


from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__UpperCamelCase      						=    logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCamelCase      						=    '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'
def     UpperCamelCase_(			_A				:Tuple			,       _A				:int			,       _A				:Tuple=8					)->						Optional[int]:
			UpperCamelCase__   =    height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCamelCase__   =    width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor
def     UpperCamelCase_(			_A				:int			,       _A				:Any=5_12			,       _A				:Optional[Any]=5_12					)->						List[Any]:
			UpperCamelCase__   =    pil_image.resize((w, h)			,       resample=Image.BICUBIC			,       reducing_gap=1					)
			UpperCamelCase__   =    np.array(pil_image.convert("RGB"					)					)
			UpperCamelCase__   =    arr.astype(np.floataa					) / 1_27.5 - 1
			UpperCamelCase__   =    np.transpose(_A			,       [2, 0, 1]					)
			UpperCamelCase__   =    torch.from_numpy(_A					).unsqueeze(0					)
			return image







class     lowerCamelCase__		(							snake_case__ ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case ,  snake_case ,  ):

						'''simple docstring'''


						super().__init__()

						self.register_modules(
						    unet=snake_case ,  scheduler=snake_case ,  movq=snake_case ,  )
						UpperCamelCase__   =    2 ** (len(self.movq.config.block_out_channels       ) - 1)




			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    min(int(num_inference_steps * strength       ) ,  snake_case       )

						UpperCamelCase__   =    max(num_inference_steps - init_timestep ,  0       )
						UpperCamelCase__   =    self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start




			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case=None       ):

						'''simple docstring'''


						if not isinstance(snake_case ,  (torch.Tensor, PIL.Image.Image, list)       ):
									raise ValueError(
									    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case       )}'''       )

						UpperCamelCase__   =    image.to(device=snake_case ,  dtype=snake_case       )

						UpperCamelCase__   =    batch_size * num_images_per_prompt

						if image.shape[1] == 4:
									UpperCamelCase__   =    image

						else:
									if isinstance(snake_case ,  snake_case       ) and len(snake_case       ) != batch_size:
												raise ValueError(
												    F'''You have passed a list of generators of length {len(snake_case       )}, but requested an effective batch'''
												    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''       )

									elif isinstance(snake_case ,  snake_case       ):
												UpperCamelCase__   =    [
												    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(snake_case       )
												]
												UpperCamelCase__   =    torch.cat(snake_case ,  dim=0       )
									else:
												UpperCamelCase__   =    self.movq.encode(snake_case       ).latent_dist.sample(snake_case       )

									UpperCamelCase__   =    self.movq.config.scaling_factor * init_latents

						UpperCamelCase__   =    torch.cat([init_latents] ,  dim=0       )

						UpperCamelCase__   =    init_latents.shape
						UpperCamelCase__   =    randn_tensor(snake_case ,  generator=snake_case ,  device=snake_case ,  dtype=snake_case       )

						# get latents
						UpperCamelCase__   =    self.scheduler.add_noise(snake_case ,  snake_case ,  snake_case       )

						UpperCamelCase__   =    init_latents

						return latents




			def 			snake_case__			(							self ,  snake_case=0       ):

						'''simple docstring'''


						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError("Please install accelerate via `pip install accelerate`"       )

						UpperCamelCase__   =    torch.device(F'''cuda:{gpu_id}'''       )

						UpperCamelCase__   =    [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(snake_case ,  snake_case       )




			def 			snake_case__			(							self ,  snake_case=0       ):

						'''simple docstring'''


						if is_accelerate_available() and is_accelerate_version(">=" ,  "0.17.0.dev0"       ):
									from accelerate import cpu_offload_with_hook
						else:
									raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."       )

						UpperCamelCase__   =    torch.device(F'''cuda:{gpu_id}'''       )

						if self.device.type != "cpu":
									self.to("cpu" ,  silence_dtype_warnings=snake_case       )
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						UpperCamelCase__   =    None
						for cpu_offloaded_model in [self.unet, self.movq]:
									UpperCamelCase__   =    cpu_offload_with_hook(snake_case ,  snake_case ,  prev_module_hook=snake_case       )

