

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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase      				=     logging.getLogger(__name__)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			, )
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """Whether tp freeze the encoder."""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """Whether  to freeze the embeddings."""}							)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}							)
							__UpperCAmelCase       =field(
							    default="""summarization"""			, metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}			, )
							__UpperCAmelCase       =field(
							    default=1024			, metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=128			, metadata={
							        """help""": (
							            """The maximum total sequence length for target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=142			, metadata={
							        """help""": (
							            """The maximum total sequence length for validation target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded. """
							            """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
							            """during ``evaluate`` and ``predict``."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=142			, metadata={
							        """help""": (
							            """The maximum total sequence length for test target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			, )
							__UpperCAmelCase       =field(default=-1			, metadata={"""help""": """# training examples. -1 means use all."""}							)
							__UpperCAmelCase       =field(default=-1			, metadata={"""help""": """# validation examples. -1 means use all."""}							)
							__UpperCAmelCase       =field(default=-1			, metadata={"""help""": """# test examples. -1 means use all."""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """Source language id for translation."""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """Target language id for translation."""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """# num_beams to use for evaluation."""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}			, )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					logger.info(F"""***** {split} metrics *****"""      )
					for key in sorted(metrics.keys()      ):
										logger.info(F"""  {key} = {metrics[key]}"""      )
					save_json(UpperCAmelCase__		,					os.path.join(UpperCAmelCase__		,					F"""{split}_results.json"""      )      )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)      )

					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.
										__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
					else:
										__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							parser.parse_args_into_dataclasses()

					check_output_dir(UpperCAmelCase__      )

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,					datefmt='%m/%d/%Y %H:%M:%S'		,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,					)
					logger.warning(
					    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'		,					training_args.local_rank		,					training_args.device		,					training_args.n_gpu		,					bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED      )		,					training_args.fpaa		,					)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank      ):
										transformers.utils.logging.set_verbosity_info()
					logger.info('Training/evaluation parameters %s'		,					UpperCAmelCase__      )

					# Set seed
					set_seed(training_args.seed      )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__lowerCAmelCase					=							AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,					cache_dir=model_args.cache_dir		,					)

					__lowerCAmelCase					=							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
					for p in extra_model_params:
										if getattr(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):
															assert hasattr(UpperCAmelCase__		,					UpperCAmelCase__      ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
															setattr(UpperCAmelCase__		,					UpperCAmelCase__		,					getattr(UpperCAmelCase__		,					UpperCAmelCase__      )      )

					__lowerCAmelCase					=							AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,					cache_dir=model_args.cache_dir		,					)
					__lowerCAmelCase					=							AutoModelForSeqaSeqLM.from_pretrained(
					    model_args.model_name_or_path		,					from_tf='.ckpt' in model_args.model_name_or_path		,					config=UpperCAmelCase__		,					cache_dir=model_args.cache_dir		,					)

					# use task specific params
					use_task_specific_params(UpperCAmelCase__		,					data_args.task      )

					# set num_beams for evaluation
					if data_args.eval_beams is None:
										__lowerCAmelCase					=							model.config.num_beams

					# set decoder_start_token_id for MBart
					if model.config.decoder_start_token_id is None and isinstance(UpperCAmelCase__		,					(MBartTokenizer, MBartTokenizerFast)      ):
										assert (
										    data_args.tgt_lang is not None and data_args.src_lang is not None
										), "mBart requires --tgt_lang and --src_lang"
										if isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
															__lowerCAmelCase					=							tokenizer.lang_code_to_id[data_args.tgt_lang]
										else:
															__lowerCAmelCase					=							tokenizer.convert_tokens_to_ids(data_args.tgt_lang      )

					if model_args.freeze_embeds:
										freeze_embeds(UpperCAmelCase__      )
					if model_args.freeze_encoder:
										freeze_params(model.get_encoder()      )
										assert_all_frozen(model.get_encoder()      )

