



"""simple docstring"""


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,
)


snake_case_								=			logging.getLogger(__name__)
torch.set_grad_enabled(False)
snake_case_								=			"""cuda""" if torch.cuda.is_available() else """cpu"""

def 					_lowerCAmelCase (  lowercase_						,      lowercase_=100						,      lowercase_=" "		):
						UpperCAmelCase			 =					text.split(lowercase_		)
						return [character.join(text[i : i + n]		).strip() for i in range(0						,      len(lowercase_		)						,      lowercase_		)]

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase ,       UpperCAmelCase			 =					[], []
						for title, text in zip(documents['title']						,      documents['text']		):
												if text is not None:
																		for passage in split_text(lowercase_		):
																								titles.append(title if title is not None else ''		)
																								texts.append(lowercase_		)
						return {"title": titles, "text": texts}

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					ctx_tokenizer(
						    documents['title']						,      documents['text']						,      truncation=lowercase_						,      padding='longest'						,      return_tensors='pt'		)['input_ids']
						UpperCAmelCase			 =					ctx_encoder(input_ids.to(device=lowercase_		)						,      return_dict=lowercase_		).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      ):
						######################################
						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
						UpperCAmelCase			 =					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
						UpperCAmelCase			 =					dataset.map(lowercase_						,      batched=lowercase_						,      num_proc=processing_args.num_proc		)

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

						# And finally save your dataset
						UpperCAmelCase			 =					os.path.join(rag_example_args.output_dir						,      'my_knowledge_dataset'		)
						dataset.save_to_disk(lowercase_		)
						# 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
						UpperCAmelCase			 =					faiss.IndexHNSWFlat(index_hnsw_args.d						,      index_hnsw_args.m						,      faiss.METRIC_INNER_PRODUCT		)
						dataset.add_faiss_index('embeddings'						,      custom_index=lowercase_		)

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

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

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

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







@dataclass
class  A_     :



						"""simple docstring"""
						__UpperCamelCase													=				field(
						    default=str(Path(SCREAMING_SNAKE_CASE_							).parent / """test_data""" / """my_knowledge_dataset.csv"""							)      ,  metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}      ,  )
						__UpperCamelCase													=				field(
						    default=SCREAMING_SNAKE_CASE_      ,  metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}      ,  )
						__UpperCamelCase													=				field(
						    default="""facebook/rag-sequence-nq"""      ,  metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}      ,  )
						__UpperCamelCase													=				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'"""
						        )
						    }      ,  )
						__UpperCamelCase													=				field(
						    default=SCREAMING_SNAKE_CASE_      ,  metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}      ,  )







@dataclass
class  A_     :



						"""simple docstring"""
						__UpperCamelCase													=				field(
						    default=SCREAMING_SNAKE_CASE_      ,  metadata={
						        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
						    }      ,  )
						__UpperCamelCase													=				field(
						    default=16      ,  metadata={
						        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
						    }      ,  )







@dataclass
class  A_     :



						"""simple docstring"""
						__UpperCamelCase													=				field(
						    default=7_68      ,  metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}      ,  )
						__UpperCamelCase													=				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)

				snake_case_								=			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
				snake_case_	,  snake_case_	,  snake_case_								=			parser.parse_args_into_dataclasses()
				with TemporaryDirectory() as tmp_dir:
								snake_case_								=			rag_example_args.output_dir or tmp_dir
								main(rag_example_args, processing_args, index_hnsw_args)










"""simple docstring"""


import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    )							->     Union[str, Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     PIL.Image.Image:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[List[PIL.Image.Image], PIL.Image.Image]   )							->     np.ndarray:
												if not isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[images]
												UpperCAmelCase			 =					[np.array(lowercase_   ).astype(np.floataa   ) / 255.0 for image in images]
												UpperCAmelCase			 =					np.stack(lowercase_  ,    axis=0   )

												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     torch.FloatTensor:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[..., None]

