


import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MaskFormerSwinBackbone
    from transformers.models.maskformer import MaskFormerSwinModel

class 							lowercase  :







     def __init__(						self :						Optional[int]   ,   _lowercase :						str   ,   _lowercase :						Tuple=13   ,   _lowercase :						Union[str, Any]=32   ,   _lowercase :						str=2   ,   _lowercase :						Dict=3   ,   _lowercase :						str=16   ,   _lowercase :						str=[1, 2, 1]   ,   _lowercase :						str=[2, 2, 4]   ,   _lowercase :						str=2   ,   _lowercase :						List[str]=2.0   ,   _lowercase :						Optional[int]=True   ,   _lowercase :						Union[str, Any]=0.0   ,   _lowercase :						List[str]=0.0   ,   _lowercase :						Optional[int]=0.1   ,   _lowercase :						Optional[int]="gelu"   ,   _lowercase :						Union[str, Any]=False   ,   _lowercase :						Tuple=True   ,   _lowercase :						Any=0.02   ,   _lowercase :						Optional[Any]=1E-5   ,   _lowercase :						Optional[int]=True   ,   _lowercase :						str=None   ,   _lowercase :						Optional[Any]=True   ,   _lowercase :						List[str]=10   ,   _lowercase :						Optional[Any]=8   ,   _lowercase :						Any=["stage1", "stage2", "stage3"]   ,   _lowercase :						str=[1, 2, 3]   ,   ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		parent
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		batch_size
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		image_size
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		patch_size
           SCREAMING_SNAKE_CASE__       :  int						     =		num_channels
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		embed_dim
           SCREAMING_SNAKE_CASE__       :  Any						     =		depths
           SCREAMING_SNAKE_CASE__       :  int						     =		num_heads
           SCREAMING_SNAKE_CASE__       :  str						     =		window_size
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		mlp_ratio
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		qkv_bias
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		hidden_dropout_prob
           SCREAMING_SNAKE_CASE__       :  int						     =		attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		drop_path_rate
           SCREAMING_SNAKE_CASE__       :  int						     =		hidden_act
           SCREAMING_SNAKE_CASE__       :  int						     =		use_absolute_embeddings
           SCREAMING_SNAKE_CASE__       :  int						     =		patch_norm
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		layer_norm_eps
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		initializer_range
           SCREAMING_SNAKE_CASE__       :  str						     =		is_training
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		scope
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		use_labels
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		type_sequence_label_size
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		encoder_stride
           SCREAMING_SNAKE_CASE__       :  Any						     =		out_features
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		out_indices







     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		ids_tensor([self.batch_size]   ,   self.type_sequence_label_size      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_config()

           return config, pixel_values, labels







     def 							lowercase__       (						self :						Union[str, Any]      ):
           return MaskFormerSwinConfig(
               image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   embed_dim=self.embed_dim   ,   depths=self.depths   ,   num_heads=self.num_heads   ,   window_size=self.window_size   ,   mlp_ratio=self.mlp_ratio   ,   qkv_bias=self.qkv_bias   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   drop_path_rate=self.drop_path_rate   ,   hidden_act=self.hidden_act   ,   use_absolute_embeddings=self.use_absolute_embeddings   ,   path_norm=self.patch_norm   ,   layer_norm_eps=self.layer_norm_eps   ,   initializer_range=self.initializer_range   ,   encoder_stride=self.encoder_stride   ,   out_features=self.out_features   ,   out_indices=self.out_indices   ,   )







     def 							lowercase__       (						self :						Any   ,   _lowercase :						Optional[Any]   ,   _lowercase :						Dict   ,   _lowercase :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		MaskFormerSwinModel(config=_lowercase      )
           model.to(_lowercase      )
           model.eval()
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		model(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
           SCREAMING_SNAKE_CASE__       :  Any						     =		int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

           self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, expected_seq_len, expected_dim)      )







     def 							lowercase__       (						self :						Tuple   ,   _lowercase :						int   ,   _lowercase :						Any   ,   _lowercase :						int      ):
           SCREAMING_SNAKE_CASE__       :  Any						     =		MaskFormerSwinBackbone(config=_lowercase      )
           model.to(_lowercase      )
           model.eval()
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		model(_lowercase      )

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps      )   ,   len(config.out_features      )      )
           self.parent.assertListEqual(list(result.feature_maps[0].shape      )   ,   [13, 16, 16, 16]      )

           # verify channels
           self.parent.assertEqual(len(model.channels      )   ,   len(config.out_features      )      )
           self.parent.assertListEqual(model.channels   ,   [16, 32, 64]      )

           # verify ValueError
           with self.parent.assertRaises(_lowercase      ):
                 SCREAMING_SNAKE_CASE__       :  str						     =		['''stem''']
                 SCREAMING_SNAKE_CASE__       :  str						     =		MaskFormerSwinBackbone(config=_lowercase      )







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  int						     =		config_and_inputs
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		{'''pixel_values''': pixel_values}
           return config, inputs_dict




