





"""simple docstring"""





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]=32    ,						SCREAMING_SNAKE_CASE       :							int=10    ,						SCREAMING_SNAKE_CASE       :							List[str]=100    ,						SCREAMING_SNAKE_CASE       :							str=1026    ,						SCREAMING_SNAKE_CASE       :							Any=True    ,						SCREAMING_SNAKE_CASE       :							Tuple="data/tokenized_stories_train_wikitext103.jbl"    ,						SCREAMING_SNAKE_CASE       :							Tuple="igf_context_pairs.jbl"    ,						):


							'''simple docstring'''







							set_seed(3   )
							# generate train_data and objective_set
							__lowerCamelCase    ,						__lowerCamelCase      :      Dict         =generate_datasets(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						number=SCREAMING_SNAKE_CASE    ,						min_len=1026    ,						trim=SCREAMING_SNAKE_CASE   )
							# keeps model same across runs
							set_seed(4   )
							# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
							# can we train on GPU?
							__lowerCamelCase      :      Tuple         =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )

							# load pretrained model
							__lowerCamelCase      :      Optional[int]         =load_gpta('''gpt2'''   ).to(SCREAMING_SNAKE_CASE   )
							print('''computing perplexity on objective set'''   )
							__lowerCamelCase      :      Union[str, Any]         =compute_perplexity(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ).item()
							print('''perplexity on objective set:'''    ,						SCREAMING_SNAKE_CASE   )

							# collect igf pairs and save to file demo.jbl
							collect_objective_set(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# clean up, delete model and data we don't need anymore
							del model, train_data, objective_set
							torch.cuda.empty_cache()



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]=15    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]=128    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]=100    ,						SCREAMING_SNAKE_CASE       :							Dict="igf_model.pt"    ,						):


							'''simple docstring'''







							set_seed(42   )

							# Load pre-trained model
							__lowerCamelCase      :      Optional[int]         =GPTaLMHeadModel.from_pretrained('''gpt2'''   )

							# Initialize secondary learner to use embedding weights of model
							__lowerCamelCase      :      Optional[int]         =SecondaryLearner(SCREAMING_SNAKE_CASE   )

							# Train secondary learner
							__lowerCamelCase      :      Union[str, Any]         =train_secondary_learner(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						max_epochs=SCREAMING_SNAKE_CASE    ,						batch_size=SCREAMING_SNAKE_CASE    ,						eval_freq=100    ,						igf_model_path=SCREAMING_SNAKE_CASE    ,						)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							str=32    ,						SCREAMING_SNAKE_CASE       :							Any=1000    ,						SCREAMING_SNAKE_CASE       :							int=16    ,						SCREAMING_SNAKE_CASE       :							Optional[int]=1.0    ,						SCREAMING_SNAKE_CASE       :							Dict=recopy_gpta    ,						SCREAMING_SNAKE_CASE       :							Optional[int]=None    ,						SCREAMING_SNAKE_CASE       :							str=10    ,						SCREAMING_SNAKE_CASE       :							List[Any]="gpt2_finetuned.pt"    ,						):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )
							__lowerCamelCase      :      Union[str, Any]         =RandomSampler(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      int         =DataLoader(SCREAMING_SNAKE_CASE    ,						sampler=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[int]         =max_steps // (len(SCREAMING_SNAKE_CASE   )) + 1
							__lowerCamelCase      :      Union[str, Any]         =0
							__lowerCamelCase      :      Any         =torch.zeros((1, context_len)    ,						dtype=torch.long    ,						device=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Dict         =recopy_model(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(SCREAMING_SNAKE_CASE   )
														secondary_learner.eval()
							__lowerCamelCase      :      Dict         =[]
							__lowerCamelCase      :      str         =0

							__lowerCamelCase      :      str         =[]
							__lowerCamelCase      :      int         =[]

							# Compute the performance of the transformer model at the beginning
							__lowerCamelCase      :      List[str]         =compute_perplexity(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							test_perps.append(SCREAMING_SNAKE_CASE   )
							print('''Test perplexity, step'''    ,						SCREAMING_SNAKE_CASE    ,						''':'''    ,						SCREAMING_SNAKE_CASE   )
							for epoch in range(int(SCREAMING_SNAKE_CASE   )   ):
														for step, example in enumerate(SCREAMING_SNAKE_CASE   ):
																					torch.cuda.empty_cache()
																					__lowerCamelCase      :      Tuple         =random.randint(0    ,						example.size(2   ) - context_len - 1   )
																					__lowerCamelCase      :      Optional[Any]         =example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					__lowerCamelCase      :      Any         =model(SCREAMING_SNAKE_CASE    ,						labels=SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[Any]         =True

																					if secondary_learner is not None:
																												__lowerCamelCase      :      Tuple         =secondary_learner.forward(
																												    torch.tensor(SCREAMING_SNAKE_CASE    ,						dtype=torch.long    ,						device=SCREAMING_SNAKE_CASE   ).unsqueeze(0   )   )[0].item()
																												observed_qs.append(float(SCREAMING_SNAKE_CASE   )   )

																												# Here we implement the simple non-constant threshold for the predicted IG(X) value
																												# We will decay the selectivity of our secondary learner filter from
																												# 1 standard deviation above average to 1 below average after 10 batches.

