




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







class 						lowercase							(   UpperCAmelCase_		):

     def __init__(  self		,     _a		,     _a		,     _a		,     )						->							Union[str, Any]:
           super().__init__()
           self.register_modules(vqvae=lowerCAmelCase__		,     unet=lowerCAmelCase__		,     scheduler=lowerCAmelCase__				)



     @torch.no_grad()
     def __call__(  self		,     _a = None		,     _a = 1		,     _a = 100		,     _a = 0.0		,     _a = None		,     _a = "pil"		,     _a = True		,     )						->							Dict:
           if isinstance(lowerCAmelCase__		,     PIL.Image.Image				):
                 _A							:     List[str]			=						1
           elif isinstance(lowerCAmelCase__		,     torch.Tensor				):
                 _A							:     str			=						image.shape[0]
           else:
                 raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__				)}'''				)

           if isinstance(lowerCAmelCase__		,     PIL.Image.Image				):
                 _A							:     Dict			=						preprocess(lowerCAmelCase__				)

           _A							:     Optional[int]			=						image.shape[-2:]

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

           _A							:     Dict			=						randn_tensor(lowerCAmelCase__		,     generator=lowerCAmelCase__		,     device=self.device		,     dtype=lowerCAmelCase__				)

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

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

           # scale the initial noise by the standard deviation required by the scheduler
           _A							:     List[str]			=						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]
           _A							:     List[str]			=						"eta" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
           _A							:     Optional[Any]			=						{}
           if accepts_eta:
                 _A							:     int			=						eta

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

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

           if output_type == "pil":
                 _A							:     Dict			=						self.numpy_to_pil(lowerCAmelCase__				)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=lowerCAmelCase__				)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									# For consistency across different places the DisjunctiveConstraint is called,
									# dc.token_ids is a list of integers. It is also initialized only by integers.

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [[1, 2, 4], [1, 2, 3, 4]]
									SCREAMING_SNAKE_CASE_: Any  		=       DisjunctiveConstraint(lowerCAmelCase__)
									self.assertTrue(isinstance(dc.token_ids   ,						lowerCAmelCase__))

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									# We can't have constraints that are complete subsets of another. This leads to a preverse
									# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
									# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
									# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
									# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2], [1, 2, 3, 4]]

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint(lowerCAmelCase__)  # fails here



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       [[1, 2, 3], [1, 2, 4]]

									SCREAMING_SNAKE_CASE_: Tuple  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       dc.update(1)
									SCREAMING_SNAKE_CASE_: Dict  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       dc.update(2)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(3)
									SCREAMING_SNAKE_CASE_: Tuple  		=       stepped is True and completed is True and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 3])




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

									SCREAMING_SNAKE_CASE_: List[Any]  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       dc.update(2)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(4)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2, 4])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dc.update(5)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 4, 5])

									dc.reset()

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.remaining() == 3)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(2)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.remaining() == 2)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       dc.update(5)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.remaining() == 0)
									self.assertTrue(dc.current_seq == [1, 2, 5])






