




'''simple docstring'''

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 						a				:


				def __init__(		self    :					str       ,				lowercase_    :					Any       ,				lowercase_    :					Dict=2       ,				lowercase_    :					Optional[Any]=True       ,				lowercase_    :					Optional[int]=False       ,				lowercase_    :					List[Any]=10       ,				lowercase_    :					Dict=3       ,				lowercase_    :					str=32 * 4       ,				lowercase_    :					Dict=32 * 6       ,				lowercase_    :					Union[str, Any]=4       ,				lowercase_    :					str=32       ,				):
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				is_training
									snake_case_									=				use_auxiliary_loss
									snake_case_									=				num_queries
									snake_case_									=				num_channels
									snake_case_									=				min_size
									snake_case_									=				max_size
									snake_case_									=				num_labels
									snake_case_									=				mask_feature_size


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
									    lowercase_					)

									snake_case_									=				torch.ones([self.batch_size, self.min_size, self.max_size]       ,				device=lowercase_					)

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

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


				def   A_ (		self    :					Any					):
									return MaskFormerConfig.from_backbone_and_decoder_configs(
									    backbone_config=SwinConfig(
									        depths=[1, 1, 1, 1]       ,				)       ,				decoder_config=DetrConfig(
									        decoder_ffn_dim=128       ,				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   A_ (		self    :					List[Any]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.prepare_config_and_inputs()
									snake_case_									=				{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
									return config, inputs_dict


				def   A_ (		self    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Tuple					):
									snake_case_									=				output.encoder_hidden_states
									snake_case_									=				output.pixel_decoder_hidden_states
									snake_case_									=				output.transformer_decoder_hidden_states

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


				def   A_ (		self    :					Any       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Tuple       ,				lowercase_    :					Any       ,				lowercase_    :					List[str]=False					):
									with torch.no_grad():
														snake_case_									=				MaskFormerModel(config=lowercase_					)
														model.to(lowercase_					)
														model.eval()

														snake_case_									=				model(pixel_values=lowercase_       ,				pixel_mask=lowercase_					)
														snake_case_									=				model(lowercase_       ,				output_hidden_states=lowercase_					)
									# 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(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Tuple       ,				lowercase_    :					Union[str, Any]					):
									snake_case_									=				MaskFormerForInstanceSegmentation(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()

									def comm_check_on_output(lowercase_    :					Union[str, Any]					):
														# 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():
														snake_case_									=				model(pixel_values=lowercase_       ,				pixel_mask=lowercase_					)
														snake_case_									=				model(lowercase_					)

														comm_check_on_output(lowercase_					)

														snake_case_									=				model(
														    pixel_values=lowercase_       ,				pixel_mask=lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					)

									comm_check_on_output(lowercase_					)

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







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
				snake_case_      =		(
				    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
				    if is_torch_available()
				    else {}
				)

				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				MaskFormerModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				has_text_modality=lowercase_					)


				def   A_ (		self    :					Any					):
									self.config_tester.run_common_tests()


				def   A_ (		self    :					Dict					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(lowercase_       ,				**lowercase_       ,				output_hidden_states=lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowercase_					)


				@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''					)
				def   A_ (		self    :					List[Any]					):
									pass


				@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''					)
				def   A_ (		self    :					str					):
									pass


				@unittest.skip(reason='''MaskFormer is not a generative model'''					)
				def   A_ (		self    :					Tuple					):
									pass


				@unittest.skip(reason='''MaskFormer does not use token embeddings'''					)
				def   A_ (		self    :					Optional[Any]					):
									pass


				@require_torch_multi_gpu
				@unittest.skip(
				    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''					)
				def   A_ (		self    :					List[str]					):
									pass


				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
				def   A_ (		self    :					Union[str, Any]					):
									pass


				def   A_ (		self    :					str					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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

														snake_case_									=				['''pixel_values''']
														self.assertListEqual(arg_names[:1]       ,				lowercase_					)


				@slow
				def   A_ (		self    :					List[str]					):
									for model_name in ["facebook/maskformer-swin-small-coco"]:
														snake_case_									=				MaskFormerModel.from_pretrained(lowercase_					)
														self.assertIsNotNone(lowercase_					)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				(self.model_tester.min_size,) * 2
									snake_case_									=				{
									    '''pixel_values''': torch.randn((2, 3, *size)       ,				device=lowercase_					),
									    '''mask_labels''': torch.randn((2, 10, *size)       ,				device=lowercase_					),
									    '''class_labels''': torch.zeros(2       ,				10       ,				device=lowercase_					).long(),
									}

