
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__				:  Dict       					=				'''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

UpperCAmelCase__				:  Optional[Any]       					=				logging.getLogger(__name__)



@dataclass
class 			UpperCamelCase_			:




							'''simple docstring'''

							UpperCamelCase_					   =							field(
							    default="""roberta-large-mnli"""	,     metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )
							UpperCamelCase_					   =							field(
							    default="""This example is {}."""	,     metadata={
							        """help""": (
							            """Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"""
							            """where class name is inserted."""
							        )
							    }	,     )
							UpperCamelCase_					   =							field(
							    default=32	,     metadata={"""help""": """Batch size for generating teacher predictions."""}   )
							UpperCamelCase_					   =							field(
							    default=__UpperCamelCase	,     metadata={
							        """help""": (
							            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
							            """multi-class multi-label classification)."""
							        )
							    }	,     )
							UpperCamelCase_					   =							field(
							    default=1.0	,     metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}   )



@dataclass
class 			UpperCamelCase_			:




							'''simple docstring'''

							UpperCamelCase_					   =							field(
							    default="""distilbert-base-uncased"""	,     metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )



@dataclass
class 			UpperCamelCase_			:




							'''simple docstring'''

							UpperCamelCase_					   =							field(metadata={"""help""": """Text file with one unlabeled instance per line."""}   )
							UpperCamelCase_					   =							field(metadata={"""help""": """Text file with one class name per line."""}   )
							UpperCamelCase_					   =							field(
							    default=__UpperCamelCase	,     metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}	,     )



@dataclass
class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							field(
							    default=__UpperCamelCase	,     metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}	,     )
							UpperCamelCase_					   =							field(
							    default=32	,     metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}   )
							UpperCamelCase_					   =							field(
							    default=128	,     metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}   )
							UpperCamelCase_					   =							field(default=1.0	,     metadata={"""help""": """Total number of training epochs to perform."""}   )
							UpperCamelCase_					   =							field(default=__UpperCamelCase	,     metadata={"""help""": """Whether to run training of student model."""}   )
							UpperCamelCase_					   =							field(
							    default=__UpperCamelCase	,     metadata={
							        """help""": (
							            """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
							            """after training."""
							        )
							    }	,     )
							UpperCamelCase_					   =							field(
							    default=0	,     metadata={
							        """help""": (
							            """Limit the total amount of checkpoints."""
							            """Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."""
							        )
							    }	,     )



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase=False) ->	int:
										UpperCamelCase__    :		Dict     =				inputs['labels']
										UpperCamelCase__    :		Optional[int]     =				model(inputs['input_ids']     ,						attention_mask=inputs['attention_mask'])
										UpperCamelCase__    :		List[Any]     =				outputs[0]

										UpperCamelCase__    :		int     =				-torch.sum(target_p * logits.log_softmax(dim=-1)     ,						axis=-1).mean()

										if return_outputs:
													return loss, outputs

										return loss



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Tuple:
			UpperCamelCase__    :		Optional[Any]     =				[]
			with open(__SCREAMING_SNAKE_CASE				,    'r' ) as f:
						for line in f:
									UpperCamelCase__    :		Tuple     =				line.strip()
									if len(__SCREAMING_SNAKE_CASE ) > 0:
												lines.append(__SCREAMING_SNAKE_CASE )
			return lines



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Any:
			UpperCamelCase__    :		Optional[int]     =				[]
			UpperCamelCase__    :		Optional[Any]     =				[]
			for example in examples:
						for name in class_names:
									premises.append(__SCREAMING_SNAKE_CASE )
									hypotheses.append(hypothesis_template.format(__SCREAMING_SNAKE_CASE ) )
			return premises, hypotheses



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Dict:
			for label, ind in config.labelaid.items():
						if label.lower().startswith('entail' ):
									return ind
			logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.' )
			return -1



