





"""simple docstring"""





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,
)


_UpperCamelCase							=      logging.getLogger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE_							:
    """simple docstring"""
    __snake_case   :       str	 =		field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
    __snake_case   :       Optional[str]	 =		field(
        default=snake_case__       ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
    __snake_case   :       Optional[str]	 =		field(
        default=snake_case__       ,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
    __snake_case   :       Optional[str]	 =		field(
        default=snake_case__       ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,  )
    __snake_case   :       bool	 =		field(default=snake_case__       ,  metadata={"""help""": """Whether tp freeze the encoder."""}   )
    __snake_case   :       bool	 =		field(default=snake_case__       ,  metadata={"""help""": """Whether  to freeze the embeddings."""}   )

@dataclass
class 		SCREAMING_SNAKE_CASE_							:
    """simple docstring"""
    __snake_case   :       str	 =		field(
        metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
    __snake_case   :       Optional[str]	 =		field(
        default="""summarization"""       ,  metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}       ,  )
    __snake_case   :       Optional[int]	 =		field(
        default=10_24       ,  metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }       ,  )
    __snake_case   :       Optional[int]	 =		field(
        default=1_28       ,  metadata={
            """help""": (
                """The maximum total sequence length for target text after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }       ,  )
    __snake_case   :       Optional[int]	 =		field(
        default=1_42       ,  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``."""
            )
        }       ,  )
    __snake_case   :       Optional[int]	 =		field(
        default=1_42       ,  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."""
            )
        }       ,  )
    __snake_case   :       Optional[int]	 =		field(default=-1       ,  metadata={"""help""": """# training examples. -1 means use all."""}   )
    __snake_case   :       Optional[int]	 =		field(default=-1       ,  metadata={"""help""": """# validation examples. -1 means use all."""}   )
    __snake_case   :       Optional[int]	 =		field(default=-1       ,  metadata={"""help""": """# test examples. -1 means use all."""}   )
    __snake_case   :       Optional[str]	 =		field(default=snake_case__       ,  metadata={"""help""": """Source language id for translation."""}   )
    __snake_case   :       Optional[str]	 =		field(default=snake_case__       ,  metadata={"""help""": """Target language id for translation."""}   )
    __snake_case   :       Optional[int]	 =		field(default=snake_case__       ,  metadata={"""help""": """# num_beams to use for evaluation."""}   )
    __snake_case   :       bool	 =		field(
        default=snake_case__       ,  metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}       ,  )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Any   ):


       '''simple docstring'''







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



def 				lowerCAmelCase_			(      ):


       '''simple docstring'''







       __lowerCamelCase      :      Optional[int]         =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      :      Optional[int]         =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
       else:
              __lowerCamelCase      :      Tuple         =parser.parse_args_into_dataclasses()

       check_output_dir(SCREAMING_SNAKE_CASE   )

       # 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'''    ,						SCREAMING_SNAKE_CASE   )

       # 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      :      Optional[Any]         =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      :      Union[str, Any]         =('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
       for p in extra_model_params:
              if getattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
                     assert hasattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
                     setattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						getattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )   )

       __lowerCamelCase      :      Union[str, Any]         =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      :      List[str]         =AutoModelForSeqaSeqLM.from_pretrained(
           model_args.model_name_or_path    ,						from_tf='''.ckpt''' in model_args.model_name_or_path    ,						config=SCREAMING_SNAKE_CASE    ,						cache_dir=model_args.cache_dir    ,						)

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

       # set num_beams for evaluation
       if data_args.eval_beams is None:
              __lowerCamelCase      :      List[str]         =model.config.num_beams

       # set decoder_start_token_id for MBart
       if model.config.decoder_start_token_id is None and isinstance(SCREAMING_SNAKE_CASE    ,						(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(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
                     __lowerCamelCase      :      Union[str, Any]         =tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     __lowerCamelCase      :      Optional[int]         =tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

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

       __lowerCamelCase      :      Tuple         =SeqaSeqDataset

       # Get datasets
       __lowerCamelCase      :      List[str]         =(
           dataset_class(
               SCREAMING_SNAKE_CASE    ,						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      :      Optional[Any]         =(
           dataset_class(
               SCREAMING_SNAKE_CASE    ,						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      :      Any         =(
           dataset_class(
               SCREAMING_SNAKE_CASE    ,						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      :      str         =(
           build_compute_metrics_fn(data_args.task    ,						SCREAMING_SNAKE_CASE   ) if training_args.predict_with_generate else None
       )
       __lowerCamelCase      :      Dict         =SeqaSeqTrainer(
           model=SCREAMING_SNAKE_CASE    ,						args=SCREAMING_SNAKE_CASE    ,						data_args=SCREAMING_SNAKE_CASE    ,						train_dataset=SCREAMING_SNAKE_CASE    ,						eval_dataset=SCREAMING_SNAKE_CASE    ,						data_collator=SeqaSeqDataCollator(
               SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						model.config.decoder_start_token_id    ,						training_args.tpu_num_cores   )    ,						compute_metrics=SCREAMING_SNAKE_CASE    ,						tokenizer=SCREAMING_SNAKE_CASE    ,						)

       __lowerCamelCase      :      Union[str, Any]         ={}
       # Training
       if training_args.do_train:
              logger.info('''*** Train ***'''   )

              __lowerCamelCase      :      Dict         =trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
              __lowerCamelCase      :      Dict         =train_result.metrics
              __lowerCamelCase      :      Optional[Any]         =data_args.n_train

              trainer.save_model()  # this also saves the tokenizer

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

                     # 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      :      Any         =trainer.evaluate(metric_key_prefix='''val'''   )
              __lowerCamelCase      :      int         =data_args.n_val
              __lowerCamelCase      :      Tuple         =round(metrics['''val_loss''']    ,						4   )

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

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

              __lowerCamelCase      :      Tuple         =trainer.predict(test_dataset=SCREAMING_SNAKE_CASE    ,						metric_key_prefix='''test'''   )
              __lowerCamelCase      :      Optional[int]         =test_output.metrics
              __lowerCamelCase      :      List[Any]         =data_args.n_test

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

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

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

       return all_metrics



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple   ):


       '''simple docstring'''







       main()


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			)