																												if global_step == 10:
																																			__lowerCamelCase      :      Any         =-1
																												if predicted_q < threshold:
																																			__lowerCamelCase      :      List[Any]         =False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()   )   )
																												__lowerCamelCase      :      Any         =outputs[0]
																												lm_loss.backward()
																												examples += 1

																					del outputs

																					# Once the batch is filled with enough contexts, backprop on the batch.
																					if examples == batch_size:
																												torch.cuda.empty_cache()
																												__lowerCamelCase      :      List[Any]         =0
																												# Do LM backprop
																												torch.nn.utils.clip_grad_norm_(model.parameters()    ,						3.0   )
																												lm_optimizer.step()
																												lm_scheduler.step()  # Update learning rate schedule
																												global_step += 1
																												# Compute the performance of the transformer model at this batch
																												if global_step % eval_interval == 0:
																																			__lowerCamelCase      :      Dict         =compute_perplexity(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
																																			test_perps.append(SCREAMING_SNAKE_CASE   )

																																			print('''Test perplexity, step'''    ,						SCREAMING_SNAKE_CASE    ,						''':'''    ,						SCREAMING_SNAKE_CASE   )
            # Break out of the loop after 60 batches
																					if max_steps > 0 and global_step > 60:
																												break
														if max_steps > 0 and global_step > 60:
																					break

    # save finetuned transformer model
							torch.save(model.state_dict()    ,						SCREAMING_SNAKE_CASE   )
							torch.cuda.empty_cache()
							# Do some cleaning up so we can reinitialize for the next run of this function
							del lm_optimizer
							del lm_scheduler
							return model



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''   )

							# Required parameters
							parser.add_argument(
							    '''--data_dir'''    ,						default=SCREAMING_SNAKE_CASE    ,						type=SCREAMING_SNAKE_CASE    ,						required=SCREAMING_SNAKE_CASE    ,						help='''The input data dir. Should contain data files for WikiText.'''    ,						)
							parser.add_argument(
							    '''--model_name_or_path'''    ,						default=SCREAMING_SNAKE_CASE    ,						type=SCREAMING_SNAKE_CASE    ,						required=SCREAMING_SNAKE_CASE    ,						help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,						)
							parser.add_argument(
							    '''--data_file'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						help=(
							        '''A jbl file containing tokenized data which can be split as objective dataset, '''
							        '''train_dataset and test_dataset.'''
							    )    ,						)

							parser.add_argument(
							    '''--igf_data_file'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						help='''A jbl file containing the context and information gain pairs to train secondary learner.'''    ,						)

							parser.add_argument(
							    '''--output_dir'''    ,						default=SCREAMING_SNAKE_CASE    ,						type=SCREAMING_SNAKE_CASE    ,						required=SCREAMING_SNAKE_CASE    ,						help='''The output directory where the final fine-tuned model is stored.'''    ,						)

							parser.add_argument(
							    '''--tokenizer_name'''    ,						default=SCREAMING_SNAKE_CASE    ,						type=SCREAMING_SNAKE_CASE    ,						help='''Pretrained tokenizer name or path if not the same as model_name'''    ,						)
							parser.add_argument('''--seed'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						help='''A seed for reproducible training.'''   )

							parser.add_argument(
							    '''--context_len'''    ,						default=32    ,						type=SCREAMING_SNAKE_CASE    ,						help=(
							        '''The maximum total input sequence length after tokenization. Sequences longer '''
							        '''than this will be truncated, sequences shorter will be padded.'''
							    )    ,						)

							parser.add_argument(
							    '''--size_objective_set'''    ,						default=100    ,						type=SCREAMING_SNAKE_CASE    ,						help='''number of articles that are long enough to be used as our objective set'''    ,						)
							parser.add_argument(
							    '''--eval_freq'''    ,						default=100    ,						type=SCREAMING_SNAKE_CASE    ,						help='''secondary model evaluation is triggered at eval_freq'''   )

							parser.add_argument('''--max_steps'''    ,						default=1000    ,						type=SCREAMING_SNAKE_CASE    ,						help='''To calculate training epochs'''   )

							parser.add_argument(
							    '''--secondary_learner_batch_size'''    ,						default=128    ,						type=SCREAMING_SNAKE_CASE    ,						help='''batch size of training data for secondary learner'''    ,						)

