




'''simple docstring'''




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class     UpperCamelCase_     (_SCREAMING_SNAKE_CASE     ):







						"""simple docstring"""


						def __init__(     self      :       List[Any]							, _lowerCamelCase      :       AutoencoderKL							, _lowerCamelCase      :       CLIPTextModel							, _lowerCamelCase      :       CLIPTokenizer							, _lowerCamelCase      :       UNetaDConditionModel							, _lowerCamelCase      :       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]							, ):





											"""simple docstring"""







											super().__init__()

											self.register_modules(vae=UpperCamelCase__							, text_encoder=UpperCamelCase__							, tokenizer=UpperCamelCase__							, unet=UpperCamelCase__							, scheduler=UpperCamelCase__    )







						def       _a    (     self      :       Tuple							, _lowerCamelCase      :       str    ):





											"""simple docstring"""







											with torch.no_grad():
																A_ :     Optional[int] =							self.vae.encode(tfms.ToTensor()(UpperCamelCase__    ).unsqueeze(0    ).to(self.device    ) * 2 - 1    )
																A_ :     int =							0.1_82_15 * latent.latent_dist.sample()
											return latent







						def       _a    (     self      :       List[Any]							, _lowerCamelCase      :       Any    ):





											"""simple docstring"""







											A_ :     Any =							(1 / 0.1_82_15) * latent
											with torch.no_grad():
																A_ :     List[Any] =							self.vae.decode(UpperCamelCase__    ).sample
											A_ :     str =							(img / 2 + 0.5).clamp(0							, 1    )
											A_ :     Any =							img.detach().cpu().permute(0							, 2							, 3							, 1    ).numpy()
											A_ :     str =							(img * 255).round().astype('''uint8'''    )
											return Image.fromarray(img[0]    )







						def       _a    (     self      :       List[str]							, _lowerCamelCase      :       List[Any]    ):





											"""simple docstring"""







											A_ :     Any =							self.tokenizer(
											    UpperCamelCase__							, padding='''max_length'''							, max_length=self.tokenizer.model_max_length							, truncation=UpperCamelCase__							, return_tensors='''pt'''							, )

											A_ :     Dict =							self.text_encoder(text_input.input_ids.to(self.device    )    )[0]

											A_ :     List[str] =							self.tokenizer(
											    ''''''							, padding='''max_length'''							, max_length=self.tokenizer.model_max_length							, truncation=UpperCamelCase__							, return_tensors='''pt'''							, )

											A_ :     List[str] =							self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

											return torch.cat([uncond_embedding, text_embedding]    )







						def __call__(     self      :       Tuple							, _lowerCamelCase      :       Image.Image							, _lowerCamelCase      :       str							, _lowerCamelCase      :       float = 0.3							, _lowerCamelCase      :       float = 0.6							, _lowerCamelCase      :       float = 0.5							, _lowerCamelCase      :       int = 42							, _lowerCamelCase      :       int = 50							, _lowerCamelCase      :       float = 7.5							, ):





											"""simple docstring"""







											A_ :     Optional[int] =							steps - int(kmin * steps    )
											A_ :     Optional[int] =							steps - int(kmax * steps    )

											A_ :     List[Any] =							self.prep_text(UpperCamelCase__    )

											self.scheduler.set_timesteps(UpperCamelCase__    )

											A_ ,A_ :     Optional[Any] =							img.size
											A_ :     Union[str, Any] =							self.encode(UpperCamelCase__    )

											torch.manual_seed(UpperCamelCase__    )
											A_ :     List[Any] =							torch.randn(
											    (1, self.unet.config.in_channels, height // 8, width // 8)							, ).to(self.device    )

											A_ :     List[Any] =							self.scheduler.add_noise(
											    UpperCamelCase__							, UpperCamelCase__							, timesteps=self.scheduler.timesteps[tmax]							, )

											A_ :     str =							torch.cat([latents] * 2    )

											A_ :     Optional[int] =							self.scheduler.scale_model_input(UpperCamelCase__							, self.scheduler.timesteps[tmax]    )

											with torch.no_grad():
																A_ :     int =							self.unet(
																    UpperCamelCase__							, self.scheduler.timesteps[tmax]							, encoder_hidden_states=UpperCamelCase__							, ).sample

											A_ ,A_ :     Optional[Any] =							pred.chunk(2    )
											A_ :     Optional[Any] =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

											A_ :     int =							self.scheduler.step(UpperCamelCase__							, self.scheduler.timesteps[tmax]							, UpperCamelCase__    ).prev_sample

											for i, t in enumerate(tqdm(self.scheduler.timesteps    )    ):
																if i > tmax:
																					if i < tmin:  # layout generation phase
																										A_ :     Optional[Any] =							self.scheduler.add_noise(
																										    UpperCamelCase__							, UpperCamelCase__							, timesteps=UpperCamelCase__							, )

																										A_ :     int =							(mix_factor * latents) + (
																										    1 - mix_factor
																										) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																										A_ :     Union[str, Any] =							torch.cat([input] * 2    )

																					else:  # content generation phase
																										A_ :     Optional[int] =							torch.cat([latents] * 2    )

