




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


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





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

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

												lowerCAmelCase_      			=  [np.array(i.resize((w, h) ,  resample=PIL_INTERPOLATION['''lanczos''']  )  )[None, :] for i in image]
												lowerCAmelCase_      			=  np.concatenate(_snake_case ,  axis=0  )
												lowerCAmelCase_      			=  np.array(_snake_case  ).astype(np.floataa  ) / 2_5_5.0
												lowerCAmelCase_      			=  image.transpose(0 ,  3 ,  1 ,  2  )
												lowerCAmelCase_      			=  2.0 * image - 1.0
												lowerCAmelCase_      			=  torch.from_numpy(_snake_case  )
						elif isinstance(image[0] ,  torch.Tensor  ):
												lowerCAmelCase_      			=  torch.cat(_snake_case ,  dim=0  )
						return image





def __UpperCamelCase			(       _A  ):


						if isinstance(_snake_case ,  torch.Tensor  ):
												return mask
						elif isinstance(_snake_case ,  PIL.Image.Image  ):
												lowerCAmelCase_      			=  [mask]

						if isinstance(mask[0] ,  PIL.Image.Image  ):
												lowerCAmelCase_      			=  mask[0].size
												lowerCAmelCase_      			=  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
												lowerCAmelCase_      			=  [np.array(m.convert('''L'''  ).resize((w, h) ,  resample=PIL_INTERPOLATION['''nearest''']  )  )[None, :] for m in mask]
												lowerCAmelCase_      			=  np.concatenate(_snake_case ,  axis=0  )
												lowerCAmelCase_      			=  mask.astype(np.floataa  ) / 2_5_5.0
												lowerCAmelCase_      			=  0
												lowerCAmelCase_      			=  1
												lowerCAmelCase_      			=  torch.from_numpy(_snake_case  )
						elif isinstance(mask[0] ,  torch.Tensor  ):
												lowerCAmelCase_      			=  torch.cat(_snake_case ,  dim=0  )
						return mask






class  A		(  __UpperCAmelCase     ):
		__snake_case									=   42
		__snake_case									=   42



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								super().__init__()
								self.register_modules(unet=_a,	scheduler=_a   )







		@torch.no_grad()
		def __call__(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__ = 250,	UpperCamelCase__ = 0.0,	UpperCamelCase__ = 10,	UpperCamelCase__ = 10,	UpperCamelCase__ = None,	UpperCamelCase__ = "pil",	UpperCamelCase__ = True,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  image

								lowerCAmelCase_      			=  _preprocess_image(_a   )
								lowerCAmelCase_      			=  original_image.to(device=self.device,	dtype=self.unet.dtype   )
								lowerCAmelCase_      			=  _preprocess_mask(_a   )
								lowerCAmelCase_      			=  mask_image.to(device=self.device,	dtype=self.unet.dtype   )

								lowerCAmelCase_      			=  original_image.shape[0]

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

								lowerCAmelCase_      			=  original_image.shape
								lowerCAmelCase_      			=  randn_tensor(_a,	generator=_a,	device=self.device,	dtype=self.unet.dtype   )

								# set step values
								self.scheduler.set_timesteps(_a,	_a,	_a,	self.device   )
								lowerCAmelCase_      			=  eta

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

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

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

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=_a   )






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


snake_case :		List[Any]							      =  "\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

snake_case :		Any							      =  "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

snake_case :		str							      =  "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,						_KWARGS_DESCRIPTION				)
class       _snake_case					(     datasets.Metric				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return datasets.MetricInfo(
				    description=_DESCRIPTION						,   citation=_CITATION						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
				        {
				            "predictions": datasets.Value("string"						,   id="sequence"		),
				            "references": datasets.Value("string"						,   id="sequence"		),
				        }		)						,   codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]						,   reference_urls=[
				        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
				        "https://github.com/google-research/google-research/tree/master/rouge",
				    ]						,   )







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a=None						,   _a=True						,   _a=False		):
				if rouge_types is None:
					__magic_name__		:		str	   =     ["rouge1", "rouge2", "rougeL", "rougeLsum"]

				__magic_name__		:		List[str]	   =     rouge_scorer.RougeScorer(rouge_types=_a						,   use_stemmer=_a		)
				if use_aggregator:
					__magic_name__		:		Dict	   =     scoring.BootstrapAggregator()
				else:
					__magic_name__		:		str	   =     []

				for ref, pred in zip(_a						,   _a		):
					__magic_name__		:		Union[str, Any]	   =     scorer.score(_a						,   _a		)
					if use_aggregator:
						aggregator.add_scores(_a		)
					else:
						scores.append(_a		)

				if use_aggregator:
					__magic_name__		:		Any	   =     aggregator.aggregate()
				else:
					__magic_name__		:		List[Any]	   =     {}
					for key in scores[0]:
						__magic_name__		:		str	   =     [score[key] for score in scores]

				return result