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    )	->     Optional[Any]:
			UpperCamelCase__    :		List[Any]     =				AutoModelForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		int     =				model.config
			if not no_cuda and torch.cuda.is_available():
						UpperCamelCase__    :		List[Any]     =				nn.DataParallel(model.cuda() )
						batch_size *= len(model.device_ids )
			UpperCamelCase__    :		Optional[Any]     =				AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE				,    use_fast=__SCREAMING_SNAKE_CASE )

			UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				get_premise_hypothesis_pairs(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[int]     =				[]

			for i in tqdm(range(0				,    len(__SCREAMING_SNAKE_CASE )				,    __SCREAMING_SNAKE_CASE ) ):
						UpperCamelCase__    :		Tuple     =				premises[i : i + batch_size]
						UpperCamelCase__    :		Any     =				hypotheses[i : i + batch_size]

						UpperCamelCase__    :		int     =				tokenizer(
						    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    padding=__SCREAMING_SNAKE_CASE				,    truncation='only_first'				,    return_tensors='pt'				,    )

						with torch.cuda.amp.autocast(enabled=__SCREAMING_SNAKE_CASE ):
									with torch.no_grad():
												UpperCamelCase__    :		Optional[int]     =				model(**__SCREAMING_SNAKE_CASE )
						logits.append(outputs.logits.detach().cpu().float() )

			UpperCamelCase__    :		List[str]     =				get_entailment_id(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[int]     =				-1 if entail_id == 0 else 0
			UpperCamelCase__    :		str     =				torch.cat(__SCREAMING_SNAKE_CASE				,    dim=0 )  # N*K x 3
			UpperCamelCase__    :		Dict     =				logits.reshape(len(__SCREAMING_SNAKE_CASE )				,    len(__SCREAMING_SNAKE_CASE )				,    -1 )[..., [contr_id, entail_id]]  # N x K x 2

			if multi_label:
						# softmax over (contr, entail) logits for each class independently
						UpperCamelCase__    :		Union[str, Any]     =				(nli_logits / temperature).softmax(-1 )
			else:
						# softmax over entail logits across classes s.t. class probabilities sum to 1.
						UpperCamelCase__    :		int     =				(nli_logits / temperature).softmax(1 )

			return nli_prob[..., 1]  # N x K



def       _lowercase					(      )	->     List[Any]:
			UpperCamelCase__    :		List[str]     =				HfArgumentParser(
			    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)				,    description=__SCREAMING_SNAKE_CASE				,    )

			if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						UpperCamelCase__   ,			UpperCamelCase__   ,			UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				parser.parse_json_file(
						    json_file=os.path.abspath(sys.argv[1] ) )
			else:
						UpperCamelCase__   ,			UpperCamelCase__   ,			UpperCamelCase__   ,			UpperCamelCase__    :		int     =				parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			UpperCamelCase__    :		List[Any]     =				None
			if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCamelCase__    :		Tuple     =				get_last_checkpoint(training_args.output_dir )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
									raise ValueError(
									    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
									    'Use --overwrite_output_dir to overcome.' )
						elif last_checkpoint is not None:
									logger.info(
									    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )

    # Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,    datefmt='%m/%d/%Y %H:%M:%S'				,    handlers=[logging.StreamHandler(sys.stdout )]				,    )
			logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

			# Log on each process the small summary:
			logger.warning(
			    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
			    + F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank ):
						utils.logging.set_verbosity_info()
						utils.logging.enable_default_handler()
						utils.logging.enable_explicit_format()

			if training_args.local_rank != -1:
						raise ValueError('Distributed training is not currently supported.' )
			if training_args.tpu_num_cores is not None:
						raise ValueError('TPU acceleration is not currently supported.' )

			logger.info(F"""Training/evaluation parameters {training_args}""" )

			# Set seed before initializing model.
			set_seed(training_args.seed )

			# 1. read in data
			UpperCamelCase__    :		Union[str, Any]     =				read_lines(data_args.data_file )
			UpperCamelCase__    :		Dict     =				read_lines(data_args.class_names_file )

			# 2. get teacher predictions and load into dataset
			logger.info('Generating predictions from zero-shot teacher model' )
			UpperCamelCase__    :		Any     =				get_teacher_predictions(
			    teacher_args.teacher_name_or_path				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    teacher_args.hypothesis_template				,    teacher_args.teacher_batch_size				,    teacher_args.temperature				,    teacher_args.multi_label				,    data_args.use_fast_tokenizer				,    training_args.no_cuda				,    training_args.fpaa				,    )
			UpperCamelCase__    :		Tuple     =				Dataset.from_dict(
			    {
			        'text': examples,
			        'labels': teacher_soft_preds,
			    } )

			# 3. create student
			logger.info('Initializing student model' )
			UpperCamelCase__    :		List[str]     =				AutoModelForSequenceClassification.from_pretrained(
			    student_args.student_name_or_path				,    num_labels=len(__SCREAMING_SNAKE_CASE ) )
			UpperCamelCase__    :		List[str]     =				AutoTokenizer.from_pretrained(student_args.student_name_or_path				,    use_fast=data_args.use_fast_tokenizer )
			UpperCamelCase__    :		List[Any]     =				dict(enumerate(__SCREAMING_SNAKE_CASE ) )
			UpperCamelCase__    :		List[Any]     =				{label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE )}

			# 4. train student on teacher predictions
			UpperCamelCase__    :		str     =				dataset.map(__SCREAMING_SNAKE_CASE				,    input_columns='text' )
			dataset.set_format('torch' )

			def compute_metrics(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=False ):
						UpperCamelCase__    :		List[str]     =				p.predictions.argmax(-1 )
						UpperCamelCase__    :		List[str]     =				p.label_ids.argmax(-1 )  # "label_ids" are actually distributions
						return {"agreement": (preds == proxy_labels).mean().item()}

			UpperCamelCase__    :		Union[str, Any]     =				DistillationTrainer(
			    model=__SCREAMING_SNAKE_CASE				,    tokenizer=__SCREAMING_SNAKE_CASE				,    args=__SCREAMING_SNAKE_CASE				,    train_dataset=__SCREAMING_SNAKE_CASE				,    compute_metrics=__SCREAMING_SNAKE_CASE				,    )

			if training_args.do_train:
						logger.info('Training student model on teacher predictions' )
						trainer.train()

			if training_args.do_eval:
						UpperCamelCase__    :		Optional[Any]     =				trainer.evaluate(eval_dataset=__SCREAMING_SNAKE_CASE )['eval_agreement']
						logger.info(F"""Agreement of student and teacher predictions: {agreement * 100:0.2f}%""" )

			trainer.save_model()


if __name__ == "__main__":
							main()





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__				:  Optional[int]       					=				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__				:  Optional[int]       					=				'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=8 )	->     List[Any]:
			UpperCamelCase__    :		Union[str, Any]     =				height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCamelCase__    :		Tuple     =				width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=512				,    __SCREAMING_SNAKE_CASE=512 )	->     Union[str, Any]:
			UpperCamelCase__    :		List[str]     =				pil_image.resize((w, h)				,    resample=Image.BICUBIC				,    reducing_gap=1 )
			UpperCamelCase__    :		List[str]     =				np.array(pil_image.convert('RGB' ) )
			UpperCamelCase__    :		Tuple     =				arr.astype(np.floataa ) / 127.5 - 1
			UpperCamelCase__    :		Tuple     =				np.transpose(__SCREAMING_SNAKE_CASE				,    [2, 0, 1] )
			UpperCamelCase__    :		int     =				torch.from_numpy(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
			return image



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						) ->	Any:
										super().__init__()

										self.register_modules(
										    unet=UpperCamelCase     ,						scheduler=UpperCamelCase     ,						movq=UpperCamelCase     ,						)
										UpperCamelCase__    :		str     =				2 ** (len(self.movq.config.block_out_channels) - 1)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
										# get the original timestep using init_timestep
										UpperCamelCase__    :		str     =				min(int(num_inference_steps * strength)     ,						UpperCamelCase)