						# We'll offload the last model manually.
						UpperCamelCase__   =    hook




			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						if not hasattr(self.unet ,  "_hf_hook"       ):
									return self.device
						for module in self.unet.modules():
									if (
									    hasattr(snake_case ,  "_hf_hook"       )
									    and hasattr(module._hf_hook ,  "execution_device"       )
									    and module._hf_hook.execution_device is not None
									):
												return torch.device(module._hf_hook.execution_device       )
						return self.device







			@torch.no_grad()
			@replace_example_docstring(snake_case       )
			def __call__(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case = 512 ,  snake_case = 512 ,  snake_case = 100 ,  snake_case = 4.0 ,  snake_case = 0.3 ,  snake_case = 1 ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    self._execution_device

						UpperCamelCase__   =    guidance_scale > 1.0

						if isinstance(snake_case ,  snake_case       ):
									UpperCamelCase__   =    torch.cat(snake_case ,  dim=0       )
						if isinstance(snake_case ,  snake_case       ):
									UpperCamelCase__   =    torch.cat(snake_case ,  dim=0       )
						if isinstance(snake_case ,  snake_case       ):
									UpperCamelCase__   =    torch.cat(snake_case ,  dim=0       )

						UpperCamelCase__   =    image_embeds.shape[0]

						if do_classifier_free_guidance:
									UpperCamelCase__   =    image_embeds.repeat_interleave(snake_case ,  dim=0       )
									UpperCamelCase__   =    negative_image_embeds.repeat_interleave(snake_case ,  dim=0       )
									UpperCamelCase__   =    hint.repeat_interleave(snake_case ,  dim=0       )

						UpperCamelCase__   =    torch.cat([negative_image_embeds, image_embeds] ,  dim=0       ).to(dtype=self.unet.dtype ,  device=snake_case       )
						UpperCamelCase__   =    torch.cat([hint, hint] ,  dim=0       ).to(dtype=self.unet.dtype ,  device=snake_case       )

						if not isinstance(snake_case ,  snake_case       ):
									UpperCamelCase__   =    [image]
						if not all(isinstance(snake_case ,  (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
									raise ValueError(
									    F'''Input is in incorrect format: {[type(snake_case       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''       )

						UpperCamelCase__   =    torch.cat([prepare_image(snake_case ,  snake_case ,  snake_case       ) for i in image] ,  dim=0       )
						UpperCamelCase__   =    image.to(dtype=image_embeds.dtype ,  device=snake_case       )

						UpperCamelCase__   =    self.movq.encode(snake_case       )['latents']
						UpperCamelCase__   =    latents.repeat_interleave(snake_case ,  dim=0       )
						self.scheduler.set_timesteps(snake_case ,  device=snake_case       )
						UpperCamelCase__   =    self.get_timesteps(snake_case ,  snake_case ,  snake_case       )
						UpperCamelCase__   =    timesteps[:1].repeat(batch_size * num_images_per_prompt       )
						UpperCamelCase__   =    downscale_height_and_width(snake_case ,  snake_case ,  self.movq_scale_factor       )
						UpperCamelCase__   =    self.prepare_latents(
						    snake_case ,  snake_case ,  snake_case ,  snake_case ,  image_embeds.dtype ,  snake_case ,  snake_case       )
						for i, t in enumerate(self.progress_bar(snake_case       )       ):
									# expand the latents if we are doing classifier free guidance
									UpperCamelCase__   =    torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

									UpperCamelCase__   =    {'image_embeds': image_embeds, 'hint': hint}
									UpperCamelCase__   =    self.unet(
									    sample=snake_case ,  timestep=snake_case ,  encoder_hidden_states=snake_case ,  added_cond_kwargs=snake_case ,  return_dict=snake_case ,  )[0]

									if do_classifier_free_guidance:
												UpperCamelCase__   =    noise_pred.split(latents.shape[1] ,  dim=1       )
												UpperCamelCase__   =    noise_pred.chunk(2       )
												UpperCamelCase__   =    variance_pred.chunk(2       )
												UpperCamelCase__   =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												UpperCamelCase__   =    torch.cat([noise_pred, variance_pred_text] ,  dim=1       )

									if not (
									    hasattr(self.scheduler.config ,  "variance_type"       )
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
												UpperCamelCase__   =    noise_pred.split(latents.shape[1] ,  dim=1       )

