



"""simple docstring"""




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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase_						=       logging.get_logger(__name__)  # pylint: disable=invalid-name



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			warnings.warn(
			    "The preprocess method is deprecated and will be removed in a future version. Please"
			    " use VaeImageProcessor.preprocess instead"  ,					SCREAMING_SNAKE_CASE_  ,					)
			if isinstance(SCREAMING_SNAKE_CASE_  ,					torch.Tensor					):
						return image
			elif isinstance(SCREAMING_SNAKE_CASE_  ,					PIL.Image.Image					):
						lowercase__   					=			[image]

			if isinstance(image[0]  ,					PIL.Image.Image					):
						lowercase__					,    lowercase__   					=			image[0].size
						lowercase__					,    lowercase__   					=			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						lowercase__   					=			[np.array(i.resize((w, h)  ,					resample=PIL_INTERPOLATION["lanczos"]					)					)[None, :] for i in image]
						lowercase__   					=			np.concatenate(SCREAMING_SNAKE_CASE_  ,					axis=0					)
						lowercase__   					=			np.array(SCREAMING_SNAKE_CASE_					).astype(np.floataa					) / 255.0
						lowercase__   					=			image.transpose(0  ,					3  ,					1  ,					2					)
						lowercase__   					=			2.0 * image - 1.0
						lowercase__   					=			torch.from_numpy(SCREAMING_SNAKE_CASE_					)
			elif isinstance(image[0]  ,					torch.Tensor					):
						lowercase__   					=			torch.cat(SCREAMING_SNAKE_CASE_  ,					dim=0					)
			return image



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if isinstance(SCREAMING_SNAKE_CASE_  ,					torch.Tensor					):
						return mask
			elif isinstance(SCREAMING_SNAKE_CASE_  ,					PIL.Image.Image					):
						lowercase__   					=			[mask]

			if isinstance(mask[0]  ,					PIL.Image.Image					):
						lowercase__					,    lowercase__   					=			mask[0].size
						lowercase__					,    lowercase__   					=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						lowercase__   					=			[np.array(m.convert("L"					).resize((w, h)  ,					resample=PIL_INTERPOLATION["nearest"]					)					)[None, :] for m in mask]
						lowercase__   					=			np.concatenate(SCREAMING_SNAKE_CASE_  ,					axis=0					)
						lowercase__   					=			mask.astype(np.floataa					) / 255.0
						lowercase__   					=			0
						lowercase__   					=			1
						lowercase__   					=			torch.from_numpy(SCREAMING_SNAKE_CASE_					)
			elif isinstance(mask[0]  ,					torch.Tensor					):
						lowercase__   					=			torch.cat(SCREAMING_SNAKE_CASE_  ,					dim=0					)
			return mask




class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   UNetaDModel
				UpperCamelCase__			:   RePaintScheduler




				def __init__(  self   :  Tuple,   __lowercase   :  Any,   __lowercase   :  Tuple  ):
							super().__init__()
							self.register_modules(unet=__lowercase,   scheduler=__lowercase  )




				@torch.no_grad()
				def __call__(  self   :  Union[str, Any],   __lowercase   :  Union[torch.Tensor, PIL.Image.Image],   __lowercase   :  Union[torch.Tensor, PIL.Image.Image],   __lowercase   :  int = 250,   __lowercase   :  float = 0.0,   __lowercase   :  int = 10,   __lowercase   :  int = 10,   __lowercase   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None,   __lowercase   :  Optional[str] = "pil",   __lowercase   :  bool = True,   ):
							lowercase__   					=			image

							lowercase__   					=			_preprocess_image(__lowercase  )
							lowercase__   					=			original_image.to(device=self.device,   dtype=self.unet.dtype  )
							lowercase__   					=			_preprocess_mask(__lowercase  )
							lowercase__   					=			mask_image.to(device=self.device,   dtype=self.unet.dtype  )

							lowercase__   					=			original_image.shape[0]

							# sample gaussian noise to begin the loop
							if isinstance(__lowercase,   __lowercase  ) and len(__lowercase  ) != batch_size:
										raise ValueError(
										    F'''You have passed a list of generators of length {len(__lowercase  )}, but requested an effective batch'''
										    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

							lowercase__   					=			original_image.shape
							lowercase__   					=			randn_tensor(__lowercase,   generator=__lowercase,   device=self.device,   dtype=self.unet.dtype  )

