





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


__UpperCAmelCase	:       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"

__UpperCAmelCase	:       Union[str, 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"

__UpperCAmelCase	:       Any    	=							"\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      ):





							'''simple docstring'''







							def 							UpperCAmelCase__			(  self			:   Tuple		):
									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 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Optional[Any]							,    A			:   Tuple							,    A			:   List[Any]=None							,    A			:   Optional[int]=True							,    A			:   Optional[int]=False		):
									if rouge_types is None:
											__snake_case:						List[Any]			  =						['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

									__snake_case:						List[Any]			  =						rouge_scorer.RougeScorer(rouge_types=_UpperCAmelCase							,    use_stemmer=_UpperCAmelCase		)
									if use_aggregator:
											__snake_case:						Optional[int]			  =						scoring.BootstrapAggregator()
									else:
											__snake_case:						List[str]			  =						[]

									for ref, pred in zip(_UpperCAmelCase							,    _UpperCAmelCase		):
											__snake_case:						List[str]			  =						scorer.score(_UpperCAmelCase							,    _UpperCAmelCase		)
											if use_aggregator:
													aggregator.add_scores(_UpperCAmelCase		)
											else:
													scores.append(_UpperCAmelCase		)

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

									return result













import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