												UpperCAmelCase			 =					torch.from_numpy(images.transpose(0  ,    3  ,    1  ,    2   )   )
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:torch.FloatTensor   )							->     np.ndarray:
												UpperCAmelCase			 =					images.cpu().permute(0  ,    2  ,    3  ,    1   ).float().numpy()
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:List[Any]   )							->     Optional[int]:
												return 2.0 * images - 1.0






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str   )							->     str:
												return (images / 2 + 0.5).clamp(0  ,    1   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:PIL.Image.Image   )							->     PIL.Image.Image:
												UpperCAmelCase			 =					image.convert('RGB'   )
												return image






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:PIL.Image.Image  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     PIL.Image.Image:
												if height is None:
																		UpperCAmelCase			 =					image.height
												if width is None:
																		UpperCAmelCase			 =					image.width

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    x - x % self.config.vae_scale_factor for x in (width, height)
												)  # resize to integer multiple of vae_scale_factor
												UpperCAmelCase			 =					image.resize((width, height)  ,    resample=PIL_INTERPOLATION[self.config.resample]   )
												return image






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     torch.Tensor:
												UpperCAmelCase			 =					(PIL.Image.Image, np.ndarray, torch.Tensor)
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[image]
												elif not (isinstance(lowercase_  ,    lowercase_   ) and all(isinstance(lowercase_  ,    lowercase_   ) for i in image   )):
																		raise ValueError(
																		    f"""Input is in incorrect format: {[type(lowercase_   ) for i in image]}. Currently, we only support {', '.join(lowercase_   )}"""   )

												if isinstance(image[0]  ,    PIL.Image.Image   ):
																		if self.config.do_convert_rgb:
																								UpperCAmelCase			 =					[self.convert_to_rgb(lowercase_   ) for i in image]
																		if self.config.do_resize:
																								UpperCAmelCase			 =					[self.resize(lowercase_  ,    lowercase_  ,    lowercase_   ) for i in image]
																		UpperCAmelCase			 =					self.pil_to_numpy(lowercase_   )  # to np
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )  # to pt

												elif isinstance(image[0]  ,    np.ndarray   ):
																		UpperCAmelCase			 =					np.concatenate(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else np.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape
																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

												elif isinstance(image[0]  ,    torch.Tensor   ):
																		UpperCAmelCase			 =					torch.cat(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape

																		# don't need any preprocess if the image is latents
																		if channel == 4:
																								return image

																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

        # expected range [0,1], normalize to [-1,1]
												UpperCAmelCase			 =					self.config.do_normalize
												if image.min() < 0:
																		warnings.warn(
																		    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
																		    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""  ,    lowercase_  ,    )
																		UpperCAmelCase			 =					False

												if do_normalize:
																		UpperCAmelCase			 =					self.normalize(lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     List[str]:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if output_type == "latent":
																		return image

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												if output_type == "pt":
																		return image

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		return image

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Union[str, Any]  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    )							->     List[Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Optional[int]   )							->     Optional[Any]:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(image[:, :, :3]   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Any   )							->     Union[str, Any]:
												return image[:, :, 1] * 2**8 + image[:, :, 2]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:int   )							->     Tuple:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					images[:, :, :, 3:]
												if images.shape[-1] == 6:
																		UpperCAmelCase			 =					(images_depth * 2_55).round().astype('uint8'   )
																		UpperCAmelCase			 =					[
																		    Image.fromarray(self.rgblike_to_depthmap(lowercase_   )  ,    mode='I;16'   ) for image_depth in images_depth
																		]
												elif images.shape[-1] == 4:
																		UpperCAmelCase			 =					(images_depth * 6_5535.0).astype(np.uintaa   )
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_  ,    mode='I;16'   ) for image_depth in images_depth]
												else:
																		raise Exception('Not supported'   )

												return pil_images






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     Dict:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		if image.shape[-1] == 6:
																								UpperCAmelCase			 =					np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]  ,    axis=0   )
																		else:
																								UpperCAmelCase			 =					image[:, :, :, 3:]
																		return image[:, :, :, :3], image_depth

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   ), self.numpy_to_depth(lowercase_   )
												else:
																		raise Exception(f"""This type {output_type} is not supported"""   )





