

"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__A	:       Dict        =						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if "token" in model_name_or_path:
										return "rag_token"
					if "sequence" in model_name_or_path:
										return "rag_sequence"
					if "bart" in model_name_or_path:
										return "bart"
					return None
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return max(metric_fn(lowercase__     ,				lowercase__ ) for gt in ground_truths )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   [line.strip() for line in open(lowercase__     ,				"r" ).readlines()]
					A     =   []

					if args.gold_data_mode == "qa":
										A     =   pd.read_csv(lowercase__     ,				sep="\t"     ,				header=lowercase__ )
										for answer_list in data[1]:
															A     =   ast.literal_eval(lowercase__ )
															answers.append(lowercase__ )
					else:
										A     =   [line.strip() for line in open(lowercase__     ,				"r" ).readlines()]
										A     =   [[reference] for reference in references]

					A     =   A     =   A     =   0
					for prediction, ground_truths in zip(lowercase__     ,				lowercase__ ):
										total += 1
										em += metric_max_over_ground_truths(lowercase__     ,				lowercase__     ,				lowercase__ )
										fa += metric_max_over_ground_truths(lowercase__     ,				lowercase__     ,				lowercase__ )

					A     =   1_00.0 * em / total
					A     =   1_00.0 * fa / total

					logger.info(F"""F1: {fa:.2f}""" )
					logger.info(F"""EM: {em:.2f}""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   args.k
					A     =   [line.strip() for line in open(lowercase__     ,				"r" ).readlines()]
					A     =   [line.strip() for line in open(lowercase__     ,				"r" ).readlines()]

					A     =   A     =   0
					for hypo, reference in zip(lowercase__     ,				lowercase__ ):
										A     =   set(hypo.split("\t" )[:k] )
										A     =   set(reference.split("\t" ) )
										total += 1
										em += len(hypo_provenance & ref_provenance ) / k

					A     =   1_00.0 * em / total
					logger.info(F"""Precision@{k}: {em: .2f}""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					def strip_title(lowercase__ ):
										if title.startswith("\"" ):
															A     =   title[1:]
										if title.endswith("\"" ):
															A     =   title[:-1]
										return title

					A     =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    lowercase__     ,				return_tensors="pt"     ,				padding=lowercase__     ,				truncation=lowercase__     ,				)["input_ids"].to(args.device )

					A     =   rag_model.rag.question_encoder(lowercase__ )
					A     =   question_enc_outputs[0]

					A     =   rag_model.retriever(
					    lowercase__     ,				question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy()     ,				prefix=rag_model.rag.generator.config.prefix     ,				n_docs=rag_model.config.n_docs     ,				return_tensors="pt"     ,				)
					A     =   rag_model.retriever.index.get_doc_dicts(result.doc_ids )
					A     =   []
					for docs in all_docs:
										A     =   [strip_title(lowercase__ ) for title in docs["title"]]
										provenance_strings.append("\t".join(lowercase__ ) )
					return provenance_strings
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					with torch.no_grad():
										A     =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    lowercase__     ,				return_tensors="pt"     ,				padding=lowercase__     ,				truncation=lowercase__ )

										A     =   inputs_dict.input_ids.to(args.device )
										A     =   inputs_dict.attention_mask.to(args.device )
										A     =   rag_model.generate(  # rag_model overwrites generate
										    lowercase__     ,				attention_mask=lowercase__     ,				num_beams=args.num_beams     ,				min_length=args.min_length     ,				max_length=args.max_length     ,				early_stopping=lowercase__     ,				num_return_sequences=1     ,				bad_words_ids=[[0, 0]]     ,				)
										A     =   rag_model.retriever.generator_tokenizer.batch_decode(lowercase__     ,				skip_special_tokens=lowercase__ )

										if args.print_predictions:
															for q, a in zip(lowercase__     ,				lowercase__ ):
																				logger.info("Q: {} - A: {}".format(lowercase__     ,				lowercase__ ) )

