





'''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 					_a   (__SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase			,     _UpperCamelCase       			=image.size
		_UpperCamelCase			,     _UpperCamelCase       			=(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		_UpperCamelCase       			=image.resize((w, h)  ,      resample=PIL_INTERPOLATION['''lanczos''']   )
		_UpperCamelCase       			=np.array(__SCREAMING_SNAKE_CASE   ).astype(np.floataa   ) / 2_5_5.0
		_UpperCamelCase       			=image[None].transpose(0  ,      3  ,      1  ,      2   )
		_UpperCamelCase       			=torch.from_numpy(__SCREAMING_SNAKE_CASE   )
		return 2.0 * image - 1.0







class 		UpperCAmelCase   (				lowercase_):


		"""simple docstring"""







		def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					VQModel							,	UpperCamelCase__    :					UNetaDModel							,	UpperCamelCase__    :					Union[
		        DDIMScheduler,
		        PNDMScheduler,
		        LMSDiscreteScheduler,
		        EulerDiscreteScheduler,
		        EulerAncestralDiscreteScheduler,
		        DPMSolverMultistepScheduler,
		    ]							,	)     ->						Optional[Any]:
				super().__init__()
				self.register_modules(vqvae=UpperCamelCase__							,	unet=UpperCamelCase__							,	scheduler=UpperCamelCase__  )




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







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

				if isinstance(UpperCamelCase__							,	PIL.Image.Image  ):
						_UpperCamelCase       			=preprocess(UpperCamelCase__  )

				_UpperCamelCase			,     _UpperCamelCase       			=image.shape[-2:]

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

				_UpperCamelCase       			=randn_tensor(UpperCamelCase__							,	generator=UpperCamelCase__							,	device=self.device							,	dtype=UpperCamelCase__  )

				_UpperCamelCase       			=image.to(device=self.device							,	dtype=UpperCamelCase__  )

				# set timesteps and move to the correct device
				self.scheduler.set_timesteps(UpperCamelCase__							,	device=self.device  )
				_UpperCamelCase       			=self.scheduler.timesteps

				# scale the initial noise by the standard deviation required by the scheduler
				_UpperCamelCase       			=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]
				_UpperCamelCase       			='''eta''' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
				_UpperCamelCase       			={}
				if accepts_eta:
						_UpperCamelCase       			=eta

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

				# decode the image latents with the VQVAE
				_UpperCamelCase       			=self.vqvae.decode(UpperCamelCase__  ).sample
				_UpperCamelCase       			=torch.clamp(UpperCamelCase__							,	-1.0							,	1.0  )
				_UpperCamelCase       			=image / 2 + 0.5
				_UpperCamelCase       			=image.cpu().permute(0							,	2							,	3							,	1  ).numpy()

				if output_type == "pil":
						_UpperCamelCase       			=self.numpy_to_pil(UpperCamelCase__  )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=UpperCamelCase__  )













'''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


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)

__lowerCamelCase						: int		=   {
    '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 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""mobilenet_v2"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Union[str, Any]=3							,	UpperCamelCase__    :					Dict=224							,	UpperCamelCase__    :					str=1.0							,	UpperCamelCase__    :					List[Any]=8							,	UpperCamelCase__    :					Union[str, Any]=8							,	UpperCamelCase__    :					str=6							,	UpperCamelCase__    :					str=32							,	UpperCamelCase__    :					str=True							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					str="relu6"							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					List[str]=0.8							,	UpperCamelCase__    :					List[str]=0.02							,	UpperCamelCase__    :					str=0.001							,	UpperCamelCase__    :					Dict=255							,	**UpperCamelCase__    :					Tuple							,	)     ->						List[Any]:
    super().__init__(**UpperCamelCase__  )

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

    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=depth_multiplier
    _UpperCamelCase       			=depth_divisible_by
    _UpperCamelCase       			=min_depth
    _UpperCamelCase       			=expand_ratio
    _UpperCamelCase       			=output_stride
    _UpperCamelCase       			=first_layer_is_expansion
    _UpperCamelCase       			=finegrained_output
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=tf_padding
    _UpperCamelCase       			=classifier_dropout_prob
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=layer_norm_eps
    _UpperCamelCase       			=semantic_loss_ignore_index







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				version.parse("""1.11""")


  @property
  def   UpperCamelCase__      (				self    :					str  )     ->						Mapping[str, Mapping[int, str]]:
    return OrderedDict([('''pixel_values''', {0: '''batch'''})]  )


  @property
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Mapping[str, Mapping[int, str]]:
    if self.task == "image-classification":
      return OrderedDict([('''logits''', {0: '''batch'''})]  )
    else:
      return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]  )




  @property
  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						float:
    return 1E-4






