

"""simple docstring"""




from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

__UpperCAmelCase =				_symbol_database.Default()


__UpperCAmelCase =				_descriptor_pool.Default().AddSerializedFile(
    B'\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

__UpperCAmelCase =				globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
						__UpperCAmelCase =				None
						__UpperCAmelCase =				B'H\003'
						# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
						__UpperCAmelCase =				45
						__UpperCAmelCase =				1581
						__UpperCAmelCase =				1517
						__UpperCAmelCase =				1570
						__UpperCAmelCase =				1584
						__UpperCAmelCase =				1793
						__UpperCAmelCase =				1795
						__UpperCAmelCase =				1916
						__UpperCAmelCase =				1864
						__UpperCAmelCase =				1905
						__UpperCAmelCase =				1919
						__UpperCAmelCase =				2429
						__UpperCAmelCase =				2208
						__UpperCAmelCase =				2418
						__UpperCAmelCase =				2323
						__UpperCAmelCase =				2407
# @@protoc_insertion_point(module_scope)








"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

__UpperCAmelCase =				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=8      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					h // scale_factor**2
 if h % scale_factor**2 != 0:
  new_h += 1
 UpperCAmelCase__					:  Any					   =					w // scale_factor**2
 if w % scale_factor**2 != 0:
  new_w += 1
 return new_h * scale_factor, new_w * scale_factor







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			str					,A		:			MultilingualCLIP					,A		:			XLMRobertaTokenizer					,A		:			UNetaDConditionModel					,A		:			Union[DDIMScheduler, DDPMScheduler]					,A		:			VQModel					,):






   '''simple docstring'''
   super().__init__()

   self.register_modules(
       text_encoder=A					,tokenizer=A					,unet=A					,scheduler=A					,movq=A					,)
   UpperCAmelCase__					:  Optional[Any]					   =					2 ** (len(self.movq.config.block_out_channels    ) - 1)

  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			Tuple					,A		:			Dict					,A		:			int					,A		:			str					,A		:			List[str]    ):






   '''simple docstring'''
   if latents is None:
    UpperCAmelCase__					:  Any					   =					randn_tensor(A					,generator=A					,device=A					,dtype=A    )
   else:
    if latents.shape != shape:
     raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
    UpperCAmelCase__					:  int					   =					latents.to(A    )

   UpperCAmelCase__					:  Any					   =					latents * scheduler.init_noise_sigma
   return latents

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[Any]					,A		:			Optional[Any]					,A		:			str					,A		:			Optional[Any]					,A		:			str=None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					len(A    ) if isinstance(A					,A    ) else 1
   # get prompt text embeddings
   UpperCAmelCase__					:  List[Any]					   =					self.tokenizer(
       A					,padding="""max_length"""					,truncation=A					,max_length=77					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)

   UpperCAmelCase__					:  List[str]					   =					text_inputs.input_ids
   UpperCAmelCase__					:  Any					   =					self.tokenizer(A					,padding="""longest"""					,return_tensors="""pt"""    ).input_ids

   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A					,A    ):
    UpperCAmelCase__					:  List[str]					   =					self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
    logger.warning(
        """The following part of your input was truncated because CLIP can only handle sequences up to"""
        f" {self.tokenizer.model_max_length} tokens: {removed_text}"    )

   UpperCAmelCase__					:  str					   =					text_input_ids.to(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					text_inputs.attention_mask.to(A    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.text_encoder(
       input_ids=A					,attention_mask=A    )

   UpperCAmelCase__					:  Optional[int]					   =					prompt_embeds.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  Optional[int]					   =					text_encoder_hidden_states.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  List[str]					   =					text_mask.repeat_interleave(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  List[str]
    if negative_prompt is None:
     UpperCAmelCase__					:  List[Any]					   =					[""""""] * batch_size
    elif type(A    ) is not type(A    ):
     raise TypeError(
         f"`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !="
         f" {type(A    )}."    )
    elif isinstance(A					,A    ):
     UpperCAmelCase__					:  Any					   =					[negative_prompt]
    elif batch_size != len(A    ):
     raise ValueError(
         f"`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:"
         f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
         """ the batch size of `prompt`."""    )
    else:
     UpperCAmelCase__					:  List[Any]					   =					negative_prompt

    UpperCAmelCase__					:  Any					   =					self.tokenizer(
        A					,padding="""max_length"""					,max_length=77					,truncation=A					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)
    UpperCAmelCase__					:  Optional[int]					   =					uncond_input.input_ids.to(A    )
    UpperCAmelCase__					:  str					   =					uncond_input.attention_mask.to(A    )

    UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self.text_encoder(
        input_ids=A					,attention_mask=A    )

    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.shape[1]
    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.repeat(1					,A    )
    UpperCAmelCase__					:  str					   =					negative_prompt_embeds.view(batch_size * num_images_per_prompt					,A    )