@require_torch
class 							lowercase  (						_UpperCAmelCase    ,       _UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			List[str]      = (
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase			:			str      = {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
     lowerCamelCase			:			str      = False
     lowerCamelCase			:			List[Any]      = False
     lowerCamelCase			:			Any      = False
     lowerCamelCase			:			str      = False
     lowerCamelCase			:			List[str]      = False







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		MaskFormerSwinModelTester(self      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		ConfigTester(self   ,   config_class=_lowercase   ,   embed_dim=37      )







     @require_torch_multi_gpu
     @unittest.skip(
         reason=(
             '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
             ''' `nn.DataParallel`'''
         )      )
     def 							lowercase__       (						self :						str      ):
           pass







     def 							lowercase__       (						self :						List[str]      ):
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()







     def 							lowercase__       (						self :						Optional[Any]      ):
           return







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*_lowercase      )







     @unittest.skip('''Swin does not use inputs_embeds'''      )
     def 							lowercase__       (						self :						Dict      ):
           pass







     @unittest.skip('''Swin does not support feedforward chunking'''      )
     def 							lowercase__       (						self :						Tuple      ):
           pass







     def 							lowercase__       (						self :						List[str]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		model_class(_lowercase      )
                 self.assertIsInstance(model.get_input_embeddings()   ,   (nn.Module)      )
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_lowercase   ,   nn.Linear      )      )







     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  str						     =		model_class(_lowercase      )
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		inspect.signature(model.forward      )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 SCREAMING_SNAKE_CASE__       :  Any						     =		[*signature.parameters.keys()]

                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		['''pixel_values''']
                 self.assertListEqual(arg_names[:1]   ,   _lowercase      )







     @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''      )
     def 							lowercase__       (						self :						Union[str, Any]      ):
           pass







     @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''      )
     def 							lowercase__       (						self :						List[str]      ):
           pass







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						int   ,   _lowercase :						Optional[Any]   ,   _lowercase :						List[Any]   ,   _lowercase :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model_class(_lowercase      )
           model.to(_lowercase      )
           model.eval()

           with torch.no_grad():
                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model(**self._prepare_for_class(_lowercase   ,   _lowercase      )      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		outputs.hidden_states

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		getattr(
               self.model_tester   ,   '''expected_num_hidden_layers'''   ,   len(self.model_tester.depths      ) + 1      )
           self.assertEqual(len(_lowercase      )   ,   _lowercase      )

           # Swin has a different seq_length
           SCREAMING_SNAKE_CASE__       :  Any						     =		(
               config.patch_size
               if isinstance(config.patch_size   ,   collections.abc.Iterable      )
               else (config.patch_size, config.patch_size)
           )

           SCREAMING_SNAKE_CASE__       :  int						     =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]      )   ,   [num_patches, self.model_tester.embed_dim]   ,   )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE__       :  str						     =		(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size   ,   collections.abc.Iterable      )
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  int						     =		True
                 self.check_hidden_states_output(_lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		True

                 self.check_hidden_states_output(_lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      )







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Any						     =		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE__       :  Dict						     =		3

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size   ,   collections.abc.Iterable      )
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		(
               config.patch_size
               if isinstance(config.patch_size   ,   collections.abc.Iterable      )
               else (config.patch_size, config.patch_size)
           )

           SCREAMING_SNAKE_CASE__       :  int						     =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           SCREAMING_SNAKE_CASE__       :  Any						     =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		True
                 self.check_hidden_states_output(_lowercase   ,   _lowercase   ,   _lowercase   ,   (padded_height, padded_width)      )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		True
                 self.check_hidden_states_output(_lowercase   ,   _lowercase   ,   _lowercase   ,   (padded_height, padded_width)      )







     @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''      )
     def 							lowercase__       (						self :						Any      ):
           pass







     @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
     def 							lowercase__       (						self :						List[Any]      ):
           pass







     @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
     def 							lowercase__       (						self :						Optional[Any]      ):
           pass







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.model_tester.prepare_config_and_inputs_for_common()

           def set_nan_tensor_to_zero(_lowercase :						Tuple      ):
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		0
                 return t

           def check_equivalence(_lowercase :						int   ,   _lowercase :						int   ,   _lowercase :						Dict   ,   _lowercase :						Optional[int]={}      ):
                 with torch.no_grad():
                       SCREAMING_SNAKE_CASE__       :  List[str]						     =		model(**_lowercase   ,   return_dict=_lowercase   ,   **_lowercase      )
                       SCREAMING_SNAKE_CASE__       :  List[str]						     =		model(**_lowercase   ,   return_dict=_lowercase   ,   **_lowercase      ).to_tuple()

