import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
			from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
			_SCREAMING_SNAKE_CASE:						List[str]    	=       None

if is_ray_available():
			import ray  # noqa: E402 # isort:skip
			from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
			_SCREAMING_SNAKE_CASE:						Optional[int]    	=       None
			_SCREAMING_SNAKE_CASE:						Dict    	=       None
			_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       None






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
						_snake_case              =      unittest.skip("test requires Datasets, Faiss, psutil"  )(lowerCamelCase_  )
			return test_case







@require_distributed_retrieval
class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					_snake_case              =      tempfile.mkdtemp()
					_snake_case              =      8

					# DPR tok
					_snake_case              =      [
					    "[UNK]",
					    "[CLS]",
					    "[SEP]",
					    "[PAD]",
					    "[MASK]",
					    "want",
					    "##want",
					    "##ed",
					    "wa",
					    "un",
					    "runn",
					    "##ing",
					    ",",
					    "low",
					    "lowest",
					]
					_snake_case              =      os.path.join(self.tmpdirname  ,					"dpr_tokenizer"   )
					os.makedirs(__SCREAMING_SNAKE_CASE  ,					exist_ok=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      os.path.join(__SCREAMING_SNAKE_CASE  ,					DPR_VOCAB_FILES_NAMES["vocab_file"]   )
					with open(self.vocab_file  ,					"w"  ,					encoding="utf-8"   ) as vocab_writer:
								vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )

					# BART tok
					_snake_case              =      [
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "\u0120",
					    "\u0120l",
					    "\u0120n",
					    "\u0120lo",
					    "\u0120low",
					    "er",
					    "\u0120lowest",
					    "\u0120newer",
					    "\u0120wider",
					    "<unk>",
					]
					_snake_case              =      dict(zip(__SCREAMING_SNAKE_CASE  ,					range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
					_snake_case              =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
					_snake_case              =      {"unk_token": "<unk>"}

					_snake_case              =      os.path.join(self.tmpdirname  ,					"bart_tokenizer"   )
					os.makedirs(__SCREAMING_SNAKE_CASE  ,					exist_ok=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      os.path.join(__SCREAMING_SNAKE_CASE  ,					BART_VOCAB_FILES_NAMES["vocab_file"]   )
					_snake_case              =      os.path.join(__SCREAMING_SNAKE_CASE  ,					BART_VOCAB_FILES_NAMES["merges_file"]   )
					with open(self.vocab_file  ,					"w"  ,					encoding="utf-8"   ) as fp:
								fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + "\n"   )
					with open(self.merges_file  ,					"w"  ,					encoding="utf-8"   ) as fp:
								fp.write("\n".join(__SCREAMING_SNAKE_CASE   )   )



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






					"""simple docstring"""
					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,					"dpr_tokenizer"   )   )



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






					"""simple docstring"""
					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,					"bart_tokenizer"   )   )



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					shutil.rmtree(self.tmpdirname   )



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      Dataset.from_dict(
					    {
					        "id": ["0", "1"],
					        "text": ["foo", "bar"],
					        "title": ["Foo", "Bar"],
					        "embeddings": [np.ones(self.retrieval_vector_size   ), 2 * np.ones(self.retrieval_vector_size   )],
					    }   )
					dataset.add_faiss_index("embeddings"  ,					string_factory="Flat"  ,					metric_type=faiss.METRIC_INNER_PRODUCT   )
					return dataset



		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				bool  ,					__SCREAMING_SNAKE_CASE    :				Tuple=12_345   ):






					"""simple docstring"""
					_snake_case              =      self.get_dummy_dataset()
					_snake_case              =      RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size  ,					question_encoder=DPRConfig().to_dict()  ,					generator=BartConfig().to_dict()  ,					)
					with patch("transformers.models.rag.retrieval_rag.load_dataset"   ) as mock_load_dataset:
								_snake_case              =      dataset
								_snake_case              =      RagPyTorchDistributedRetriever(
								    __SCREAMING_SNAKE_CASE  ,					question_encoder_tokenizer=self.get_dpr_tokenizer()  ,					generator_tokenizer=self.get_bart_tokenizer()  ,					)
								if init_retrieval:
											retriever.init_retrieval(__SCREAMING_SNAKE_CASE   )
					return retriever



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				bool   ):






					"""simple docstring"""
					ray.init(local_mode=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size  ,					question_encoder=DPRConfig().to_dict()  ,					generator=BartConfig().to_dict()  ,					)
					_snake_case              =      ray.remote(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      [remote_cls.remote() for _ in range(1   )]
					with patch("transformers.models.rag.retrieval_rag.load_dataset"   ) as mock_load_dataset:
								_snake_case              =      self.get_dummy_dataset()
								_snake_case              =      RagRayDistributedRetriever(
								    __SCREAMING_SNAKE_CASE  ,					question_encoder_tokenizer=self.get_dpr_tokenizer()  ,					generator_tokenizer=self.get_bart_tokenizer()  ,					retrieval_workers=__SCREAMING_SNAKE_CASE  ,					)
								if init_retrieval:
											retriever.init_retrieval()
					return retriever



