

'''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


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


def  a__       (    _SCREAMING_SNAKE_CASE   :       Union[List, PIL.Image.Image, torch.Tensor]				)     ->					List[str]:


	"""simple docstring"""





	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				):
		UpperCAmelCase_     :      List[Any]						=  [image]

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

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


def  a__       (    _SCREAMING_SNAKE_CASE   :       Union[List, PIL.Image.Image, torch.Tensor]				)     ->					Union[str, Any]:


	"""simple docstring"""





	if isinstance(_SCREAMING_SNAKE_CASE	,				torch.Tensor				):
		return mask
	elif isinstance(_SCREAMING_SNAKE_CASE	,				PIL.Image.Image				):
		UpperCAmelCase_     :      Optional[int]						=  [mask]

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





class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	UNetaDModel
			__A  :	RePaintScheduler






			def __init__(  self			,_snake_case			,_snake_case     ):
				super().__init__()
				self.register_modules(unet=_snake_case			,scheduler=_snake_case     )






			@torch.no_grad()
			def __call__(  self			,_snake_case			,_snake_case			,_snake_case = 2_50			,_snake_case = 0.0			,_snake_case = 10			,_snake_case = 10			,_snake_case = None			,_snake_case = "pil"			,_snake_case = True			,):
				UpperCAmelCase_     :      List[str]						=  image

				UpperCAmelCase_     :      Tuple						=  _preprocess_image(_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  original_image.to(device=self.device			,dtype=self.unet.dtype     )
				UpperCAmelCase_     :      List[str]						=  _preprocess_mask(_snake_case     )
				UpperCAmelCase_     :      Dict						=  mask_image.to(device=self.device			,dtype=self.unet.dtype     )

				UpperCAmelCase_     :      Dict						=  original_image.shape[0]

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

				UpperCAmelCase_     :      List[Any]						=  original_image.shape
				UpperCAmelCase_     :      Optional[Any]						=  randn_tensor(_snake_case			,generator=_snake_case			,device=self.device			,dtype=self.unet.dtype     )

				# set step values
				self.scheduler.set_timesteps(_snake_case			,_snake_case			,_snake_case			,self.device     )
				UpperCAmelCase_     :      List[str]						=  eta

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

					else:
						# compute the reverse: x_t-1 -> x_t
						UpperCAmelCase_     :      Optional[int]						=  self.scheduler.undo_step(_snake_case			,_snake_case			,_snake_case     )
					UpperCAmelCase_     :      Dict						=  t

				UpperCAmelCase_     :      Union[str, Any]						=  (image / 2 + 0.5).clamp(0			,1     )
				UpperCAmelCase_     :      Optional[int]						=  image.cpu().permute(0			,2			,3			,1     ).numpy()
				if output_type == "pil":
					UpperCAmelCase_     :      Optional[int]						=  self.numpy_to_pil(_snake_case     )

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=_snake_case     )












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__a   :							Tuple						= "\nHuman: <<task>>\n\nAssistant: "


__a   :							Union[str, Any]						= "huggingface-tools/default-prompts"
__a   :							Optional[int]						= {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]="run"     )    ->   Optional[int]:





    """simple docstring"""

    if prompt_or_repo_id is None:
        __A       =						DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search("""\\s"""      ,					__lowercase     ) is not None:
        return prompt_or_repo_id

    __A       =						cached_file(
        __lowercase      ,					PROMPT_FILES[mode]      ,					repo_type="""dataset"""      ,					user_agent={"""agent""": agent_name}     )
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""     ) as f:
        return f.read()