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

										return timesteps, num_inference_steps - t_start
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase=None) ->	Optional[Any]:
										if not isinstance(UpperCamelCase     ,						(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(UpperCamelCase)}""")

										UpperCamelCase__    :		int     =				image.to(device=UpperCamelCase     ,						dtype=UpperCamelCase)

										UpperCamelCase__    :		int     =				batch_size * num_images_per_prompt

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

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

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

													UpperCamelCase__    :		List[Any]     =				self.movq.config.scaling_factor * init_latents

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

										UpperCamelCase__    :		Union[str, Any]     =				init_latents.shape
										UpperCamelCase__    :		Union[str, Any]     =				randn_tensor(UpperCamelCase     ,						generator=UpperCamelCase     ,						device=UpperCamelCase     ,						dtype=UpperCamelCase)

										# get latents
										UpperCamelCase__    :		Optional[int]     =				self.scheduler.add_noise(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				init_latents

										return latents
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	str:
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError('Please install accelerate via `pip install accelerate`')

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

										UpperCamelCase__    :		int     =				[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(UpperCamelCase     ,						UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	Tuple:
										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__    :		int     =				torch.device(F"""cuda:{gpu_id}""")

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

										UpperCamelCase__    :		Dict     =				None
										for cpu_offloaded_model in [self.unet, self.movq]:
													UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				cpu_offload_with_hook(UpperCamelCase     ,						UpperCamelCase     ,						prev_module_hook=UpperCamelCase)

										# We'll offload the last model manually.
										UpperCamelCase__    :		List[Any]     =				hook
							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										if not hasattr(self.unet     ,						'_hf_hook'):
													return self.device
										for module in self.unet.modules():
													if (
													    hasattr(UpperCamelCase     ,						'_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(UpperCamelCase)
							def __call__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase = 5_12     ,						UpperCamelCase = 5_12     ,						UpperCamelCase = 1_00     ,						UpperCamelCase = 4.0     ,						UpperCamelCase = 0.3     ,						UpperCamelCase = 1     ,						UpperCamelCase = None     ,						UpperCamelCase = "pil"     ,						UpperCamelCase = True     ,						) ->	Optional[Any]:
										UpperCamelCase__    :		List[Any]     =				self._execution_device

										UpperCamelCase__    :		Union[str, Any]     =				guidance_scale > 1.0

										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Dict     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		List[Any]     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)

										UpperCamelCase__    :		Optional[Any]     =				image_embeds.shape[0]

										if do_classifier_free_guidance:
													UpperCamelCase__    :		List[str]     =				image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		Any     =				negative_image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		str     =				hint.repeat_interleave(UpperCamelCase     ,						dim=0)

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

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

										UpperCamelCase__    :		List[str]     =				torch.cat([prepare_image(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) for i in image]     ,						dim=0)
										UpperCamelCase__    :		int     =				image.to(dtype=image_embeds.dtype     ,						device=UpperCamelCase)

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

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

													if do_classifier_free_guidance:
																UpperCamelCase__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				noise_pred.chunk(2)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				variance_pred.chunk(2)
																UpperCamelCase__    :		Tuple     =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																UpperCamelCase__    :		Any     =				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__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)

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

													UpperCamelCase__    :		Union[str, Any]     =				self.scheduler.step(
													    UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						generator=UpperCamelCase     ,						)[0]

										# post-processing
										UpperCamelCase__    :		Optional[Any]     =				self.movq.decode(UpperCamelCase     ,						force_not_quantize=UpperCamelCase)['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__    :		Any     =				image * 0.5 + 0.5
													UpperCamelCase__    :		Tuple     =				image.clamp(0     ,						1)
													UpperCamelCase__    :		Tuple     =				image.cpu().permute(0     ,						2     ,						3     ,						1).float().numpy()

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

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=UpperCamelCase)



