






'''simple docstring'''
import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


UpperCamelCase__ =			HfApi()

UpperCamelCase__ =			{}
# fmt: off
UpperCamelCase__ =			torch.tensor([
    -0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1.17_43, -3.74_67,
    1.23_42, -2.24_85, 0.46_36, 0.80_76, -0.79_91, 0.39_69, 0.84_98, 0.91_89,
    -1.88_87, -3.35_22, 0.76_39, 0.20_40, 0.62_71, -2.71_48, -1.63_16, 3.08_39,
    0.31_86, 0.27_21, -0.97_59, -1.24_61, 2.62_57, 1.35_57
])
UpperCamelCase__ =			torch.tensor([
    -2.36_39, -2.53_44, 0.00_54, -0.66_74, 1.59_90, 1.01_58, 0.31_24, -2.14_36,
    1.87_95, -2.54_29, -0.15_66, -0.39_73, 1.24_90, 2.64_47, 1.22_83, -0.52_08,
    -2.81_54, -3.51_19, 2.38_38, 1.20_33, 1.72_01, -2.12_56, -1.45_76, 2.79_48,
    2.42_04, -0.97_52, -1.25_46, 0.80_27, 3.27_58, 3.13_65
])
UpperCamelCase__ =			torch.tensor([
    -0.65_31, -0.68_91, -0.31_72, -0.53_75, -0.91_40, -0.53_67, -0.11_75, -0.78_69,
    -0.38_08, -0.45_13, -0.20_98, -0.00_83, 0.31_83, 0.51_40, 0.22_47, -0.13_04,
    -0.13_02, -0.28_02, -0.20_84, -0.20_25, -0.49_67, -0.48_73, -0.08_61, 0.69_25,
    0.02_50, 0.12_90, -0.15_43, 0.63_16, 1.04_60, 1.49_43
])
UpperCamelCase__ =			torch.tensor([
    0.09_11, 0.11_07, 0.01_82, 0.04_35, -0.08_05, -0.06_08, 0.03_81, 0.21_72,
    -0.02_80, 0.13_27, -0.02_99, -0.02_55, -0.00_50, -0.11_70, -0.10_46, 0.03_09,
    0.13_67, 0.17_28, -0.05_33, -0.07_48, -0.05_34, 0.16_24, 0.03_84, -0.18_05,
    -0.07_07, 0.06_42, 0.02_20, -0.01_34, -0.13_33, -0.15_05
])
UpperCamelCase__ =			torch.tensor([
    0.13_21, 0.13_37, 0.04_40, 0.06_22, -0.05_91, -0.03_70, 0.05_03, 0.21_33,
    -0.01_77, 0.14_15, -0.01_16, -0.01_12, 0.00_44, -0.09_80, -0.07_89, 0.03_95,
    0.15_02, 0.17_85, -0.04_88, -0.05_14, -0.04_04, 0.15_39, 0.04_54, -0.15_59,
    -0.06_65, 0.06_59, 0.03_83, -0.00_05, -0.12_66, -0.13_86
])
UpperCamelCase__ =			torch.tensor([
    0.11_54, 0.12_18, 0.03_07, 0.05_26, -0.07_11, -0.05_41, 0.03_66, 0.20_78,
    -0.02_67, 0.13_17, -0.02_26, -0.01_93, -0.00_14, -0.10_55, -0.09_02, 0.03_30,
    0.13_91, 0.17_09, -0.05_62, -0.06_93, -0.05_60, 0.14_82, 0.03_81, -0.16_83,
    -0.06_81, 0.06_61, 0.03_31, -0.00_46, -0.12_68, -0.14_31
])
UpperCamelCase__ =			torch.tensor([
    0.11_92, 0.12_40, 0.04_14, 0.06_06, -0.05_57, -0.04_12, 0.04_30, 0.20_42,
    -0.02_00, 0.13_85, -0.01_15, -0.01_32, 0.00_17, -0.09_65, -0.08_02, 0.03_98,
    0.14_33, 0.17_47, -0.04_58, -0.05_33, -0.04_07, 0.15_45, 0.04_19, -0.15_74,
    -0.06_45, 0.06_26, 0.03_41, -0.00_10, -0.11_99, -0.13_90
])
UpperCamelCase__ =			torch.tensor([
    0.10_75, 0.10_74, 0.02_05, 0.04_31, -0.07_74, -0.06_07, 0.02_98, 0.20_42,