							parser.add_argument(
							    '''--batch_size'''    ,						default=16    ,						type=SCREAMING_SNAKE_CASE    ,						help='''batch size of training data of language model(gpt2) '''   )

							parser.add_argument(
							    '''--eval_interval'''    ,						default=10    ,						type=SCREAMING_SNAKE_CASE    ,						help=(
							        '''decay the selectivity of our secondary learner filter from'''
							        '''1 standard deviation above average to 1 below average after 10 batches'''
							    )    ,						)

							parser.add_argument(
							    '''--number'''    ,						default=100    ,						type=SCREAMING_SNAKE_CASE    ,						help='''The number of examples split to be used as objective_set/test_data'''   )

							parser.add_argument(
							    '''--min_len'''    ,						default=1026    ,						type=SCREAMING_SNAKE_CASE    ,						help='''The minimum length of the article to be used as objective set'''   )

							parser.add_argument(
							    '''--secondary_learner_max_epochs'''    ,						default=15    ,						type=SCREAMING_SNAKE_CASE    ,						help='''number of epochs to train secondary learner'''   )

							parser.add_argument('''--trim'''    ,						default=SCREAMING_SNAKE_CASE    ,						type=SCREAMING_SNAKE_CASE    ,						help='''truncate the example if it exceeds context length'''   )

							parser.add_argument(
							    '''--threshold'''    ,						default=1.0    ,						type=SCREAMING_SNAKE_CASE    ,						help=(
							        '''The threshold value used by secondary learner to filter the train_data and allow only'''
							        ''' informative data as input to the model'''
							    )    ,						)

							parser.add_argument('''--finetuned_model_name'''    ,						default='''gpt2_finetuned.pt'''    ,						type=SCREAMING_SNAKE_CASE    ,						help='''finetuned_model_name'''   )

							parser.add_argument(
							    '''--recopy_model'''    ,						default=SCREAMING_SNAKE_CASE    ,						type=SCREAMING_SNAKE_CASE    ,						help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''    ,						)

							# function calls
							# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
							generate_n_pairs(
							    context_len=32    ,						max_steps=10    ,						size_objective_set=100    ,						min_len=1026    ,						trim=SCREAMING_SNAKE_CASE    ,						data_file='''data/tokenized_stories_train_wikitext103.jbl'''    ,						igf_data_file='''igf_context_pairs.jbl'''    ,						)

							# Load train data for secondary learner
							__lowerCamelCase      :      Union[str, Any]         =joblib.load('''data/IGF_values.jbl'''   )

							# Train secondary learner
							__lowerCamelCase      :      Any         =training_secondary_learner(
							    SCREAMING_SNAKE_CASE    ,						secondary_learner_max_epochs=15    ,						secondary_learner_batch_size=128    ,						eval_freq=100    ,						igf_model_path='''igf_model.pt'''    ,						)

							# load pretrained gpt2 model
							__lowerCamelCase      :      Optional[Any]         =GPTaLMHeadModel.from_pretrained('''gpt2'''   )
							set_seed(42   )

							# Generate train and test data to train and evaluate gpt2 model
							__lowerCamelCase    ,						__lowerCamelCase      :      int         =generate_datasets(
							    context_len=32    ,						file='''data/tokenized_stories_train_wikitext103.jbl'''    ,						number=100    ,						min_len=1026    ,						trim=SCREAMING_SNAKE_CASE   )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						context_len=32    ,						max_steps=1000    ,						batch_size=16    ,						threshold=1.0    ,						recopy_model=SCREAMING_SNAKE_CASE    ,						secondary_learner=SCREAMING_SNAKE_CASE    ,						eval_interval=10    ,						finetuned_model_name='''gpt2_finetuned.pt'''    ,						)


if __name__ == "__main__":
							main()











"""simple docstring"""





from typing import List, Optional, Union

import torch

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


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

_UpperCamelCase							=      '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\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 = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\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        >>> 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        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Tuple=8   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__lowerCamelCase      :      Tuple         =width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :UNetaDConditionModel						,    __lowercase  :DDPMScheduler						,    __lowercase  :VQModel						,    ):
											super().__init__()

											self.register_modules(
											    unet=__lowercase						,    scheduler=__lowercase						,    movq=__lowercase						,    )
											__lowerCamelCase      :      Any         =2 ** (len(self.movq.config.block_out_channels			) - 1)




				def 					__lowercase  (     self  :int						,    __lowercase  :Tuple						,    __lowercase  :Dict						,    __lowercase  :str						,    __lowercase  :Dict						,    __lowercase  :Union[str, Any]						,    __lowercase  :Dict			):
											if latents is None:
																		__lowerCamelCase      :      str         =randn_tensor(__lowercase						,    generator=__lowercase						,    device=__lowercase						,    dtype=__lowercase			)
											else:
																		if latents.shape != shape:
																									raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'			)
																		__lowerCamelCase      :      str         =latents.to(__lowercase			)