										return answers
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   argparse.ArgumentParser()
					parser.add_argument(
					    "--model_type"     ,				choices=["rag_sequence", "rag_token", "bart"]     ,				type=lowercase__     ,				help=(
					        "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
					        " model_name_or_path"
					    )     ,				)
					parser.add_argument(
					    "--index_name"     ,				default=lowercase__     ,				choices=["exact", "compressed", "legacy"]     ,				type=lowercase__     ,				help="RAG model retriever type"     ,				)
					parser.add_argument(
					    "--index_path"     ,				default=lowercase__     ,				type=lowercase__     ,				help="Path to the retrieval index"     ,				)
					parser.add_argument("--n_docs"     ,				default=5     ,				type=lowercase__     ,				help="Number of retrieved docs" )
					parser.add_argument(
					    "--model_name_or_path"     ,				default=lowercase__     ,				type=lowercase__     ,				required=lowercase__     ,				help="Path to pretrained checkpoints or model identifier from huggingface.co/models"     ,				)
					parser.add_argument(
					    "--eval_mode"     ,				choices=["e2e", "retrieval"]     ,				default="e2e"     ,				type=lowercase__     ,				help=(
					        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
					        " precision@k."
					    )     ,				)
					parser.add_argument("--k"     ,				default=1     ,				type=lowercase__     ,				help="k for the precision@k calculation" )
					parser.add_argument(
					    "--evaluation_set"     ,				default=lowercase__     ,				type=lowercase__     ,				required=lowercase__     ,				help="Path to a file containing evaluation samples"     ,				)
					parser.add_argument(
					    "--gold_data_path"     ,				default=lowercase__     ,				type=lowercase__     ,				required=lowercase__     ,				help="Path to a tab-separated file with gold samples"     ,				)
					parser.add_argument(
					    "--gold_data_mode"     ,				default="qa"     ,				type=lowercase__     ,				choices=["qa", "ans"]     ,				help=(
					        "Format of the gold data file"
					        "qa - a single line in the following format: question [tab] answer_list"
					        "ans - a single line of the gold file contains the expected answer string"
					    )     ,				)
					parser.add_argument(
					    "--predictions_path"     ,				type=lowercase__     ,				default="predictions.txt"     ,				help="Name of the predictions file, to be stored in the checkpoints directory"     ,				)
					parser.add_argument(
					    "--eval_all_checkpoints"     ,				action="store_true"     ,				help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"     ,				)
					parser.add_argument(
					    "--eval_batch_size"     ,				default=8     ,				type=lowercase__     ,				help="Batch size per GPU/CPU for evaluation."     ,				)
					parser.add_argument(
					    "--recalculate"     ,				help="Recalculate predictions even if the prediction file exists"     ,				action="store_true"     ,				)
					parser.add_argument(
					    "--num_beams"     ,				default=4     ,				type=lowercase__     ,				help="Number of beams to be used when generating answers"     ,				)
					parser.add_argument("--min_length"     ,				default=1     ,				type=lowercase__     ,				help="Min length of the generated answers" )
					parser.add_argument("--max_length"     ,				default=50     ,				type=lowercase__     ,				help="Max length of the generated answers" )

					parser.add_argument(
					    "--print_predictions"     ,				action="store_true"     ,				help="If True, prints predictions while evaluating."     ,				)
					parser.add_argument(
					    "--print_docs"     ,				action="store_true"     ,				help="If True, prints docs retried while generating."     ,				)
					A     =   parser.parse_args()
					A     =   torch.device("cuda" if torch.cuda.is_available() else "cpu" )
					return args
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   {}
					if args.model_type is None:
										A     =   infer_model_type(args.model_name_or_path )
										assert args.model_type is not None
					if args.model_type.startswith("rag" ):
										A     =   RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
										A     =   args.n_docs
										if args.index_name is not None:
															A     =   args.index_name
										if args.index_path is not None:
															A     =   args.index_path
					else:
										A     =   BartForConditionalGeneration

					A     =   (
					    [f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
					    if args.eval_all_checkpoints
					    else [args.model_name_or_path]
					)

					logger.info("Evaluate the following checkpoints: %s"     ,				lowercase__ )

					A     =   get_scores if args.eval_mode == "e2e" else get_precision_at_k
					A     =   evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

					for checkpoint in checkpoints:
										if os.path.exists(args.predictions_path ) and (not args.recalculate):
															logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path ) )
															score_fn(lowercase__     ,				args.predictions_path     ,				args.gold_data_path )
															continue

										logger.info("***** Running evaluation for {} *****".format(lowercase__ ) )
										logger.info("  Batch size = %d"     ,				args.eval_batch_size )
										logger.info("  Predictions will be stored under {}".format(args.predictions_path ) )

										if args.model_type.startswith("rag" ):
															A     =   RagRetriever.from_pretrained(lowercase__     ,				**lowercase__ )
															A     =   model_class.from_pretrained(lowercase__     ,				retriever=lowercase__     ,				**lowercase__ )
															model.retriever.init_retrieval()
										else:
															A     =   model_class.from_pretrained(lowercase__     ,				**lowercase__ )
										model.to(args.device )

										with open(args.evaluation_set     ,				"r" ) as eval_file, open(args.predictions_path     ,				"w" ) as preds_file:
															A     =   []
															for line in tqdm(lowercase__ ):
																				questions.append(line.strip() )
																				if len(lowercase__ ) == args.eval_batch_size:
																									A     =   evaluate_batch_fn(lowercase__     ,				lowercase__     ,				lowercase__ )
																									preds_file.write("\n".join(lowercase__ ) + "\n" )
																									preds_file.flush()
																									A     =   []
															if len(lowercase__ ) > 0:
																				A     =   evaluate_batch_fn(lowercase__     ,				lowercase__     ,				lowercase__ )
																				preds_file.write("\n".join(lowercase__ ) )
																				preds_file.flush()

															score_fn(lowercase__     ,				args.predictions_path     ,				args.gold_data_path )


if __name__ == "__main__":
	__A	:       Any        =						get_args()
	main(args)









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,
)


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

_A							     =       '\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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      str=8	)     ->	Any:



