


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_      : int  					=       '\\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_      : 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_      : List[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  SCREAMING_SNAKE_CASE__  (				datasets.Metric			):
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			List[str]:
             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				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Any=True     ,							SCREAMING_SNAKE_CASE__				:       Tuple=False     )		->			Optional[Any]:
             if rouge_types is None:
                    a_					:		Dict							=  ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

             a_					:		Union[str, Any]							=  rouge_scorer.RougeScorer(rouge_types=SCREAMING_SNAKE_CASE__     ,							use_stemmer=SCREAMING_SNAKE_CASE__     )
             if use_aggregator:
                    a_					:		Tuple							=  scoring.BootstrapAggregator()
             else:
                    a_					:		Union[str, Any]							=  []

             for ref, pred in zip(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Dict							=  scorer.score(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                    if use_aggregator:
                           aggregator.add_scores(SCREAMING_SNAKE_CASE__     )
                    else:
                           scores.append(SCREAMING_SNAKE_CASE__     )

             if use_aggregator:
                    a_					:		Tuple							=  aggregator.aggregate()
             else:
                    a_					:		Optional[int]							=  {}
                    for key in scores[0]:
                           a_					:		Any							=  [score[key] for score in scores]

             return result






import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      # TODO: is there an appropriate internal test set?
      snake_case__				:     Any        =						'''ssube/stable-diffusion-x4-upscaler-onnx'''
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int=0     )		->			Tuple:
             a_					:		Union[str, Any]							=  floats_tensor((1, 3, 1_2_8, 1_2_8)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     )
             a_					:		Dict							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  {
                 'prompt': 'A painting of a squirrel eating a burger',
                 'image': image,
                 'generator': generator,
                 'num_inference_steps': 3,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  self.get_dummy_inputs()
             a_					:		int							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		Tuple							=  image[0, -3:, -3:, -1].flatten()

             # started as 128, should now be 512
             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		List[Any]							=  np.array(
                 [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
             assert np.abs(image_slice - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Dict:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  PNDMScheduler.from_config(pipe.scheduler.config     ,							skip_prk_steps=SCREAMING_SNAKE_CASE__     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		List[str]							=  self.get_dummy_inputs()
             a_					:		List[str]							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		Optional[Any]							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		str							=  np.array(
                 [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]     )
             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Dict:
             a_					:		Optional[Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[Any]							=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  self.get_dummy_inputs()
             a_					:		Dict							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		str							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Optional[Any]							=  np.array(
                 [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			int:
             a_					:		List[str]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[Any]							=  self.get_dummy_inputs()
             a_					:		Dict							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		List[str]							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		int							=  np.array(
                 [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1

      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[int]							=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  self.get_dummy_inputs()
             a_					:		List[str]							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		List[str]							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Union[str, Any]							=  np.array(
                 [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Union[str, Any]:
             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[str]:
             a_					:		List[str]							=  ort.SessionOptions()
             a_					:		int							=  False
             return options
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Tuple:
             a_					:		str							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		int							=  init_image.resize((1_2_8, 1_2_8)     )
             # using the PNDM scheduler by default
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  'A fantasy landscape, trending on artstation'

             a_					:		str							=  torch.manual_seed(0     )
             a_					:		List[str]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=1_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		Dict							=  output.images
             a_					:		Any							=  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		str							=  np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]     )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2E-2

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             a_					:		Dict							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		List[str]							=  init_image.resize((1_2_8, 1_2_8)     )
             a_					:		Dict							=  LMSDiscreteScheduler.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							subfolder='scheduler'     )
             a_					:		Union[str, Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							scheduler=SCREAMING_SNAKE_CASE__     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  'A fantasy landscape, trending on artstation'

             a_					:		Tuple							=  torch.manual_seed(0     )
             a_					:		Optional[Any]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=2_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		str							=  output.images
             a_					:		List[Any]							=  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Tuple							=  np.array(
                 [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]     )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2E-2