											__lowerCamelCase      :      Any         =latents * scheduler.init_noise_sigma
											return latents




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Any=0			):
											if is_accelerate_available():
																		from accelerate import cpu_offload
											else:
																		raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

											__lowerCamelCase      :      Any         =torch.device(f'cuda:{gpu_id}'			)

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




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :List[Any]=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.'''			)

											__lowerCamelCase      :      str         =torch.device(f'cuda:{gpu_id}'			)

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

											__lowerCamelCase      :      Optional[Any]         =None
											for cpu_offloaded_model in [self.unet, self.movq]:
																		__lowerCamelCase    ,						__lowerCamelCase      :      Union[str, Any]         =cpu_offload_with_hook(__lowercase						,    __lowercase						,    prev_module_hook=__lowercase			)

											# We'll offload the last model manually.
											__lowerCamelCase      :      Dict         =hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 					__lowercase  (     self  :Tuple			):
											if not hasattr(self.unet						,    '''_hf_hook'''			):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(__lowercase						,    '''_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(__lowercase			)
				def __call__(     self  :Union[str, Any]						,    __lowercase  :Union[torch.FloatTensor, List[torch.FloatTensor]]						,    __lowercase  :Union[torch.FloatTensor, List[torch.FloatTensor]]						,    __lowercase  :torch.FloatTensor						,    __lowercase  :int = 512						,    __lowercase  :int = 512						,    __lowercase  :int = 100						,    __lowercase  :float = 4.0						,    __lowercase  :int = 1						,    __lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,    __lowercase  :Optional[torch.FloatTensor] = None						,    __lowercase  :Optional[str] = "pil"						,    __lowercase  :bool = True						,    ):
											__lowerCamelCase      :      Any         =self._execution_device

											__lowerCamelCase      :      Dict         =guidance_scale > 1.0

											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Any         =torch.cat(__lowercase						,    dim=0			)
											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Dict         =torch.cat(__lowercase						,    dim=0			)
											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Dict         =torch.cat(__lowercase						,    dim=0			)

											__lowerCamelCase      :      List[Any]         =image_embeds.shape[0] * num_images_per_prompt

											if do_classifier_free_guidance:
																		__lowerCamelCase      :      Optional[int]         =image_embeds.repeat_interleave(__lowercase						,    dim=0			)
																		__lowerCamelCase      :      Any         =negative_image_embeds.repeat_interleave(__lowercase						,    dim=0			)
																		__lowerCamelCase      :      List[Any]         =hint.repeat_interleave(__lowercase						,    dim=0			)

											__lowerCamelCase      :      List[str]         =torch.cat([negative_image_embeds, image_embeds]						,    dim=0			).to(dtype=self.unet.dtype						,    device=__lowercase			)
											__lowerCamelCase      :      List[str]         =torch.cat([hint, hint]						,    dim=0			).to(dtype=self.unet.dtype						,    device=__lowercase			)
											self.scheduler.set_timesteps(__lowercase						,    device=__lowercase			)
											__lowerCamelCase      :      str         =self.scheduler.timesteps

											__lowerCamelCase      :      Tuple         =self.movq.config.latent_channels

											__lowerCamelCase    ,						__lowerCamelCase      :      List[str]         =downscale_height_and_width(__lowercase						,    __lowercase						,    self.movq_scale_factor			)

											# create initial latent
											__lowerCamelCase      :      Optional[int]         =self.prepare_latents(
											    (batch_size, num_channels_latents, height, width)						,    image_embeds.dtype						,    __lowercase						,    __lowercase						,    __lowercase						,    self.scheduler						,    )

											for i, t in enumerate(self.progress_bar(__lowercase			)			):
																		# expand the latents if we are doing classifier free guidance
																		__lowerCamelCase      :      int         =torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

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

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

																		# compute the previous noisy sample x_t -> x_t-1
																		__lowerCamelCase      :      Tuple         =self.scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase						,    )[0]
											# post-processing
											__lowerCamelCase      :      List[str]         =self.movq.decode(__lowercase						,    force_not_quantize=__lowercase			)['''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"]:
																		__lowerCamelCase      :      List[str]         =image * 0.5 + 0.5
																		__lowerCamelCase      :      Optional[int]         =image.clamp(0						,    1			)
																		__lowerCamelCase      :      Union[str, Any]         =image.cpu().permute(0						,    2						,    3						,    1			).float().numpy()

											if output_type == "pil":
																		__lowerCamelCase      :      Optional[Any]         =self.numpy_to_pil(__lowercase			)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=__lowercase			)