	"""simple docstring"""






	a_     			=   height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	a_     			=   width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Dict=512     ,					UpperCamelCase		:      Optional[Any]=512	)     ->	List[Any]:



	"""simple docstring"""






	a_     			=   pil_image.resize((w, h)     ,					resample=Image.BICUBIC     ,					reducing_gap=1	)
	a_     			=   np.array(pil_image.convert("""RGB"""	)	)
	a_     			=   arr.astype(np.floataa	) / 127.5 - 1
	a_     			=   np.transpose(UpperCamelCase     ,					[2, 0, 1]	)
	a_     			=   torch.from_numpy(UpperCamelCase	).unsqueeze(0	)
	return image

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       ):
								super().__init__()

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								# get the original timestep using init_timestep
								a_     			=   min(int(num_inference_steps * strength    )			,       _SCREAMING_SNAKE_CASE    )

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

								return timesteps, num_inference_steps - t_start





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None    ):
								if not isinstance(_SCREAMING_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(_SCREAMING_SNAKE_CASE    )}"""    )

								a_     			=   image.to(device=_SCREAMING_SNAKE_CASE			,       dtype=_SCREAMING_SNAKE_CASE    )

								a_     			=   batch_size * num_images_per_prompt

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

								else:
									if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ) and len(_SCREAMING_SNAKE_CASE    ) != batch_size:
										raise ValueError(
										    f"""You have passed a list of generators of length {len(_SCREAMING_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(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
										a_     			=   [
										    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_SCREAMING_SNAKE_CASE    )
										]
										a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )
									else:
										a_     			=   self.movq.encode(_SCREAMING_SNAKE_CASE    ).latent_dist.sample(_SCREAMING_SNAKE_CASE    )

									a_     			=   self.movq.config.scaling_factor * init_latents

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

								a_     			=   init_latents.shape
								a_     			=   randn_tensor(_SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE			,       device=_SCREAMING_SNAKE_CASE			,       dtype=_SCREAMING_SNAKE_CASE    )

								# get latents
								a_     			=   self.scheduler.add_noise(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								a_     			=   init_latents

								return latents





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0    ):
								if is_accelerate_available():
									from accelerate import cpu_offload
								else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

								a_     			=   torch.device(f"""cuda:{gpu_id}"""    )

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0    ):
								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."""    )

								a_     			=   torch.device(f"""cuda:{gpu_id}"""    )

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

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

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





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   __magic_name__      (							self    ):
								if not hasattr(self.unet			,       """_hf_hook"""    ):
									return self.device
								for module in self.unet.modules():
									if (
									    hasattr(_SCREAMING_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(_SCREAMING_SNAKE_CASE    )
							def __call__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = 512			,       _SCREAMING_SNAKE_CASE = 512			,       _SCREAMING_SNAKE_CASE = 100			,       _SCREAMING_SNAKE_CASE = 4.0			,       _SCREAMING_SNAKE_CASE = 0.3			,       _SCREAMING_SNAKE_CASE = 1			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = "pil"			,       _SCREAMING_SNAKE_CASE = True			,       ):
								a_     			=   self._execution_device

								a_     			=   guidance_scale > 1.0

								if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									a_     			=   torch.cat(_SCREAMING_SNAKE_CASE			,       dim=0    )

								a_     			=   image_embeds.shape[0]

								if do_classifier_free_guidance:
									a_     			=   image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )
									a_     			=   negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )
									a_     			=   hint.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )

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

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

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

								a_     			=   self.movq.encode(_SCREAMING_SNAKE_CASE    )["""latents"""]
								a_     			=   latents.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )
								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			,       device=_SCREAMING_SNAKE_CASE    )
								a_			, a_     			=   self.get_timesteps(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   timesteps[:1].repeat(batch_size * num_images_per_prompt    )
								a_			, a_     			=   downscale_height_and_width(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       self.movq_scale_factor    )
								a_     			=   self.prepare_latents(
								    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       image_embeds.dtype			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
								for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE    )    ):
									# expand the latents if we are doing classifier free guidance
									a_     			=   torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

									a_     			=   {"""image_embeds""": image_embeds, """hint""": hint}
									a_     			=   self.unet(
									    sample=_SCREAMING_SNAKE_CASE			,       timestep=_SCREAMING_SNAKE_CASE			,       encoder_hidden_states=_SCREAMING_SNAKE_CASE			,       added_cond_kwargs=_SCREAMING_SNAKE_CASE			,       return_dict=_SCREAMING_SNAKE_CASE			,       )[0]

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

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

									a_     			=   self.scheduler.step(
									    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE			,       )[0]

								# post-processing
								a_     			=   self.movq.decode(_SCREAMING_SNAKE_CASE			,       force_not_quantize=_SCREAMING_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"]:
									a_     			=   image * 0.5 + 0.5
									a_     			=   image.clamp(0			,       1    )
									a_     			=   image.cpu().permute(0			,       2			,       3			,       1    ).float().numpy()

								if output_type == "pil":
									a_     			=   self.numpy_to_pil(_SCREAMING_SNAKE_CASE    )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE    )