoder(__lowerCamelCase      ,				return_tensors='pt'						)['input_ids']
					__a							=   model.generate(__lowerCamelCase						)
					__a							=   tokenizer.batch_decode(__lowerCamelCase      ,				skip_special_tokens=__lowerCamelCase						)[0]
					logger.info('Q: ' + question						)
					logger.info('A: ' + generated_string						)


@dataclass
class 						a__							:
			A__				:   str								=			field(
			    default=str(Path(__snake_case		).parent / 'test_data' / 'my_knowledge_dataset.csv'		)      ,						metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}      ,						)
			A__				:   str								=			field(
			    default='facebook/rag-sequence-nq'      ,						metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}      ,						)
			A__				:   str								=			field(
			    default='facebook/dpr-ctx_encoder-multiset-base'      ,						metadata={
			        'help': (
			            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
			            ' \'facebook/dpr-ctx_encoder-multiset-base\''
			        )
			    }      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}      ,						)






@dataclass
class 						a__							:
			A__				:   Optional[int]								=			field(
			    default=__snake_case      ,						metadata={
			        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
			    }      ,						)
			A__				:   int								=			field(
			    default=16      ,						metadata={
			        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
			    }      ,						)






@dataclass
class 						a__							:
			A__				:   int								=			field(
			    default=7_68      ,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}      ,						)
			A__				:   int								=			field(
			    default=1_28      ,						metadata={
			        'help': (
			            'The number of bi-directional links created for every new element during the HNSW index construction.'
			        )
			    }      ,						)


if __name__ == "__main__":
	logging.basicConfig(level=logging.WARNING)
	logger.setLevel(logging.INFO)

	lowerCamelCase_				:		Tuple       						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_				:		Union[str, Any]       						=       parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		lowerCamelCase_				:		Union[str, Any]       						=       rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)