    -0.03_20, 0.12_67, -0.02_81, -0.02_50, -0.00_64, -0.10_91, -0.09_46, 0.02_90,
    0.13_28, 0.16_50, -0.05_80, -0.07_38, -0.05_86, 0.14_40, 0.03_37, -0.17_46,
    -0.07_12, 0.06_05, 0.02_50, -0.00_99, -0.13_16, -0.14_73
])
UpperCamelCase__ =			torch.tensor([
    -1.45_72, -2.04_81, -0.04_14, -0.60_05, 1.41_36, 0.58_48, 0.40_28, -2.73_30,
    1.22_12, -2.12_28, 0.21_55, 0.40_39, 0.76_62, 2.05_35, 0.74_77, -0.32_43,
    -2.17_58, -2.76_48, 1.69_47, 0.70_26, 1.23_38, -1.60_78, -0.86_82, 2.28_10,
    1.85_74, -0.57_18, -0.55_86, -0.01_86, 2.34_15, 2.12_51])
UpperCamelCase__ =			torch.tensor([
    -1.36_90, -1.97_20, -0.40_90, -0.69_66, 1.46_60, 0.99_38, -0.13_85, -2.73_24,
    0.77_36, -1.89_17, 0.29_23, 0.42_93, 0.16_93, 1.41_12, 1.18_87, -0.31_81,
    -2.21_60, -2.63_81, 1.31_70, 0.81_63, 0.92_40, -1.65_44, -0.60_99, 2.52_59,
    1.64_30, -0.90_90, -0.93_92, -0.01_26, 2.42_68, 2.32_66
])
UpperCamelCase__ =			torch.tensor([
    -1.35_25, -1.96_28, -0.39_56, -0.68_60, 1.46_64, 1.00_14, -0.12_59, -2.72_12,
    0.77_72, -1.88_11, 0.29_96, 0.43_88, 0.17_04, 1.40_29, 1.17_01, -0.30_27,
    -2.20_53, -2.62_87, 1.33_50, 0.81_31, 0.92_74, -1.62_92, -0.60_98, 2.51_31,
    1.65_05, -0.89_58, -0.92_98, -0.01_51, 2.42_57, 2.33_55
])
UpperCamelCase__ =			torch.tensor([
    -2.05_85, -2.78_97, -0.28_50, -0.89_40, 1.90_52, 0.57_02, 0.63_45, -3.89_59,
    1.59_32, -3.23_19, 0.19_74, 0.02_87, 1.75_66, 2.65_43, 0.83_87, -0.53_51,
    -3.27_36, -4.33_75, 2.90_29, 1.63_90, 1.46_40, -2.17_01, -1.90_13, 2.93_41,
    3.49_81, -0.62_55, -1.16_44, -0.15_91, 3.70_97, 3.20_66
])
UpperCamelCase__ =			torch.tensor([
    -2.31_39, -2.55_94, -0.01_97, -0.67_85, 1.70_01, 1.16_06, 0.30_75, -2.17_40,
    1.80_71, -2.56_30, -0.09_26, -0.38_11, 1.21_16, 2.62_46, 1.27_31, -0.53_98,
    -2.81_53, -3.61_40, 2.38_93, 1.32_62, 1.62_58, -2.18_56, -1.32_67, 2.83_95,
    2.37_79, -1.06_23, -1.24_68, 0.89_59, 3.33_67, 3.22_43
])
UpperCamelCase__ =			torch.tensor([
    -2.06_28, -2.76_67, -0.20_89, -0.82_63, 2.05_39, 0.59_92, 0.64_95, -3.83_36,
    1.60_25, -3.28_17, 0.17_21, -0.06_33, 1.75_16, 2.70_39, 0.81_00, -0.59_08,
    -3.21_13, -4.43_43, 2.92_57, 1.36_32, 1.55_62, -2.14_89, -1.98_94, 3.05_60,
    3.33_96, -0.73_28, -1.04_17, 0.03_83, 3.70_93, 3.23_43
])
UpperCamelCase__ =			torch.tensor([
    -1.45_74, -2.05_69, -0.04_73, -0.61_17, 1.40_18, 0.57_69, 0.41_29, -2.73_44,
    1.22_41, -2.13_97, 0.20_00, 0.39_37, 0.76_16, 2.04_53, 0.73_24, -0.33_91,
    -2.17_46, -2.77_44, 1.69_63, 0.69_21, 1.21_87, -1.61_72, -0.88_77, 2.24_39,
    1.84_71, -0.58_39, -0.56_05, -0.04_64, 2.32_50, 2.12_19
])
# fmt: on