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


				def   A_ (		self    :					List[str]					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(lowercase_       ,				**lowercase_       ,				output_hidden_states=lowercase_					)


				def   A_ (		self    :					str					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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


				def   A_ (		self    :					Dict					):
									if not self.model_tester.is_training:
														return
									# only MaskFormerForInstanceSegmentation has the loss
									snake_case_									=				self.all_model_classes[1]
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs()

									snake_case_									=				model_class(lowercase_					)
									model.to(lowercase_					)
									model.train()

									snake_case_									=				model(lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					).loss
									loss.backward()


				def   A_ (		self    :					List[str]					):
									# only MaskFormerForInstanceSegmentation has the loss
									snake_case_									=				self.all_model_classes[1]
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs()
									snake_case_									=				True
									snake_case_									=				True

									snake_case_									=				model_class(lowercase_					)
									model.to(lowercase_					)
									model.train()

									snake_case_									=				model(lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					)

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

									snake_case_									=				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
									snake_case_									=				outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

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

									outputs.loss.backward(retain_graph=lowercase_					)

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







a  :				List[Any]									=  1E-4


def 		__magic_name__    (	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image






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


				@cached_property
				def   A_ (		self    :					Tuple					):
									return (
									    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
									    if is_vision_available()
									    else None
									)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''					).to(lowercase_					)
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				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(lowercase_       ,				(1, 3, 800, 1088)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									snake_case_									=				torch.tensor(
									    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)

									snake_case_									=				torch.tensor(
									    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)

									snake_case_									=				torch.tensor(
									    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					List[str]					):
									snake_case_									=				(
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
									    .to(lowercase_					)
									    .eval()
									)
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				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(lowercase_       ,				(1, 3, 800, 1088)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)
									# masks_queries_logits
									snake_case_									=				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)       ,				)
									snake_case_									=				[
									    [-1.373_7124, -1.772_4937, -1.936_4233],
									    [-1.597_7281, -1.986_7939, -2.152_3695],
									    [-1.579_5398, -1.926_9832, -2.09_3942],
									]
									snake_case_									=				torch.tensor(lowercase_					).to(lowercase_					)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)
									# class_queries_logits
									snake_case_									=				outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape       ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
									snake_case_									=				torch.tensor(
									    [
									        [1.6_512e00, -5.2_572e00, -3.3_519e00],
									        [3.6_169e-02, -5.9_025e00, -2.9_313e00],
									        [1.0_766e-04, -7.7_630e00, -5.1_263e00],
									    ]					).to(lowercase_					)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					Any					):
									snake_case_									=				(
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''					)
									    .to(lowercase_					)
									    .eval()
									)
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				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(lowercase_       ,				(1, 3, 800, 1088)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)
									# masks_queries_logits
									snake_case_									=				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)       ,				)
									snake_case_									=				[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
									snake_case_									=				torch.tensor(lowercase_					).to(lowercase_					)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)
									# class_queries_logits
									snake_case_									=				outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape       ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
									snake_case_									=				torch.tensor(
									    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]					).to(lowercase_					)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				(
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
									    .to(lowercase_					)
									    .eval()
									)
									snake_case_									=				self.default_image_processor

									snake_case_									=				image_processor(
									    [np.zeros((3, 800, 1333)					), np.zeros((3, 800, 1333)					)]       ,				segmentation_maps=[np.zeros((384, 384)					).astype(np.floataa					), np.zeros((384, 384)					).astype(np.floataa					)]       ,				return_tensors='''pt'''       ,				)

									snake_case_									=				inputs['''pixel_values'''].to(lowercase_					)
									snake_case_									=				[el.to(lowercase_					) for el in inputs['''mask_labels''']]
									snake_case_									=				[el.to(lowercase_					) for el in inputs['''class_labels''']]

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									self.assertTrue(outputs.loss is not None					)





from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A      :    Optional[int]		=					[
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

A      :    List[Any]		=					[
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2", "rougeL"]						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2"]						)
      assert (
          pd.DataFrame(no_aggregation["rouge2"]						).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["rouge2"]						).fmeasure.mean()
      )

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  "rougeLsum"
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      lowerCamelCase__		:				str    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      assert score > score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				int    =  ["rouge1", "rouge2", "rougeL"]
      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      lowerCamelCase__		:				Any    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      assert score_sep == score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  [
          "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
          "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
      ]
      lowerCamelCase__		:				Tuple    =  [
          "Margot Frank, died in 1945, a month earlier than previously thought.",
          "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
          " the final seconds on board Flight 9525.",
      ]
      assert calculate_rouge(_A    ,					_A    ,					newline_sep=_A						) == calculate_rouge(_A    ,					_A    ,					newline_sep=_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  [
          "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
      ]
      lowerCamelCase__		:				str    =  [
          " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
      ]

      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]    ,					newline_sep=_A						)["rougeLsum"]
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]						)["rougeLsum"]
      assert new_score > prev_score


def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  Path("examples/seq2seq/test_data/wmt_en_ro"						)
      lowerCamelCase__		:				Any    =  calculate_rouge_path(data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				str    =  calculate_rouge_path(
          data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)    ,					bootstrap_aggregation=_A						)
      assert isinstance(_A    ,					_A						)


