





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  __magic_name__  (				lowercase				)				-> Any:

							"""simple docstring"""



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




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   )       -> List[Any]:






									"""simple docstring"""
									super().__init__()
									self.register_modules(vqvae=snake_case__,   unet=snake_case__,   scheduler=snake_case__  )






		@torch.no_grad()
		def __call__(							self,   snake_case__ = None,   snake_case__ = 1,   snake_case__ = 1_00,   snake_case__ = 0.0,   snake_case__ = None,   snake_case__ = "pil",   snake_case__ = True,   )       -> Union[Tuple, ImagePipelineOutput]:






									"""simple docstring"""
									if isinstance(snake_case__,   PIL.Image.Image  ):
																lowercase_	:   Optional[Any]  				=     1
									elif isinstance(snake_case__,   torch.Tensor  ):
																lowercase_	:   Any  				=     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  ):
																lowercase_	:   List[Any]  				=     preprocess(snake_case__  )

									lowercase_				,		lowercase_	:   List[Any]  				=     image.shape[-2:]

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

									lowercase_	:   str  				=     randn_tensor(snake_case__,   generator=snake_case__,   device=self.device,   dtype=snake_case__  )

									lowercase_	:   Union[str, Any]  				=     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  )
									lowercase_	:   Union[str, Any]  				=     self.scheduler.timesteps

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

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

									# decode the image latents with the VQVAE
									lowercase_	:   Tuple  				=     self.vqvae.decode(snake_case__  ).sample
									lowercase_	:   Tuple  				=     torch.clamp(snake_case__,   -1.0,   1.0  )
									lowercase_	:   str  				=     image / 2 + 0.5
									lowercase_	:   List[str]  				=     image.cpu().permute(0,   2,   3,   1  ).numpy()

									if output_type == "pil":
																lowercase_	:   Any  				=     self.numpy_to_pil(snake_case__  )

									if not return_dict:
																return (image,)

									return ImagePipelineOutput(images=snake_case__  )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Optional[int]       =	"""canine"""






		def __init__(							self,   snake_case__=7_68,   snake_case__=12,   snake_case__=12,   snake_case__=30_72,   snake_case__="gelu",   snake_case__=0.1,   snake_case__=0.1,   snake_case__=1_63_84,   snake_case__=16,   snake_case__=0.02,   snake_case__=1E-12,   snake_case__=0,   snake_case__=0XE_0_0_0,   snake_case__=0XE_0_0_1,   snake_case__=4,   snake_case__=4,   snake_case__=8,   snake_case__=1_63_84,   snake_case__=1_28,   **snake_case__,   )       -> Optional[int]:






									"""simple docstring"""
									super().__init__(pad_token_id=snake_case__,   bos_token_id=snake_case__,   eos_token_id=snake_case__,   **snake_case__  )

									lowercase_	:   int  				=     max_position_embeddings
									lowercase_	:   Union[str, Any]  				=     hidden_size
									lowercase_	:   Optional[Any]  				=     num_hidden_layers
									lowercase_	:   str  				=     num_attention_heads
									lowercase_	:   List[Any]  				=     intermediate_size
									lowercase_	:   Optional[Any]  				=     hidden_act
									lowercase_	:   List[str]  				=     hidden_dropout_prob
									lowercase_	:   Any  				=     attention_probs_dropout_prob
									lowercase_	:   Union[str, Any]  				=     initializer_range
									lowercase_	:   Optional[Any]  				=     type_vocab_size
									lowercase_	:   Dict  				=     layer_norm_eps

									# Character config:
									lowercase_	:   Optional[int]  				=     downsampling_rate
									lowercase_	:   List[str]  				=     upsampling_kernel_size
									lowercase_	:   int  				=     num_hash_functions
									lowercase_	:   List[str]  				=     num_hash_buckets
									lowercase_	:   List[str]  				=     local_transformer_stride