					__lowerCAmelCase					=							SeqaSeqDataset

					# Get datasets
					__lowerCAmelCase					=							(
					    dataset_class(
					        UpperCAmelCase__		,					type_path='train'		,					data_dir=data_args.data_dir		,					n_obs=data_args.n_train		,					max_target_length=data_args.max_target_length		,					max_source_length=data_args.max_source_length		,					prefix=model.config.prefix or ''		,					)
					    if training_args.do_train
					    else None
					)
					__lowerCAmelCase					=							(
					    dataset_class(
					        UpperCAmelCase__		,					type_path='val'		,					data_dir=data_args.data_dir		,					n_obs=data_args.n_val		,					max_target_length=data_args.val_max_target_length		,					max_source_length=data_args.max_source_length		,					prefix=model.config.prefix or ''		,					)
					    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
					    else None
					)
					__lowerCAmelCase					=							(
					    dataset_class(
					        UpperCAmelCase__		,					type_path='test'		,					data_dir=data_args.data_dir		,					n_obs=data_args.n_test		,					max_target_length=data_args.test_max_target_length		,					max_source_length=data_args.max_source_length		,					prefix=model.config.prefix or ''		,					)
					    if training_args.do_predict
					    else None
					)

					# Initialize our Trainer
					__lowerCAmelCase					=							(
					    build_compute_metrics_fn(data_args.task		,					UpperCAmelCase__      ) if training_args.predict_with_generate else None
					)
					__lowerCAmelCase					=							SeqaSeqTrainer(
					    model=UpperCAmelCase__		,					args=UpperCAmelCase__		,					data_args=UpperCAmelCase__		,					train_dataset=UpperCAmelCase__		,					eval_dataset=UpperCAmelCase__		,					data_collator=SeqaSeqDataCollator(
					        UpperCAmelCase__		,					UpperCAmelCase__		,					model.config.decoder_start_token_id		,					training_args.tpu_num_cores      )		,					compute_metrics=UpperCAmelCase__		,					tokenizer=UpperCAmelCase__		,					)

					__lowerCAmelCase					=							{}
					# Training
					if training_args.do_train:
										logger.info('*** Train ***'      )

										__lowerCAmelCase					=							trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
										__lowerCAmelCase					=							train_result.metrics
										__lowerCAmelCase					=							data_args.n_train

										trainer.save_model()  # this also saves the tokenizer

										if trainer.is_world_process_zero():
															handle_metrics('train'		,					UpperCAmelCase__		,					training_args.output_dir      )
															all_metrics.update(UpperCAmelCase__      )

															# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
															trainer.state.save_to_json(os.path.join(training_args.output_dir		,					'trainer_state.json'      )      )

															# For convenience, we also re-save the tokenizer to the same directory,
															# so that you can share your model easily on huggingface.co/models =)
															tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
					if training_args.do_eval:
										logger.info('*** Evaluate ***'      )

										__lowerCAmelCase					=							trainer.evaluate(metric_key_prefix='val'      )
										__lowerCAmelCase					=							data_args.n_val
										__lowerCAmelCase					=							round(metrics['val_loss']		,					4      )

										if trainer.is_world_process_zero():
															handle_metrics('val'		,					UpperCAmelCase__		,					training_args.output_dir      )
															all_metrics.update(UpperCAmelCase__      )

					if training_args.do_predict:
										logger.info('*** Predict ***'      )

