
'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	image.size
 lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
 lowerCAmelCase__     :   Dict     		=	image.resize((w, h)      ,			resample=PIL_INTERPOLATION["lanczos"]			)
 lowerCAmelCase__     :   Any     		=	np.array(lowerCamelCase_			).astype(np.floataa			) / 255.0
 lowerCAmelCase__     :   Union[str, Any]     		=	image[None].transpose(0      ,			3      ,			1      ,			2			)
 lowerCAmelCase__     :   Tuple     		=	torch.from_numpy(lowerCamelCase_			)
 return 2.0 * image - 1.0







class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Optional[Any]			,							a__  : VQModel			,							a__  : UNetaDModel			,							a__  : Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ]			,							):
  '''simple docstring'''

  super().__init__()
  self.register_modules(vqvae=a__			,							unet=a__			,							scheduler=a__    )


 @torch.no_grad()
 def __call__(    self  : int			,							a__  : Union[torch.Tensor, PIL.Image.Image] = None			,							a__  : Optional[int] = 1			,							a__  : Optional[int] = 100			,							a__  : Optional[float] = 0.0			,							a__  : Optional[Union[torch.Generator, List[torch.Generator]]] = None			,							a__  : Optional[str] = "pil"			,							a__  : bool = True			,							):
  '''simple docstring'''

  if isinstance(a__			,							PIL.Image.Image    ):
   lowerCAmelCase__     :   Any     		=	1
  elif isinstance(a__			,							torch.Tensor    ):
   lowerCAmelCase__     :   Optional[Any]     		=	image.shape[0]
  else:
   raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(a__    )}'''    )

  if isinstance(a__			,							PIL.Image.Image    ):
   lowerCAmelCase__     :   Optional[int]     		=	preprocess(a__    )

  lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	image.shape[-2:]

  # in_channels should be 6: 3 for latents, 3 for low resolution image
  lowerCAmelCase__     :   Optional[Any]     		=	(batch_size, self.unet.config.in_channels // 2, height, width)
  lowerCAmelCase__     :   str     		=	next(self.unet.parameters()    ).dtype

  lowerCAmelCase__     :   Tuple     		=	randn_tensor(a__			,							generator=a__			,							device=self.device			,							dtype=a__    )

  lowerCAmelCase__     :   str     		=	image.to(device=self.device			,							dtype=a__    )

  # set timesteps and move to the correct device
  self.scheduler.set_timesteps(a__			,							device=self.device    )
  lowerCAmelCase__     :   List[Any]     		=	self.scheduler.timesteps

  # scale the initial noise by the standard deviation required by the scheduler
  lowerCAmelCase__     :   Optional[Any]     		=	latents * self.scheduler.init_noise_sigma

  # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
  # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
  # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
  # and should be between [0, 1]
  lowerCAmelCase__     :   Union[str, Any]     		=	"eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
  lowerCAmelCase__     :   Dict     		=	{}
  if accepts_eta:
   lowerCAmelCase__     :   str     		=	eta

  for t in self.progress_bar(a__    ):
   # concat latents and low resolution image in the channel dimension.
   lowerCAmelCase__     :   Any     		=	torch.cat([latents, image]			,							dim=1    )
   lowerCAmelCase__     :   str     		=	self.scheduler.scale_model_input(a__			,							a__    )
   # predict the noise residual
   lowerCAmelCase__     :   List[Any]     		=	self.unet(a__			,							a__    ).sample
   # compute the previous noisy sample x_t -> x_t-1
   lowerCAmelCase__     :   Tuple     		=	self.scheduler.step(a__			,							a__			,							a__			,							**a__    ).prev_sample

  # decode the image latents with the VQVAE
  lowerCAmelCase__     :   Optional[Any]     		=	self.vqvae.decode(a__    ).sample
  lowerCAmelCase__     :   Optional[int]     		=	torch.clamp(a__			,							-1.0			,							1.0    )
  lowerCAmelCase__     :   Any     		=	image / 2 + 0.5
  lowerCAmelCase__     :   Optional[Any]     		=	image.cpu().permute(0			,							2			,							3			,							1    ).numpy()

  if output_type == "pil":
   lowerCAmelCase__     :   Tuple     		=	self.numpy_to_pil(a__    )

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=a__    )







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			"""mobilenet_v2"""


 def __init__(    self  : Optional[int]			,							a__  : Dict=3			,							a__  : str=224			,							a__  : int=1.0			,							a__  : Dict=8			,							a__  : Dict=8			,							a__  : Dict=6			,							a__  : Optional[Any]=32			,							a__  : List[str]=True			,							a__  : int=True			,							a__  : List[str]="relu6"			,							a__  : List[str]=True			,							a__  : List[Any]=0.8			,							a__  : Tuple=0.02			,							a__  : Tuple=0.001			,							a__  : Optional[Any]=255			,							**a__  : str			,							):
  '''simple docstring'''

  super().__init__(**a__    )

  if depth_multiplier <= 0:
   raise ValueError("depth_multiplier must be greater than zero."    )

  lowerCAmelCase__     :   Any     		=	num_channels
  lowerCAmelCase__     :   Union[str, Any]     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	depth_multiplier
  lowerCAmelCase__     :   Optional[int]     		=	depth_divisible_by
  lowerCAmelCase__     :   Union[str, Any]     		=	min_depth
  lowerCAmelCase__     :   List[str]     		=	expand_ratio
  lowerCAmelCase__     :   Union[str, Any]     		=	output_stride
  lowerCAmelCase__     :   Optional[int]     		=	first_layer_is_expansion
  lowerCAmelCase__     :   Tuple     		=	finegrained_output
  lowerCAmelCase__     :   Optional[int]     		=	hidden_act
  lowerCAmelCase__     :   Optional[Any]     		=	tf_padding
  lowerCAmelCase__     :   Union[str, Any]     		=	classifier_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	initializer_range
  lowerCAmelCase__     :   str     		=	layer_norm_eps
  lowerCAmelCase__     :   int     		=	semantic_loss_ignore_index




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[int]	=			version.parse("""1.11"""  )


 @property
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  return OrderedDict([("pixel_values", {0: "batch"})]    )


 @property
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  if self.task == "image-classification":
   return OrderedDict([("logits", {0: "batch"})]    )
  else:
   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]    )


 @property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return 1e-4