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				bool  ,					__SCREAMING_SNAKE_CASE    :				bool  ,					__SCREAMING_SNAKE_CASE    :				List[str]=12_345   ):






					"""simple docstring"""
					_snake_case              =      self.get_dummy_dataset()
					_snake_case              =      RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size  ,					question_encoder=DPRConfig().to_dict()  ,					generator=BartConfig().to_dict()  ,					index_name="custom"  ,					)
					if from_disk:
								_snake_case              =      os.path.join(self.tmpdirname  ,					"dataset"   )
								_snake_case              =      os.path.join(self.tmpdirname  ,					"index.faiss"   )
								dataset.get_index("embeddings"   ).save(os.path.join(self.tmpdirname  ,					"index.faiss"   )   )
								dataset.drop_index("embeddings"   )
								dataset.save_to_disk(os.path.join(self.tmpdirname  ,					"dataset"   )   )
								del dataset
								_snake_case              =      RagPyTorchDistributedRetriever(
								    __SCREAMING_SNAKE_CASE  ,					question_encoder_tokenizer=self.get_dpr_tokenizer()  ,					generator_tokenizer=self.get_bart_tokenizer()  ,					)
					else:
								_snake_case              =      RagPyTorchDistributedRetriever(
								    __SCREAMING_SNAKE_CASE  ,					question_encoder_tokenizer=self.get_dpr_tokenizer()  ,					generator_tokenizer=self.get_bart_tokenizer()  ,					index=CustomHFIndex(config.retrieval_vector_size  ,					__SCREAMING_SNAKE_CASE   )  ,					)
					if init_retrieval:
								retriever.init_retrieval(__SCREAMING_SNAKE_CASE   )
					return retriever



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				bool  ,					__SCREAMING_SNAKE_CASE    :				bool   ):






					"""simple docstring"""
					ray.init(local_mode=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.get_dummy_dataset()
					_snake_case              =      RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size  ,					question_encoder=DPRConfig().to_dict()  ,					generator=BartConfig().to_dict()  ,					index_name="custom"  ,					)
					_snake_case              =      ray.remote(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      [remote_cls.remote() for _ in range(1   )]
					if from_disk:
								_snake_case              =      os.path.join(self.tmpdirname  ,					"dataset"   )
								_snake_case              =      os.path.join(self.tmpdirname  ,					"index.faiss"   )
								dataset.get_index("embeddings"   ).save(os.path.join(self.tmpdirname  ,					"index.faiss"   )   )
								dataset.drop_index("embeddings"   )
								dataset.save_to_disk(os.path.join(self.tmpdirname  ,					"dataset"   )   )
								del dataset
								_snake_case              =      RagRayDistributedRetriever(
								    __SCREAMING_SNAKE_CASE  ,					question_encoder_tokenizer=self.get_dpr_tokenizer()  ,					generator_tokenizer=self.get_bart_tokenizer()  ,					retrieval_workers=__SCREAMING_SNAKE_CASE  ,					index=CustomHFIndex.load_from_disk(
								        vector_size=config.retrieval_vector_size  ,					dataset_path=config.passages_path  ,					index_path=config.index_path  ,					)  ,					)
					else:
								_snake_case              =      RagRayDistributedRetriever(
								    __SCREAMING_SNAKE_CASE  ,					question_encoder_tokenizer=self.get_dpr_tokenizer()  ,					generator_tokenizer=self.get_bart_tokenizer()  ,					retrieval_workers=__SCREAMING_SNAKE_CASE  ,					index=CustomHFIndex(config.retrieval_vector_size  ,					__SCREAMING_SNAKE_CASE   )  ,					)
					if init_retrieval:
								retriever.init_retrieval()
					return retriever



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				RagRetriever  ,					__SCREAMING_SNAKE_CASE    :				np.array  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case              =      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,					n_docs=__SCREAMING_SNAKE_CASE   )
					self.assertEqual(retrieved_doc_embeds.shape  ,					(2, n_docs, self.retrieval_vector_size)   )
					self.assertEqual(len(__SCREAMING_SNAKE_CASE   )  ,					2   )
					self.assertEqual(sorted(doc_dicts[0]   )  ,					["embeddings", "id", "text", "title"]   )
					self.assertEqual(len(doc_dicts[0]["id"]   )  ,					__SCREAMING_SNAKE_CASE   )
					self.assertEqual(doc_dicts[0]["id"][0]  ,					"1"   )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]["id"][0]  ,					"0"   )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()  ,					[[1], [0]]   )



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					_snake_case              =      1
					_snake_case              =      np.array(
					    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]  ,					dtype=np.floataa   )

					self.distributed_retriever_check(
					    self.get_dummy_pytorch_distributed_retriever(init_retrieval=__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      1
					_snake_case              =      np.array(
					    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]  ,					dtype=np.floataa   )

