

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

	if is_vision_available():
		from transformers import MaskFormerImageProcessor

if is_vision_available():
	from PIL import Image


class 			lowerCamelCase_    :


							def __init__( self      ,				lowerCamelCase_      ,				lowerCamelCase_=2      ,				lowerCamelCase_=True      ,				lowerCamelCase_=False      ,				lowerCamelCase_=10      ,				lowerCamelCase_=3      ,				lowerCamelCase_=32 * 4      ,				lowerCamelCase_=32 * 6      ,				lowerCamelCase_=4      ,				lowerCamelCase_=32      ,				)							->   str:



								"""simple docstring"""

								_UpperCamelCase			=   parent
								_UpperCamelCase			=   batch_size
								_UpperCamelCase			=   is_training
								_UpperCamelCase			=   use_auxiliary_loss
								_UpperCamelCase			=   num_queries
								_UpperCamelCase			=   num_channels
								_UpperCamelCase			=   min_size
								_UpperCamelCase			=   max_size
								_UpperCamelCase			=   num_labels
								_UpperCamelCase			=   mask_feature_size


							def  lowercase   ( self						)							->   str:



								"""simple docstring"""

								_UpperCamelCase			=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
								    __lowerCamelCase						)

								_UpperCamelCase			=   torch.ones([self.batch_size, self.min_size, self.max_size]      ,				device=__lowerCamelCase						)

								_UpperCamelCase			=   (
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,				device=__lowerCamelCase						) > 0.5
								).float()
								_UpperCamelCase			=   (torch.rand((self.batch_size, self.num_labels)      ,				device=__lowerCamelCase						) > 0.5).long()

								_UpperCamelCase			=   self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels


							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								return MaskFormerConfig.from_backbone_and_decoder_configs(
								    backbone_config=SwinConfig(
								        depths=[1, 1, 1, 1]      ,				)      ,				decoder_config=DetrConfig(
								        decoder_ffn_dim=1_28      ,				num_queries=self.num_queries      ,				decoder_attention_heads=2      ,				d_model=self.mask_feature_size      ,				)      ,				mask_feature_size=self.mask_feature_size      ,				fpn_feature_size=self.mask_feature_size      ,				num_channels=self.num_channels      ,				num_labels=self.num_labels      ,				)


							def  lowercase   ( self						)							->   Union[str, Any]:



								"""simple docstring"""

								_UpperCamelCase			=   self.prepare_config_and_inputs()
								_UpperCamelCase			=   {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
								return config, inputs_dict


							def  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_						)							->   Dict:



								"""simple docstring"""

								_UpperCamelCase			=   output.encoder_hidden_states
								_UpperCamelCase			=   output.pixel_decoder_hidden_states
								_UpperCamelCase			=   output.transformer_decoder_hidden_states

								self.parent.assertTrue(len(__lowerCamelCase						)      ,				len(config.backbone_config.depths						)						)
								self.parent.assertTrue(len(__lowerCamelCase						)      ,				len(config.backbone_config.depths						)						)
								self.parent.assertTrue(len(__lowerCamelCase						)      ,				config.decoder_config.decoder_layers						)


							def  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_=False						)							->   Tuple:



								"""simple docstring"""

								with torch.no_grad():
									_UpperCamelCase			=   MaskFormerModel(config=__lowerCamelCase						)
									model.to(__lowerCamelCase						)
									model.eval()

									_UpperCamelCase			=   model(pixel_values=__lowerCamelCase      ,				pixel_mask=__lowerCamelCase						)
									_UpperCamelCase			=   model(__lowerCamelCase      ,				output_hidden_states=__lowerCamelCase						)
								# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
								# encoder and pixel decoder
								self.parent.assertEqual(
								    output.transformer_decoder_last_hidden_state.shape      ,				(self.batch_size, self.num_queries, self.mask_feature_size)      ,				)
								# let's ensure the other two hidden state exists
								self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
								self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

								if output_hidden_states:
									self.check_output_hidden_state(__lowerCamelCase      ,				__lowerCamelCase						)







							def  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_						)							->   Optional[int]:



								"""simple docstring"""

								_UpperCamelCase			=   MaskFormerForInstanceSegmentation(config=__lowerCamelCase						)
								model.to(__lowerCamelCase						)
								model.eval()

								def comm_check_on_output(lowerCamelCase_						):
									# let's still check that all the required stuff is there
									self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
									self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
									self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
									# okay, now we need to check the logits shape
									# due to the encoder compression, masks have a //4 spatial size
									self.parent.assertEqual(
									    result.masks_queries_logits.shape      ,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)      ,				)
									# + 1 for null class
									self.parent.assertEqual(
									    result.class_queries_logits.shape      ,				(self.batch_size, self.num_queries, self.num_labels + 1)						)

								with torch.no_grad():
									_UpperCamelCase			=   model(pixel_values=__lowerCamelCase      ,				pixel_mask=__lowerCamelCase						)
									_UpperCamelCase			=   model(__lowerCamelCase						)

									comm_check_on_output(__lowerCamelCase						)

									_UpperCamelCase			=   model(
									    pixel_values=__lowerCamelCase      ,				pixel_mask=__lowerCamelCase      ,				mask_labels=__lowerCamelCase      ,				class_labels=__lowerCamelCase						)

								comm_check_on_output(__lowerCamelCase						)

								self.parent.assertTrue(result.loss is not None						)
								self.parent.assertEqual(result.loss.shape      ,				torch.Size([1]						)						)






@require_torch
class 			lowerCamelCase_    (  SCREAMING_SNAKE_CASE__	,     SCREAMING_SNAKE_CASE__	,     unittest.TestCase		):
							__lowercase				:	Dict        =				(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							__lowercase				:	str        =				(
							    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							__lowercase				:	int        =				False
							__lowercase				:	Any        =				False
							__lowercase				:	Tuple        =				False
							__lowercase				:	List[Any]        =				False


							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								_UpperCamelCase			=   MaskFormerModelTester(self						)
								_UpperCamelCase			=   ConfigTester(self      ,				config_class=__lowerCamelCase      ,				has_text_modality=__lowerCamelCase						)


							def  lowercase   ( self						)							->   List[Any]:



								"""simple docstring"""

								self.config_tester.run_common_tests()


							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								_UpperCamelCase			=   self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskformer_model(__lowerCamelCase      ,				**__lowerCamelCase      ,				output_hidden_states=__lowerCamelCase						)


							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								_UpperCamelCase			=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCamelCase						)


							@unittest.skip(reason="MaskFormer does not use inputs_embeds"						)
							def  lowercase   ( self						)							->   Optional[Any]:



								"""simple docstring"""

								pass


							@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"						)
							def  lowercase   ( self						)							->   int:



								"""simple docstring"""

								pass


							@unittest.skip(reason="MaskFormer is not a generative model"						)
							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								pass


							@unittest.skip(reason="MaskFormer does not use token embeddings"						)
							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								pass


							@require_torch_multi_gpu
							@unittest.skip(
							    reason="MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`"						)
							def  lowercase   ( self						)							->   Any:



								"""simple docstring"""

								pass


							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
							def  lowercase   ( self						)							->   Optional[Any]:



								"""simple docstring"""

								pass


							def  lowercase   ( self						)							->   List[Any]:



								"""simple docstring"""

								_UpperCamelCase			=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									_UpperCamelCase			=   model_class(__lowerCamelCase						)
									_UpperCamelCase			=   inspect.signature(model.forward						)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_UpperCamelCase			=   [*signature.parameters.keys()]

									_UpperCamelCase			=   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]      ,				__lowerCamelCase						)


							@slow
							def  lowercase   ( self						)							->   Optional[Any]:



								"""simple docstring"""

								for model_name in ["facebook/maskformer-swin-small-coco"]:
									_UpperCamelCase			=   MaskFormerModel.from_pretrained(__lowerCamelCase						)
									self.assertIsNotNone(__lowerCamelCase						)


							def  lowercase   ( self						)							->   str:



								"""simple docstring"""

								_UpperCamelCase			=   (self.model_tester.min_size,) * 2
								_UpperCamelCase			=   {
								    '''pixel_values''': torch.randn((2, 3, *size)      ,				device=__lowerCamelCase						),
								    '''mask_labels''': torch.randn((2, 10, *size)      ,				device=__lowerCamelCase						),
								    '''class_labels''': torch.zeros(2      ,				10      ,				device=__lowerCamelCase						).long(),
								}

								_UpperCamelCase			=   MaskFormerForInstanceSegmentation(MaskFormerConfig()						).to(__lowerCamelCase						)
								_UpperCamelCase			=   model(**__lowerCamelCase						)
								self.assertTrue(outputs.loss is not None						)


							def  lowercase   ( self						)							->   str:



								"""simple docstring"""

								_UpperCamelCase			=   self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskformer_model(__lowerCamelCase      ,				**__lowerCamelCase      ,				output_hidden_states=__lowerCamelCase						)


							def  lowercase   ( self						)							->   int:



								"""simple docstring"""

								_UpperCamelCase			=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									_UpperCamelCase			=   model_class(__lowerCamelCase						).to(__lowerCamelCase						)
									_UpperCamelCase			=   model(**__lowerCamelCase      ,				output_attentions=__lowerCamelCase						)
									self.assertTrue(outputs.attentions is not None						)


							def  lowercase   ( self						)							->   Dict:



								"""simple docstring"""

								if not self.model_tester.is_training:
									return
								# only MaskFormerForInstanceSegmentation has the loss
								_UpperCamelCase			=   self.all_model_classes[1]
								_UpperCamelCase			=   self.model_tester.prepare_config_and_inputs()

								_UpperCamelCase			=   model_class(__lowerCamelCase						)
								model.to(__lowerCamelCase						)
								model.train()

								_UpperCamelCase			=   model(__lowerCamelCase      ,				mask_labels=__lowerCamelCase      ,				class_labels=__lowerCamelCase						).loss
								loss.backward()







							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								_UpperCamelCase			=   self.all_model_classes[1]
								_UpperCamelCase			=   self.model_tester.prepare_config_and_inputs()
								_UpperCamelCase			=   True
								_UpperCamelCase			=   True

								_UpperCamelCase			=   model_class(__lowerCamelCase						)
								model.to(__lowerCamelCase						)
								model.train()

								_UpperCamelCase			=   model(__lowerCamelCase      ,				mask_labels=__lowerCamelCase      ,				class_labels=__lowerCamelCase						)

								_UpperCamelCase			=   outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								_UpperCamelCase			=   outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()
								# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
								_UpperCamelCase			=   outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								_UpperCamelCase			=   outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=__lowerCamelCase						)

								self.assertIsNotNone(encoder_hidden_states.grad						)
								self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
								self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
								self.assertIsNotNone(attentions.grad						)






__lowerCAmelCase			     =       1E-4



def    _lowercase			(	)							->					Optional[int]:





	"""simple docstring"""


	_UpperCamelCase			=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
	return image




@require_vision
@slow
class 			lowerCamelCase_    (  unittest.TestCase		):


							@cached_property
							def  lowercase   ( self						)							->   int:



								"""simple docstring"""

								return (
								    MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"						)
								    if is_vision_available()
								    else None
								)


							def  lowercase   ( self						)							->   int:



								"""simple docstring"""

								_UpperCamelCase			=   MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"						).to(__lowerCamelCase						)
								_UpperCamelCase			=   self.default_image_processor
								_UpperCamelCase			=   prepare_img()
								_UpperCamelCase			=   image_processor(__lowerCamelCase      ,				return_tensors="pt"						).to(__lowerCamelCase						)
								_UpperCamelCase			=   inputs['''pixel_values'''].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
								# check size
								self.assertEqual(__lowerCamelCase      ,				(1, 3, 8_00, 10_88)						)

								with torch.no_grad():
									_UpperCamelCase			=   model(**__lowerCamelCase						)

								_UpperCamelCase			=   torch.tensor(
								    [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]						).to(__lowerCamelCase						)
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,				__lowerCamelCase      ,				atol=__lowerCamelCase						)						)

								_UpperCamelCase			=   torch.tensor(
								    [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]						).to(__lowerCamelCase						)
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,				__lowerCamelCase      ,				atol=__lowerCamelCase						)						)

								_UpperCamelCase			=   torch.tensor(
								    [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]						).to(__lowerCamelCase						)
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,				__lowerCamelCase      ,				atol=__lowerCamelCase						)						)


							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								_UpperCamelCase			=   (
								    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"						)
								    .to(__lowerCamelCase						)
								    .eval()
								)
								_UpperCamelCase			=   self.default_image_processor
								_UpperCamelCase			=   prepare_img()
								_UpperCamelCase			=   image_processor(__lowerCamelCase      ,				return_tensors="pt"						).to(__lowerCamelCase						)
								_UpperCamelCase			=   inputs['''pixel_values'''].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
								# check size
								self.assertEqual(__lowerCamelCase      ,				(1, 3, 8_00, 10_88)						)

								with torch.no_grad():
									_UpperCamelCase			=   model(**__lowerCamelCase						)
								# masks_queries_logits
								_UpperCamelCase			=   outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      ,				)
								_UpperCamelCase			=   [
								    [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
								    [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
								    [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
								]
								_UpperCamelCase			=   torch.tensor(__lowerCamelCase						).to(__lowerCamelCase						)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,				__lowerCamelCase      ,				atol=__lowerCamelCase						)						)
								# class_queries_logits
								_UpperCamelCase			=   outputs.class_queries_logits
								self.assertEqual(
								    class_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
								_UpperCamelCase			=   torch.tensor(
								    [
								        [1.65_12E00, -5.25_72E00, -3.35_19E00],
								        [3.61_69E-02, -5.90_25E00, -2.93_13E00],
								        [1.07_66E-04, -7.76_30E00, -5.12_63E00],
								    ]						).to(__lowerCamelCase						)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,				__lowerCamelCase      ,				atol=__lowerCamelCase						)						)


							def  lowercase   ( self						)							->   int:



								"""simple docstring"""

								_UpperCamelCase			=   (
								    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"						)
								    .to(__lowerCamelCase						)
								    .eval()
								)
								_UpperCamelCase			=   self.default_image_processor
								_UpperCamelCase			=   prepare_img()
								_UpperCamelCase			=   image_processor(__lowerCamelCase      ,				return_tensors="pt"						).to(__lowerCamelCase						)
								_UpperCamelCase			=   inputs['''pixel_values'''].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
								# check size
								self.assertEqual(__lowerCamelCase      ,				(1, 3, 8_00, 10_88)						)

								with torch.no_grad():
									_UpperCamelCase			=   model(**__lowerCamelCase						)
								# masks_queries_logits
								_UpperCamelCase			=   outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      ,				)
								_UpperCamelCase			=   [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
								_UpperCamelCase			=   torch.tensor(__lowerCamelCase						).to(__lowerCamelCase						)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,				__lowerCamelCase      ,				atol=__lowerCamelCase						)						)
								# class_queries_logits
								_UpperCamelCase			=   outputs.class_queries_logits
								self.assertEqual(
								    class_queries_logits.shape      ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
								_UpperCamelCase			=   torch.tensor(
								    [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]						).to(__lowerCamelCase						)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,				__lowerCamelCase      ,				atol=__lowerCamelCase						)						)







							def  lowercase   ( self						)							->   Dict:



								"""simple docstring"""

								_UpperCamelCase			=   (
								    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"						)
								    .to(__lowerCamelCase						)
								    .eval()
								)
								_UpperCamelCase			=   self.default_image_processor

								_UpperCamelCase			=   image_processor(
								    [np.zeros((3, 8_00, 13_33)						), np.zeros((3, 8_00, 13_33)						)]      ,				segmentation_maps=[np.zeros((3_84, 3_84)						).astype(np.floataa						), np.zeros((3_84, 3_84)						).astype(np.floataa						)]      ,				return_tensors="pt"      ,				)

								_UpperCamelCase			=   inputs['''pixel_values'''].to(__lowerCamelCase						)
								_UpperCamelCase			=   [el.to(__lowerCamelCase						) for el in inputs['''mask_labels''']]
								_UpperCamelCase			=   [el.to(__lowerCamelCase						) for el in inputs['''class_labels''']]

								with torch.no_grad():
									_UpperCamelCase			=   model(**__lowerCamelCase						)

								self.assertTrue(outputs.loss is not None						)











"""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


a_					=     """\
@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\",
}
"""

a_					=     """\
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
"""

a_					=     """
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 						__snake_case   (						datasets.Metric	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     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   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     if rouge_types is None:
         __A       :		Dict							=	['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

     __A       :		Dict							=	rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase   ,		use_stemmer=__lowerCamelCase    )
     if use_aggregator:
         __A       :		Dict							=	scoring.BootstrapAggregator()
     else:
         __A       :		Optional[Any]							=	[]

     for ref, pred in zip(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	scorer.score(__lowerCamelCase   ,		__lowerCamelCase    )
         if use_aggregator:
             aggregator.add_scores(__lowerCamelCase    )
         else:
             scores.append(__lowerCamelCase    )

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

     return result