UpperCamelCase__ =			api.list_models(filter='diffusers')
for mod in models:
       if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
              UpperCamelCase__ =			'/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]

              print(f"""Started running {mod.modelId}!!!""")

              if mod.modelId.startswith('CompVis'):
                     UpperCamelCase__ =			UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')
              else:
                     UpperCamelCase__ =			UNetaDModel.from_pretrained(local_checkpoint)

              torch.manual_seed(0)
              random.seed(0)

              UpperCamelCase__ =			torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
              UpperCamelCase__ =			torch.tensor([10] * noise.shape[0])
              with torch.no_grad():
                     UpperCamelCase__ =			model(noise, time_step).sample

              assert torch.allclose(
                  logits[0, 0, 0, :30], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1e-3
              )
              print(f"""{mod.modelId} has passed successfully!!!""")












'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=8							):


   """simple docstring"""




   lowercase_	:    int           =     h // scale_factor**2
   if h % scale_factor**2 != 0:
      new_h += 1
   lowercase_	:    str           =     w // scale_factor**2
   if w % scale_factor**2 != 0:
      new_w += 1
   return new_h * scale_factor, new_w * scale_factor





class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       int  ,     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  ,     )
       lowercase_	:    Dict           =     2 ** (len(self.movq.config.block_out_channels			) - 1)




    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Tuple  ,     a  :       List[str]  ,     a  :       Optional[Any]  ,     a  :       str  ,     a  :       Tuple  ,     a  :       List[str]			):






       '''simple docstring'''






       if latents is None:
          lowercase_	:    List[str]           =     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}"""			)
          lowercase_	:    Optional[int]           =     latents.to(a			)

       lowercase_	:    str           =     latents * scheduler.init_noise_sigma
       return latents




    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       List[str]  ,     a  :       List[Any]  ,     a  :       Union[str, Any]  ,     a  :       str  ,     a  :       Tuple=None  ,     ):






       '''simple docstring'''






       lowercase_	:    Tuple           =     len(a			) if isinstance(a  ,     a			) else 1
       # get prompt text embeddings
       lowercase_	:    Any           =     self.tokenizer(
           a  ,     padding="max_length"  ,     truncation=a  ,     max_length=7_7  ,     return_attention_mask=a  ,     add_special_tokens=a  ,     return_tensors="pt"  ,     )

       lowercase_	:    Union[str, Any]           =     text_inputs.input_ids
       lowercase_	:    Tuple           =     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			):
          lowercase_	:    Optional[int]           =     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}"""			)

       lowercase_	:    List[str]           =     text_input_ids.to(a			)
       lowercase_	:    int           =     text_inputs.attention_mask.to(a			)

       lowercase_    ,		lowercase_	:    Optional[int]           =     self.text_encoder(
           input_ids=a  ,     attention_mask=a			)

       lowercase_	:    str           =     prompt_embeds.repeat_interleave(a  ,     dim=0			)
       lowercase_	:    int           =     text_encoder_hidden_states.repeat_interleave(a  ,     dim=0			)
       lowercase_	:    int           =     text_mask.repeat_interleave(a  ,     dim=0			)

       if do_classifier_free_guidance:
          lowercase_	:    List[str]
          if negative_prompt is None:
             lowercase_	:    int           =     [""] * 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			):
             lowercase_	:    Tuple           =     [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:
             lowercase_	:    Dict           =     negative_prompt

          lowercase_	:    str           =     self.tokenizer(
              a  ,     padding="max_length"  ,     max_length=7_7  ,     truncation=a  ,     return_attention_mask=a  ,     add_special_tokens=a  ,     return_tensors="pt"  ,     )
          lowercase_	:    List[Any]           =     uncond_input.input_ids.to(a			)
          lowercase_	:    Optional[int]           =     uncond_input.attention_mask.to(a			)

          lowercase_    ,		lowercase_	:    int           =     self.text_encoder(
              input_ids=a  ,     attention_mask=a			)

