

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   __SCREAMING_SNAKE_CASE					(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       ):
												snake_case_   =	[image]

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

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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						if isinstance(SCREAMING_SNAKE_CASE__  ,		torch.Tensor       ):
												return mask
						elif isinstance(SCREAMING_SNAKE_CASE__  ,		PIL.Image.Image       ):
												snake_case_   =	[mask]

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





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   UNetaDModel
			SCREAMING_SNAKE_CASE     :   RePaintScheduler







			def __init__(     self :				List[str]							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Optional[int]							)      ->List[str]:
									super().__init__()
									self.register_modules(unet=_UpperCamelCase							,   scheduler=_UpperCamelCase							)







			@torch.no_grad()
			def __call__(     self :				List[Any]							,   _UpperCamelCase :				Union[torch.Tensor, PIL.Image.Image]							,   _UpperCamelCase :				Union[torch.Tensor, PIL.Image.Image]							,   _UpperCamelCase :				int = 2_5_0							,   _UpperCamelCase :				float = 0.0							,   _UpperCamelCase :				int = 1_0							,   _UpperCamelCase :				int = 1_0							,   _UpperCamelCase :				Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   _UpperCamelCase :				Optional[str] = "pil"							,   _UpperCamelCase :				bool = True							,   )      ->Union[ImagePipelineOutput, Tuple]:
									snake_case_   =	image

									snake_case_   =	_preprocess_image(_UpperCamelCase							)
									snake_case_   =	original_image.to(device=self.device							,   dtype=self.unet.dtype							)
									snake_case_   =	_preprocess_mask(_UpperCamelCase							)
									snake_case_   =	mask_image.to(device=self.device							,   dtype=self.unet.dtype							)

									snake_case_   =	original_image.shape[0]

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

									snake_case_   =	original_image.shape
									snake_case_   =	randn_tensor(_UpperCamelCase							,   generator=_UpperCamelCase							,   device=self.device							,   dtype=self.unet.dtype							)

									# set step values
									self.scheduler.set_timesteps(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							,   self.device							)
									snake_case_   =	eta

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_UpperCamelCase							)


# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)