    UpperCAmelCase__					:  Dict					   =					uncond_text_encoder_hidden_states.shape[1]
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.repeat(1					,A					,1    )
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.view(
        batch_size * num_images_per_prompt					,A					,-1    )
    UpperCAmelCase__					:  List[Any]					   =					uncond_text_mask.repeat_interleave(A					,dim=0    )

    # done duplicates

    # For classifier free guidance, we need to do two forward passes.
    # Here we concatenate the unconditional and text embeddings into a single batch
    # to avoid doing two forward passes
    UpperCAmelCase__					:  Any					   =					torch.cat([negative_prompt_embeds, prompt_embeds]    )
    UpperCAmelCase__					:  int					   =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

    UpperCAmelCase__					:  str					   =					torch.cat([uncond_text_mask, text_mask]    )

   return prompt_embeds, text_encoder_hidden_states, text_mask

  def    __lowercase       (  self		:			Tuple					,A		:			Dict=0    ):






   '''simple docstring'''
   if is_accelerate_available():
    from accelerate import cpu_offload
   else:
    raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

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

   UpperCAmelCase__					:  Tuple					   =					[
       self.unet,
       self.text_encoder,
       self.movq,
   ]
   for cpu_offloaded_model in models:
    if cpu_offloaded_model is not None:
     cpu_offload(A					,A    )

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]=0    ):






   '''simple docstring'''
   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__					:  List[str]					   =					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)

   UpperCAmelCase__					:  List[str]					   =					None
   for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					cpu_offload_with_hook(A					,A					,prev_module_hook=A    )

   if self.safety_checker is not None:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					cpu_offload_with_hook(self.safety_checker					,A					,prev_module_hook=A    )

   # We'll offload the last model manually.
   UpperCAmelCase__					:  Any					   =					hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   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		:			Union[str, Any]					,A		:			Union[str, List[str]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Optional[Union[str, List[str]]] = None					,A		:			int = 512					,A		:			int = 512					,A		:			int = 100					,A		:			float = 4.0					,A		:			int = 1					,A		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A		:			Optional[torch.FloatTensor] = None					,A		:			Optional[str] = "pil"					,A		:			bool = True					,):






   '''simple docstring'''

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  str					   =					1
   elif isinstance(A					,A    ):
    UpperCAmelCase__					:  Tuple					   =					len(A    )
   else:
    raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(A    )}"    )

   UpperCAmelCase__					:  Optional[int]					   =					self._execution_device

   UpperCAmelCase__					:  Dict					   =					batch_size * num_images_per_prompt
   UpperCAmelCase__					:  Union[str, Any]					   =					guidance_scale > 1.0

   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					self._encode_prompt(
       A					,A					,A					,A					,A    )

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )
   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  Optional[int]					   =					image_embeds.repeat_interleave(A					,dim=0    )
    UpperCAmelCase__					:  Tuple					   =					negative_image_embeds.repeat_interleave(A					,dim=0    )

   UpperCAmelCase__					:  List[str]					   =					torch.cat([negative_image_embeds, image_embeds]					,dim=0    ).to(
       dtype=prompt_embeds.dtype					,device=A    )

   self.scheduler.set_timesteps(A					,device=A    )
   UpperCAmelCase__					:  int					   =					self.scheduler.timesteps

   UpperCAmelCase__					:  Union[str, Any]					   =					self.unet.config.in_channels

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					get_new_h_w(A					,A					,self.movq_scale_factor    )

   # create initial latent
   UpperCAmelCase__					:  Dict					   =					self.prepare_latents(
       (batch_size, num_channels_latents, height, width)					,text_encoder_hidden_states.dtype					,A					,A					,A					,self.scheduler					,)

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

    UpperCAmelCase__					:  Optional[Any]					   =					{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
    UpperCAmelCase__					:  Any					   =					self.unet(
        sample=A					,timestep=A					,encoder_hidden_states=A					,added_cond_kwargs=A					,return_dict=A					,)[0]

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

    # compute the previous noisy sample x_t -> x_t-1
    UpperCAmelCase__					:  Optional[Any]					   =					self.scheduler.step(
        A					,A					,A					,generator=A					,).prev_sample
   # post-processing
   UpperCAmelCase__					:  List[Any]					   =					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"]:
    UpperCAmelCase__					:  Union[str, Any]					   =					image * 0.5 + 0.5
    UpperCAmelCase__					:  Optional[Any]					   =					image.clamp(0					,1    )
    UpperCAmelCase__					:  Dict					   =					image.cpu().permute(0					,2					,3					,1    ).float().numpy()

   if output_type == "pil":
    UpperCAmelCase__					:  List[str]					   =					self.numpy_to_pil(A    )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=A    )