							# set step values
							self.scheduler.set_timesteps(__lowercase,   __lowercase,   __lowercase,   self.device  )
							lowercase__   					=			eta

							lowercase__   					=			self.scheduler.timesteps[0] + 1
							lowercase__   					=			generator[0] if isinstance(__lowercase,   __lowercase  ) else generator
							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
										if t < t_last:
													# predict the noise residual
													lowercase__   					=			self.unet(__lowercase,   __lowercase  ).sample
													# compute previous image: x_t -> x_t-1
													lowercase__   					=			self.scheduler.step(__lowercase,   __lowercase,   __lowercase,   __lowercase,   __lowercase,   __lowercase  ).prev_sample

										else:
													# compute the reverse: x_t-1 -> x_t
													lowercase__   					=			self.scheduler.undo_step(__lowercase,   __lowercase,   __lowercase  )
										lowercase__   					=			t

							lowercase__   					=			(image / 2 + 0.5).clamp(0,   1  )
							lowercase__   					=			image.cpu().permute(0,   2,   3,   1  ).numpy()
							if output_type == "pil":
										lowercase__   					=			self.numpy_to_pil(__lowercase  )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=__lowercase  )









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


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[Any]       						="""transfo-xl"""
				UpperCamelCase__			:   Dict       						=["""mems"""]
				UpperCamelCase__			:   Optional[int]       						={
				    """n_token""": """vocab_size""",
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}




				def __init__(  self   :  Optional[Any],   __lowercase   :  Optional[Any]=26_7735,   __lowercase   :  int=[2_0000, 4_0000, 20_0000],   __lowercase   :  Union[str, Any]=1024,   __lowercase   :  Tuple=1024,   __lowercase   :  Tuple=16,   __lowercase   :  Optional[Any]=64,   __lowercase   :  str=4096,   __lowercase   :  Optional[int]=4,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  Union[str, Any]=18,   __lowercase   :  List[str]=1600,   __lowercase   :  List[Any]=1000,   __lowercase   :  Union[str, Any]=True,   __lowercase   :  Tuple=True,   __lowercase   :  Optional[Any]=0,   __lowercase   :  List[str]=-1,   __lowercase   :  int=True,   __lowercase   :  Dict=0.1,   __lowercase   :  Union[str, Any]=0.0,   __lowercase   :  str=True,   __lowercase   :  Optional[Any]="normal",   __lowercase   :  str=0.01,   __lowercase   :  Tuple=0.01,   __lowercase   :  List[Any]=0.02,   __lowercase   :  Any=1e-5,   __lowercase   :  Union[str, Any]=0,   **__lowercase   :  Union[str, Any],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			[]
							self.cutoffs.extend(__lowercase  )
							if proj_share_all_but_first:
										lowercase__   					=			[False] + [True] * len(self.cutoffs  )
							else:
										lowercase__   					=			[False] + [False] * len(self.cutoffs  )
							lowercase__   					=			d_model
							lowercase__   					=			d_embed
							lowercase__   					=			d_head
							lowercase__   					=			d_inner
							lowercase__   					=			div_val
							lowercase__   					=			pre_lnorm
							lowercase__   					=			n_layer
							lowercase__   					=			n_head
							lowercase__   					=			mem_len
							lowercase__   					=			same_length
							lowercase__   					=			attn_type
							lowercase__   					=			clamp_len
							lowercase__   					=			sample_softmax
							lowercase__   					=			adaptive
							lowercase__   					=			dropout
							lowercase__   					=			dropatt
							lowercase__   					=			untie_r
							lowercase__   					=			init
							lowercase__   					=			init_range
							lowercase__   					=			proj_init_std
							lowercase__   					=			init_std
							lowercase__   					=			layer_norm_epsilon
							super().__init__(eos_token_id=__lowercase,   **__lowercase  )




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							# Message copied from Transformer-XL documentation
							logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )
							return -1




				@max_position_embeddings.setter
				def 							A__ (  self   :  List[str],   __lowercase   :  Union[str, Any]  ):
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )




