


"""simple docstring"""







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


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

lowerCamelCase_       :       List[Any]      						=  """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

lowerCamelCase_       :       int      						=  """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    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))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






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






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     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						) ->					Union[str, Any]:
     if rouge_types is None:
         a					=['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

     a					=rouge_scorer.RougeScorer(rouge_types=__A			,   use_stemmer=__A						)
     if use_aggregator:
         a					=scoring.BootstrapAggregator()
     else:
         a					=[]

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

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

     return result



"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  IFInpaintingSuperResolutionPipeline
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}      )
 __lowerCAmelCase			 =  PipelineTesterMixin.required_optional_params - {"latents"}


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     return self._get_superresolution_dummy_components()


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0						) ->					Optional[int]:
     if str(__A						).startswith('''mps'''						):
         a					=torch.manual_seed(__A						)
     else:
         a					=torch.Generator(device=__A						).manual_seed(__A						)

     a					=floats_tensor((1, 3, 16, 16)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)

     a					={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_image,
         '''mask_image''': mask_image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs


 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()			,   reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,   )
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_save_load_optional_components()


 @unittest.skipIf(torch_device != '''cuda'''			,   reason='''float16 requires CUDA'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
     super().test_save_load_floataa(expected_max_diff=1E-1						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_save_load_local()





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2			,   )