                       def recursive_check(_lowercase :						List[Any]   ,   _lowercase :						int      ):
                             if isinstance(_lowercase   ,   (List, Tuple)      ):
                                   for tuple_iterable_value, dict_iterable_value in zip(_lowercase   ,   _lowercase      ):
                                         recursive_check(_lowercase   ,   _lowercase      )
                             elif isinstance(_lowercase   ,   _lowercase      ):
                                   for tuple_iterable_value, dict_iterable_value in zip(
                                       tuple_object.values()   ,   dict_object.values()      ):
                                         recursive_check(_lowercase   ,   _lowercase      )
                             elif tuple_object is None:
                                   return
                             else:
                                   self.assertTrue(
                                       torch.allclose(
                                           set_nan_tensor_to_zero(_lowercase      )   ,   set_nan_tensor_to_zero(_lowercase      )   ,   atol=1E-5      )   ,   msg=(
                                           '''Tuple and dict output are not equal. Difference:'''
                                           f""" {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:"""
                                           f""" {torch.isnan(_lowercase      ).any()} and `inf`: {torch.isinf(_lowercase      )}. Dict has"""
                                           f""" `nan`: {torch.isnan(_lowercase      ).any()} and `inf`: {torch.isinf(_lowercase      )}."""
                                       )   ,   )

                       recursive_check(_lowercase   ,   _lowercase      )

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  int						     =		model_class(_lowercase      )
                 model.to(_lowercase      )
                 model.eval()

                 SCREAMING_SNAKE_CASE__       :  int						     =		self._prepare_for_class(_lowercase   ,   _lowercase      )
                 SCREAMING_SNAKE_CASE__       :  int						     =		self._prepare_for_class(_lowercase   ,   _lowercase      )
                 check_equivalence(_lowercase   ,   _lowercase   ,   _lowercase      )

                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self._prepare_for_class(_lowercase   ,   _lowercase   ,   return_labels=_lowercase      )
                 SCREAMING_SNAKE_CASE__       :  str						     =		self._prepare_for_class(_lowercase   ,   _lowercase   ,   return_labels=_lowercase      )
                 check_equivalence(_lowercase   ,   _lowercase   ,   _lowercase      )

                 SCREAMING_SNAKE_CASE__       :  Dict						     =		self._prepare_for_class(_lowercase   ,   _lowercase      )
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		self._prepare_for_class(_lowercase   ,   _lowercase      )
                 check_equivalence(_lowercase   ,   _lowercase   ,   _lowercase   ,   {'''output_hidden_states''': True}      )

                 SCREAMING_SNAKE_CASE__       :  Dict						     =		self._prepare_for_class(_lowercase   ,   _lowercase   ,   return_labels=_lowercase      )
                 SCREAMING_SNAKE_CASE__       :  str						     =		self._prepare_for_class(_lowercase   ,   _lowercase   ,   return_labels=_lowercase      )
                 check_equivalence(_lowercase   ,   _lowercase   ,   _lowercase   ,   {'''output_hidden_states''': True}      )




@require_torch
class 							lowercase  (						unittest.TestCase    ,       _UpperCAmelCase			):
     lowerCamelCase			:			Union[str, Any]      = (MaskFormerSwinBackbone,) if is_torch_available() else ()
     lowerCamelCase			:			List[Any]      = MaskFormerSwinConfig







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		MaskFormerSwinModelTester(self      )







     def 							lowercase__       (						self :						str      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		inputs_dict['''pixel_values'''].shape[0]

           for backbone_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		backbone_class(_lowercase      )
                 backbone.to(_lowercase      )
                 backbone.eval()

                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		backbone(**_lowercase      )

                 # Test default outputs and verify feature maps
                 self.assertIsInstance(outputs.feature_maps   ,   _lowercase      )
                 self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
                 for feature_map, n_channels in zip(outputs.feature_maps   ,   backbone.channels      ):
                       self.assertTrue(feature_map.shape[:2]   ,   (batch_size, n_channels)      )
                 self.assertIsNone(outputs.hidden_states      )
                 self.assertIsNone(outputs.attentions      )

                 # Test output_hidden_states=True
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		backbone(**_lowercase   ,   output_hidden_states=_lowercase      )
                 self.assertIsNotNone(outputs.hidden_states      )
                 self.assertTrue(len(outputs.hidden_states      )   ,   len(backbone.stage_names      )      )
                 # We skip the stem layer
                 for hidden_states, n_channels in zip(outputs.hidden_states[1:]   ,   backbone.channels      ):
                       for hidden_state in hidden_states:
                             # Hidden states are in the format (batch_size, (height * width), n_channels)
                             SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Any						     =		hidden_state.shape
                             self.assertTrue((h_batch_size, h_n_channels)   ,   (batch_size, n_channels)      )

            # Test output_attentions=True
                 if self.has_attentions:
                       SCREAMING_SNAKE_CASE__       :  Dict						     =		backbone(**_lowercase   ,   output_attentions=_lowercase      )
                       self.assertIsNotNone(outputs.attentions      )








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


_lowercase											=    """\
@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\",
}
"""

_lowercase											=    """\
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
"""

_lowercase											=    """
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 			lowercase_		(      datasets.Metric       ):


       def 	_snake_case				(    self				)						->    Union[str, 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 	_snake_case				(    self						,       __A						,       __A						,       __A=None						,       __A=True						,       __A=False				)						->    List[str]:
              if rouge_types is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

              SCREAMING_SNAKE_CASE_							:				Tuple		=rouge_scorer.RougeScorer(rouge_types=__A						,       use_stemmer=__A				)
              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=scoring.BootstrapAggregator()
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[]

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

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

              return result