																					A_ :     List[str] =							self.scheduler.scale_model_input(UpperCamelCase__							, UpperCamelCase__    )

																					with torch.no_grad():
																										A_ :     List[str] =							self.unet(
																										    UpperCamelCase__							, UpperCamelCase__							, encoder_hidden_states=UpperCamelCase__							, ).sample

																					A_ ,A_ :     Optional[Any] =							pred.chunk(2    )
																					A_ :     str =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

																					A_ :     int =							self.scheduler.step(UpperCamelCase__							, UpperCamelCase__							, UpperCamelCase__    ).prev_sample

											return self.decode(UpperCamelCase__    )









'''simple docstring'''




import logging
import random

import ray

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


snake_case__          =						logging.getLogger(__name__)






class     UpperCamelCase_     :







						"""simple docstring"""


						def __init__(     self      :       Tuple    ):





											"""simple docstring"""







											A_ :     Optional[int] =							False







						def       _a    (     self      :       Any							, _lowerCamelCase      :       List[str]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       str							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if not self.initialized:
																A_ :     Optional[Any] =							RagRetriever(
																    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )
																A_ :     Optional[Any] =							True







						def       _a    (     self      :       int    ):





											"""simple docstring"""







											self.retriever.index.init_index()







						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											del self.retriever
											A_ :     List[Any] =							False







						def       _a    (     self      :       Optional[int]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Union[str, Any]    ):





											"""simple docstring"""







											A_ ,A_ :     Dict =							self.retriever._main_retrieve(_lowerCamelCase							, _lowerCamelCase    )
											A_ :     int =							self.retriever.index.get_doc_dicts(_lowerCamelCase    )
											return doc_ids, retrieved_doc_embeds, doc_dicts






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						def __init__(     self      :       int							, _lowerCamelCase      :       Optional[Any]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Tuple=None    ):





											"""simple docstring"""







											if index is not None and index.is_initialized() and len(_lowerCamelCase    ) > 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__(
											    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )

											A_ :     Tuple =							retrieval_workers
											A_ :     Any =							question_encoder_tokenizer
											A_ :     Dict =							generator_tokenizer
											if len(self.retrieval_workers    ) > 0:
																ray.get(
																    [
																        worker.create_rag_retriever.remote(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase    )
																        for worker in self.retrieval_workers
																    ]    )







						def       _a    (     self      :       Dict    ):





											"""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       _a    (     self      :       Dict							, _lowerCamelCase      :       Tuple							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if len(self.retrieval_workers    ) > 0:
																# Select a random retrieval actor.
																A_ :     Optional[int] =							self.retrieval_workers[random.randint(0							, len(self.retrieval_workers    ) - 1    )]
																A_ ,A_ ,A_ :     Union[str, Any] =							ray.get(
																    random_worker.retrieve.remote(_lowerCamelCase							, _lowerCamelCase    )    )
											else:
																A_ ,A_ :     List[str] =							self._main_retrieve(_lowerCamelCase							, _lowerCamelCase    )
																A_ :     Union[str, Any] =							self.index.get_doc_dicts(_lowerCamelCase    )
											return retrieved_doc_embeds, doc_ids, doc_dicts







						@classmethod
						def       _a    (     cls      :       List[str]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Optional[Any]=None							, **_lowerCamelCase      :       str    ):





											"""simple docstring"""







											return super(_lowerCamelCase							, cls    ).get_tokenizers(_lowerCamelCase							, _lowerCamelCase							, **_lowerCamelCase    )







						@classmethod
						def       _a    (     cls      :       Optional[Any]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       List[Any]							, _lowerCamelCase      :       str=None							, **_lowerCamelCase      :       Tuple    ):





											"""simple docstring"""







											A_ :     Tuple =							kwargs.pop('''config'''							, _lowerCamelCase    ) or RagConfig.from_pretrained(_lowerCamelCase							, **_lowerCamelCase    )
											A_ :     List[Any] =							RagTokenizer.from_pretrained(_lowerCamelCase							, config=_lowerCamelCase    )
											A_ :     Union[str, Any] =							rag_tokenizer.question_encoder
											A_ :     Dict =							rag_tokenizer.generator

											if indexed_dataset is not None:
																A_ :     Any =							'''custom'''
																A_ :     int =							CustomHFIndex(config.retrieval_vector_size							, _lowerCamelCase    )
											else:
																A_ :     Optional[int] =							cls._build_index(_lowerCamelCase    )

											return cls(
											    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, retrieval_workers=_lowerCamelCase							, index=_lowerCamelCase							, )







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											logger.info('''re-loading the new dataset with embeddings'''    )
											# access from the training loop

											ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]    )

											# build the index object again
											A_ :     Dict =							self._build_index(self.config    )

											ray.get(
											    [
											        worker.create_rag_retriever.remote(
											            self.config							, self.question_encoder_tokenizer							, self.generator_tokenizer							, _lowerCamelCase    )
											        for worker in self.retrieval_workers
											    ]    )