										__lowerCAmelCase					=							trainer.predict(test_dataset=UpperCAmelCase__		,					metric_key_prefix='test'      )
										__lowerCAmelCase					=							test_output.metrics
										__lowerCAmelCase					=							data_args.n_test

										if trainer.is_world_process_zero():
															__lowerCAmelCase					=							round(metrics['test_loss']		,					4      )
															handle_metrics('test'		,					UpperCAmelCase__		,					training_args.output_dir      )
															all_metrics.update(UpperCAmelCase__      )

															if training_args.predict_with_generate:
																				__lowerCAmelCase					=							tokenizer.batch_decode(
																				    test_output.predictions		,					skip_special_tokens=UpperCAmelCase__		,					clean_up_tokenization_spaces=UpperCAmelCase__      )
																				__lowerCAmelCase					=							lmap(str.strip		,					UpperCAmelCase__      )
																				write_txt_file(UpperCAmelCase__		,					os.path.join(training_args.output_dir		,					'test_generations.txt'      )      )

					if trainer.is_world_process_zero():
										save_json(UpperCAmelCase__		,					os.path.join(training_args.output_dir		,					'all_results.json'      )      )

					return all_metrics


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					main()


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,
)


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

lowerCamelCase      				=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=8      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=512		,					UpperCAmelCase__=512      ):

					"""simple docstring"""






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





class     snake_case_			(				_a							):


							"""simple docstring"""





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

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




							def        A__  ( self					,						_A					,						_A					,						_A							):
												# get the original timestep using init_timestep
												__lowerCAmelCase					=							min(int(num_inference_steps * strength							)					,						_A							)

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

												return timesteps, num_inference_steps - t_start




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A=None							):
												if not isinstance(_A					,						(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(_A							)}"""							)

												__lowerCAmelCase					=							image.to(device=_A					,						dtype=_A							)

												__lowerCAmelCase					=							batch_size * num_images_per_prompt

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

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

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

																	__lowerCAmelCase					=							self.movq.config.scaling_factor * init_latents

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

												__lowerCAmelCase					=							init_latents.shape
												__lowerCAmelCase					=							randn_tensor(_A					,						generator=_A					,						device=_A					,						dtype=_A							)

												# get latents
												__lowerCAmelCase					=							self.scheduler.add_noise(_A					,						_A					,						_A							)

												__lowerCAmelCase					=							init_latents

												return latents




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

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

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




							def        A__  ( self					,						_A=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					=							torch.device(F"""cuda:{gpu_id}"""							)

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

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

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        A__  ( self							):
												if not hasattr(self.unet					,						'_hf_hook'							):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(_A					,						'_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(_A							)
							def __call__( self					,						_A					,						_A					,						_A					,						_A = 5_1_2					,						_A = 5_1_2					,						_A = 1_0_0					,						_A = 4.0					,						_A = 0.3					,						_A = 1					,						_A = None					,						_A = "pil"					,						_A = True					,						):
												__lowerCAmelCase					=							self._execution_device

												__lowerCAmelCase					=							guidance_scale > 1.0

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
												__lowerCAmelCase					=							image_embeds.shape[0]
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)

												if do_classifier_free_guidance:
																	__lowerCAmelCase					=							image_embeds.repeat_interleave(_A					,						dim=0							)
																	__lowerCAmelCase					=							negative_image_embeds.repeat_interleave(_A					,						dim=0							)

												__lowerCAmelCase					=							torch.cat([negative_image_embeds, image_embeds]					,						dim=0							).to(dtype=self.unet.dtype					,						device=_A							)

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

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

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

																	__lowerCAmelCase					=							{'image_embeds': image_embeds}
																	__lowerCAmelCase					=							self.unet(
																	    sample=_A					,						timestep=_A					,						encoder_hidden_states=_A					,						added_cond_kwargs=_A					,						return_dict=_A					,						)[0]

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

																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCAmelCase					=							self.scheduler.step(
																	    _A					,						_A					,						_A					,						generator=_A					,						)[0]

												# post-processing
												__lowerCAmelCase					=							self.movq.decode(_A					,						force_not_quantize=_A							)['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					=							image * 0.5 + 0.5
																	__lowerCAmelCase					=							image.clamp(0					,						1							)
																	__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							).float().numpy()

												if output_type == "pil":
																	__lowerCAmelCase					=							self.numpy_to_pil(_A							)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A							)