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

          lowercase_	:    List[str]           =     negative_prompt_embeds.shape[1]
          lowercase_	:    Dict           =     negative_prompt_embeds.repeat(1  ,     a			)
          lowercase_	:    Optional[Any]           =     negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,     a			)

          lowercase_	:    Any           =     uncond_text_encoder_hidden_states.shape[1]
          lowercase_	:    List[Any]           =     uncond_text_encoder_hidden_states.repeat(1  ,     a  ,     1			)
          lowercase_	:    Tuple           =     uncond_text_encoder_hidden_states.view(
              batch_size * num_images_per_prompt  ,     a  ,     -1			)
          lowercase_	:    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
          lowercase_	:    Optional[int]           =     torch.cat([negative_prompt_embeds, prompt_embeds]			)
          lowercase_	:    Tuple           =     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

          lowercase_	:    Any           =     torch.cat([uncond_text_mask, text_mask]			)

       return prompt_embeds, text_encoder_hidden_states, text_mask




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Optional[Any]=0			):






       '''simple docstring'''






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

       lowercase_	:    List[str]           =     torch.device(f"""cuda:{gpu_id}"""			)

       lowercase_	:    str           =     [
           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 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       List[str]=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."			)

       lowercase_	:    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)

       lowercase_	:    List[str]           =     None
       for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
          lowercase_    ,		lowercase_	:    Optional[int]           =     cpu_offload_with_hook(a  ,     a  ,     prev_module_hook=a			)

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

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




    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''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  :       Tuple  ,     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 = 5_1_2  ,     a  :       int = 5_1_2  ,     a  :       int = 1_0_0  ,     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			):
          lowercase_	:    List[str]           =     1
       elif isinstance(a  ,     a			):
          lowercase_	:    int           =     len(a			)
       else:
          raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a			)}"""			)

       lowercase_	:    Tuple           =     self._execution_device

       lowercase_	:    Dict           =     batch_size * num_images_per_prompt
       lowercase_	:    Dict           =     guidance_scale > 1.0

       lowercase_    ,		lowercase_    ,		lowercase_	:    List[str]           =     self._encode_prompt(
           a  ,     a  ,     a  ,     a  ,     a			)

       if isinstance(a  ,     a			):
          lowercase_	:    Optional[int]           =     torch.cat(a  ,     dim=0			)
       if isinstance(a  ,     a			):
          lowercase_	:    int           =     torch.cat(a  ,     dim=0			)

       if do_classifier_free_guidance:
          lowercase_	:    Optional[int]           =     image_embeds.repeat_interleave(a  ,     dim=0			)
          lowercase_	:    int           =     negative_image_embeds.repeat_interleave(a  ,     dim=0			)

       lowercase_	:    str           =     torch.cat([negative_image_embeds, image_embeds]  ,     dim=0			).to(
           dtype=prompt_embeds.dtype  ,     device=a			)

       self.scheduler.set_timesteps(a  ,     device=a			)
       lowercase_	:    List[str]           =     self.scheduler.timesteps

       lowercase_	:    str           =     self.unet.config.in_channels

       lowercase_    ,		lowercase_	:    int           =     get_new_h_w(a  ,     a  ,     self.movq_scale_factor			)

       # create initial latent
       lowercase_	:    str           =     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
          lowercase_	:    Optional[int]           =     torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

          lowercase_	:    Optional[int]           =     {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
          lowercase_	:    Optional[int]           =     self.unet(
              sample=a  ,     timestep=a  ,     encoder_hidden_states=a  ,     added_cond_kwargs=a  ,     return_dict=a  ,     )[0]

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

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

       if output_type == "pil":
          lowercase_	:    List[str]           =     self.numpy_to_pil(a			)

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=a			)




