"""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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> List[Any]:
				SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__			=					image.size
				SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__			=					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE__			=					image.resize((w, h)	,       resample=PIL_INTERPOLATION["lanczos"]							)
				SCREAMING_SNAKE_CASE__			=					np.array(__snake_case							).astype(np.floataa							) / 255.0
				SCREAMING_SNAKE_CASE__			=					image[None].transpose(0	,       3	,       1	,       2							)
				SCREAMING_SNAKE_CASE__			=					torch.from_numpy(__snake_case							)
				return 2.0 * image - 1.0

class lowerCamelCase		(_SCREAMING_SNAKE_CASE   ):






					'''simple docstring'''


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



					@torch.no_grad()
					def __call__(    self      :					Tuple ,			_snake_case      :					Union[torch.Tensor, PIL.Image.Image] = None ,			_snake_case      :					Optional[int] = 1 ,			_snake_case      :					Optional[int] = 100 ,			_snake_case      :					Optional[float] = 0.0 ,			_snake_case      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,			_snake_case      :					Optional[str] = "pil" ,			_snake_case      :					bool = True ,			)				->							Union[Tuple, ImagePipelineOutput]:
									if isinstance(_snake_case ,			PIL.Image.Image		):
													SCREAMING_SNAKE_CASE__			=					1
									elif isinstance(_snake_case ,			torch.Tensor		):
													SCREAMING_SNAKE_CASE__			=					image.shape[0]
									else:
													raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_snake_case		)}"""		)

									if isinstance(_snake_case ,			PIL.Image.Image		):
													SCREAMING_SNAKE_CASE__			=					preprocess(_snake_case		)

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

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

									SCREAMING_SNAKE_CASE__			=					randn_tensor(_snake_case ,			generator=_snake_case ,			device=self.device ,			dtype=_snake_case		)

									SCREAMING_SNAKE_CASE__			=					image.to(device=self.device ,			dtype=_snake_case		)

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

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

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

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

									if output_type == "pil":
													SCREAMING_SNAKE_CASE__			=					self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)