




import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=1_00					,						SCREAMING_SNAKE_CASE=" "   ) ->       str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	text.split(SCREAMING_SNAKE_CASE   )
			return [character.join(text[i : i + n]   ).strip() for i in range(0					,						len(SCREAMING_SNAKE_CASE   )					,						SCREAMING_SNAKE_CASE   )]

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







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

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	load_dataset(
			    'csv'					,						data_files=[csv_path]					,						split='train'					,						delimiter='\t'					,						column_names=['title', 'text']   )
			SCREAMING_SNAKE_CASE_			=	kb_dataset.map(
			    SCREAMING_SNAKE_CASE					,						batched=SCREAMING_SNAKE_CASE					,						num_proc=1   )  # if you want you can load already splitted csv.
			SCREAMING_SNAKE_CASE_			=	[kb_dataset.shard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						contiguous=SCREAMING_SNAKE_CASE   ) for i in range(SCREAMING_SNAKE_CASE   )]
			SCREAMING_SNAKE_CASE_			=	kb_list[process_num]

			SCREAMING_SNAKE_CASE_			=	'data_' + str(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	os.path.join(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE_			=	DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'   )
			SCREAMING_SNAKE_CASE_			=	ctx_encoder.to(device=SCREAMING_SNAKE_CASE   )

			def embed(
			    SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) -> dict:
						SCREAMING_SNAKE_CASE_			=	ctx_tokenizer(
						    documents['title']					,						documents['text']					,						truncation=SCREAMING_SNAKE_CASE					,						padding='longest'					,						return_tensors='pt'   )['input_ids']
						SCREAMING_SNAKE_CASE_			=	ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE   )					,						return_dict=SCREAMING_SNAKE_CASE   ).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}

			SCREAMING_SNAKE_CASE_			=	Features(
			    {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space

			SCREAMING_SNAKE_CASE_			=	data_shrad.map(
			    partial(SCREAMING_SNAKE_CASE					,						ctx_encoder=SCREAMING_SNAKE_CASE					,						ctx_tokenizer=SCREAMING_SNAKE_CASE					,						device=SCREAMING_SNAKE_CASE   )					,						batched=SCREAMING_SNAKE_CASE					,						batch_size=16					,						features=SCREAMING_SNAKE_CASE					,						)
			dataset.save_to_disk(SCREAMING_SNAKE_CASE   )

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	[]

			for shard_address in glob(str(SCREAMING_SNAKE_CASE   ) + '/*/'   ):
						data_shard_list.append(load_from_disk(SCREAMING_SNAKE_CASE   )   )

			SCREAMING_SNAKE_CASE_			=	concatenate_datasets(SCREAMING_SNAKE_CASE   )
			faiss.omp_set_num_threads(96   )

			SCREAMING_SNAKE_CASE_			=	faiss.IndexHNSWFlat(7_68					,						1_28					,						faiss.METRIC_INNER_PRODUCT   )
			concat.add_faiss_index('embeddings'					,						custom_index=SCREAMING_SNAKE_CASE   )
			concat.get_index('embeddings'   ).save(
			    SCREAMING_SNAKE_CASE   )  # since we load the index in to memory,we can directly update the index in the disk






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__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[images]
				SCREAMING_SNAKE_CASE_			=	[np.array(SCREAMING_SNAKE_CASE			).astype(np.floataa			) / 2_5_5.0 for image in images]
				SCREAMING_SNAKE_CASE_			=	np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)

				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[..., None]

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





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	images.cpu().permute(0			,    2			,    3			,    1			).float().numpy()
				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				str:




				"""simple docstring"""



				return 2.0 * images - 1.0





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				return (images / 2 + 0.5).clamp(0			,    1			)





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	image.convert('RGB'			)
				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				PIL.Image.Image:




				"""simple docstring"""



				if height is None:
							SCREAMING_SNAKE_CASE_			=	image.height
				if width is None:
							SCREAMING_SNAKE_CASE_			=	image.width

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





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				torch.Tensor:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	(PIL.Image.Image, np.ndarray, torch.Tensor)
				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[image]
				elif not (isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) and all(isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image			)):
							raise ValueError(
							    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE			) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE			)}'			)

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

				elif isinstance(image[0]			,    np.ndarray			):
							SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	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			):
							SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	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]
				SCREAMING_SNAKE_CASE_			=	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()}]'			,    SCREAMING_SNAKE_CASE			,    )
							SCREAMING_SNAKE_CASE_			=	False

				if do_normalize:
							SCREAMING_SNAKE_CASE_			=	self.normalize(SCREAMING_SNAKE_CASE			)

				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Any:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    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'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

				if output_type == "latent":
							return image

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

				SCREAMING_SNAKE_CASE_			=	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

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

				if output_type == "np":
							return image

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




class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image[:, :, :3]			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:




				"""simple docstring"""



				return image[:, :, 1] * 2**8 + image[:, :, 2]





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	images[:, :, :, 3:]
				if images.shape[-1] == 6:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 255).round().astype('uint8'			)
							SCREAMING_SNAKE_CASE_			=	[
							    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE			)			,    mode='I;16'			) for image_depth in images_depth
							]
				elif images.shape[-1] == 4:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			,    mode='I;16'			) for image_depth in images_depth]
				else:
							raise Exception('Not supported'			)

				return pil_images





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Union[str, Any]:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    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'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

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

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

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

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

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