									# compute the previous noisy sample x_t -> x_t-1

									UpperCamelCase__   =    self.scheduler.step(
									    snake_case ,  snake_case ,  snake_case ,  generator=snake_case ,  )[0]

						# post-processing
						UpperCamelCase__   =    self.movq.decode(snake_case ,  force_not_quantize=snake_case       )['sample']

						if output_type not in ["pt", "np", "pil"]:
									raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''       )

						if output_type in ["np", "pil"]:
									UpperCamelCase__   =    image * 0.5 + 0.5
									UpperCamelCase__   =    image.clamp(0 ,  1       )
									UpperCamelCase__   =    image.cpu().permute(0 ,  2 ,  3 ,  1       ).float().numpy()

						if output_type == "pil":
									UpperCamelCase__   =    self.numpy_to_pil(snake_case       )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=snake_case       )






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,
)


__UpperCamelCase      						=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase      						=    'cuda' if torch.cuda.is_available() else 'cpu'
def     UpperCamelCase_(			_A				:str			,       _A				:Tuple=1_00			,       _A				:str=" "					)->						List[str]:
			UpperCamelCase__   =    text.split(_A					)
			return [character.join(text[i : i + n]					).strip() for i in range(0			,       len(_A					)			,       _A					)]
def     UpperCamelCase_(			_A				:dict					)->						dict:
			UpperCamelCase__, UpperCamelCase__   =    [], []
			for title, text in zip(documents["title"]			,       documents["text"]					):
						if text is not None:
									for passage in split_text(_A					):
												titles.append(title if title is not None else ""					)
												texts.append(_A					)
			return {"title": titles, "text": texts}
def     UpperCamelCase_(			_A				:dict			,       _A				:DPRContextEncoder			,       _A				:DPRContextEncoderTokenizerFast					)->						dict:
			UpperCamelCase__   =    ctx_tokenizer(
			    documents["title"]			,       documents["text"]			,       truncation=_A			,       padding="longest"			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    ctx_encoder(input_ids.to(device=_A					)			,       return_dict=_A					).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}
def     UpperCamelCase_(			_A				:"RagExampleArguments"			,       _A				:"ProcessingArguments"			,       _A				:"IndexHnswArguments"			,       )->						List[str]:
			######################################
			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(_A			,       batched=_A			,       num_proc=processing_args.num_proc					)

			# And compute the embeddings
			UpperCamelCase__   =    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=_A					)
			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(_A			,       ctx_encoder=_A			,       ctx_tokenizer=_A					)			,       batched=_A			,       batch_size=processing_args.batch_size			,       features=_A			,       )

			# And finally save your dataset
			UpperCamelCase__   =    os.path.join(rag_example_args.output_dir			,       "my_knowledge_dataset"					)
			dataset.save_to_disk(_A					)
			# 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=_A					)

			# And save the index
			UpperCamelCase__   =    os.path.join(rag_example_args.output_dir			,       "my_knowledge_dataset_hnsw_index.faiss"					)
			dataset.get_index("embeddings"					).save(_A					)
			# 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=_A					)
			UpperCamelCase__   =    RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,       retriever=_A					)
			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(_A			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    model.generate(_A					)
			UpperCamelCase__   =    tokenizer.batch_decode(_A			,       skip_special_tokens=_A					)[0]
			logger.info("Q: " + question					)
			logger.info("A: " + generated_string					)







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






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







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






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







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   int					=						field(
			    default=768   ,		metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}   ,		)
			_UpperCamelCase						:   int					=						field(
			    default=128   ,		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)

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