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=__SCREAMING_SNAKE_CASE  ,					from_disk=__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					)



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      1
					_snake_case              =      np.array(
					    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]  ,					dtype=np.floataa   )

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=__SCREAMING_SNAKE_CASE  ,					from_disk=__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					)



		@require_ray
		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      1
					_snake_case              =      np.array(
					    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]  ,					dtype=np.floataa   )

					self.distributed_retriever_check(
					    self.get_dummy_ray_distributed_retriever(init_retrieval=__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					ray.shutdown()



		@require_ray
		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					_snake_case              =      1
					_snake_case              =      np.array(
					    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]  ,					dtype=np.floataa   )
					with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
								self.distributed_retriever_check(
								    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__SCREAMING_SNAKE_CASE  ,					from_disk=__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					)
					ray.shutdown()







		@require_ray
		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      1
					_snake_case              =      np.array(
					    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]  ,					dtype=np.floataa   )

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__SCREAMING_SNAKE_CASE  ,					from_disk=__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					ray.shutdown()







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				AutoencoderKL  ,					__SCREAMING_SNAKE_CASE    :				CLIPTextModel  ,					__SCREAMING_SNAKE_CASE    :				CLIPTokenizer  ,					__SCREAMING_SNAKE_CASE    :				UNetaDConditionModel  ,					__SCREAMING_SNAKE_CASE    :				DDIMScheduler  ,					__SCREAMING_SNAKE_CASE    :				float = 0.9_3  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					):






					"""simple docstring"""
					_snake_case              =      mixing_coeff
					_snake_case              =      leapfrog_steps

					super().__init__()
					self.register_modules(
					    vae=__SCREAMING_SNAKE_CASE  ,					text_encoder=__SCREAMING_SNAKE_CASE  ,					tokenizer=__SCREAMING_SNAKE_CASE  ,					unet=__SCREAMING_SNAKE_CASE  ,					scheduler=__SCREAMING_SNAKE_CASE  ,					)

					_snake_case              =      2 ** (len(self.vae.config.block_out_channels   ) - 1)
					_snake_case              =      VaeImageProcessor(vae_scale_factor=self.vae_scale_factor   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = False   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer(
					    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

					_snake_case              =      self.text_encoder(text_inputs.input_ids.to(self.device   )   ).last_hidden_state

					_snake_case              =      prompt_embeds.to(dtype=self.text_encoder.dtype  ,					device=self.device   )

					if do_classifier_free_guidance:
								_snake_case              =      "" if negative_prompt is None else negative_prompt

								_snake_case              =      self.tokenizer(
								    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

								_snake_case              =      self.text_encoder(uncond_input.input_ids.to(self.device   )   ).last_hidden_state

								_snake_case              =      torch.cat([negative_prompt_embeds, prompt_embeds]   )

					return prompt_embeds



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					_snake_case              =      self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					_snake_case              =      (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					# as self.alphas_cumprod is always in cpu
					_snake_case              =      int(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					_snake_case              =      (base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype   )



		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					_snake_case              =      a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype   )



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      1 / self.vae.config.scaling_factor * latents
					_snake_case              =      self.vae.decode(__SCREAMING_SNAKE_CASE   ).sample
					_snake_case              =      (image / 2 + 0.5).clamp(0  ,					1   )
					return image



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				float  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      image.to(device=self.device  ,					dtype=text_embeds.dtype   )
					_snake_case              =      self.vae.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.vae.config.scaling_factor * latent

					_snake_case              =      [latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								_snake_case              =      self.noise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

								# j - model_input index, k - base index
								for j in range(2   ):
											_snake_case              =      j ^ 1

											if self.leapfrog_steps:
														if i % 2 == 0:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.noise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

					return coupled_latents







		@torch.no_grad()
		def __call__(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				float = 3.0  ,					__SCREAMING_SNAKE_CASE    :				int = 50  ,					__SCREAMING_SNAKE_CASE    :				float = 0.8  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = "pil"  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      self.image_processor.preprocess(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,					self.device   )

					_snake_case              =      num_inference_steps - int(num_inference_steps * strength   )
					_snake_case              =      self.scheduler.timesteps[t_limit:]
					_snake_case              =      fwd_timesteps.flip(0   )

					_snake_case              =      self.prepare_latents(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								# j - model_input index, k - base index
								for k in range(2   ):
											_snake_case              =      k ^ 1

											if self.leapfrog_steps:
														if i % 2 == 1:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.denoise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

								_snake_case              =      self.denoise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

					# either one is fine
					_snake_case              =      coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
								_snake_case              =      (
								    F'the output_type {output_type} is outdated. 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   )
								_snake_case              =      "np"

					if output_type == "latent":
								_snake_case              =      final_latent
					else:
								_snake_case              =      self.decode_latents(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.image_processor.postprocess(__SCREAMING_SNAKE_CASE  ,					output_type=__SCREAMING_SNAKE_CASE   )

					return image

