'''simple docstring'''







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase_      	=	{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCamelCase_      	=	logging.get_logger(__name__)




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							str     		= 'mask2former'
						A_			:							Any     		= ['swin']
						A_			:							int     		= {'hidden_size': 'hidden_dim'}




						def __init__(    self :       List[Any]							,							UpperCamelCase_ :       Optional[Dict] = None							,							UpperCamelCase_ :       int = 2_56							,							UpperCamelCase_ :       int = 2_56							,							UpperCamelCase_ :       int = 2_56							,							UpperCamelCase_ :       int = 10_24							,							UpperCamelCase_ :       str = "relu"							,							UpperCamelCase_ :       int = 6							,							UpperCamelCase_ :       int = 10							,							UpperCamelCase_ :       int = 8							,							UpperCamelCase_ :       float = 0.0							,							UpperCamelCase_ :       int = 20_48							,							UpperCamelCase_ :       bool = False							,							UpperCamelCase_ :       bool = False							,							UpperCamelCase_ :       int = 4							,							UpperCamelCase_ :       int = 2_55							,							UpperCamelCase_ :       int = 1_00							,							UpperCamelCase_ :       float = 0.1							,							UpperCamelCase_ :       float = 2.0							,							UpperCamelCase_ :       float = 5.0							,							UpperCamelCase_ :       float = 5.0							,							UpperCamelCase_ :       int = 1_25_44							,							UpperCamelCase_ :       float = 3.0							,							UpperCamelCase_ :       float = 0.75							,							UpperCamelCase_ :       float = 0.02							,							UpperCamelCase_ :       float = 1.0							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       List[int] = [4, 8, 16, 32]							,							UpperCamelCase_ :       bool = None							,							**UpperCamelCase_ :       List[str]							,							)     ->				int:
													if backbone_config is None:
																				logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'      )
																				SCREAMING_SNAKE_CASE__     :Dict						    = CONFIG_MAPPING['swin'](
																				    image_size=2_24							,							in_channels=3							,							patch_size=4							,							embed_dim=96							,							depths=[2, 2, 18, 2]							,							num_heads=[3, 6, 12, 24]							,							window_size=7							,							drop_path_rate=0.3							,							use_absolute_embeddings=UpperCamelCase_							,							out_features=['stage1', 'stage2', 'stage3', 'stage4']							,							)

													if isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = backbone_config.pop('model_type'      )
																				SCREAMING_SNAKE_CASE__     :int						    = CONFIG_MAPPING[backbone_model_type]
																				SCREAMING_SNAKE_CASE__     :List[str]						    = config_class.from_dict(UpperCamelCase_      )

													# verify that the backbone is supported
													if backbone_config.model_type not in self.backbones_supported:
																				logger.warning_once(
																				    f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
																				    f'''Supported model types: {",".join(self.backbones_supported      )}'''      )

													SCREAMING_SNAKE_CASE__     :Any						    = backbone_config
													SCREAMING_SNAKE_CASE__     :List[Any]						    = feature_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = mask_feature_size
													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_dim
													SCREAMING_SNAKE_CASE__     :List[Any]						    = encoder_feedforward_dim
													SCREAMING_SNAKE_CASE__     :Dict						    = activation_function
													SCREAMING_SNAKE_CASE__     :str						    = encoder_layers
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = decoder_layers
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :List[str]						    = dropout
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = dim_feedforward
													SCREAMING_SNAKE_CASE__     :Tuple						    = pre_norm
													SCREAMING_SNAKE_CASE__     :Any						    = enforce_input_projection
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = common_stride
													SCREAMING_SNAKE_CASE__     :str						    = ignore_value
													SCREAMING_SNAKE_CASE__     :str						    = num_queries
													SCREAMING_SNAKE_CASE__     :List[Any]						    = no_object_weight
													SCREAMING_SNAKE_CASE__     :Any						    = class_weight
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = mask_weight
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = dice_weight
													SCREAMING_SNAKE_CASE__     :str						    = train_num_points
													SCREAMING_SNAKE_CASE__     :Any						    = oversample_ratio
													SCREAMING_SNAKE_CASE__     :int						    = importance_sample_ratio
													SCREAMING_SNAKE_CASE__     :str						    = init_std
													SCREAMING_SNAKE_CASE__     :Tuple						    = init_xavier_std
													SCREAMING_SNAKE_CASE__     :Dict						    = use_auxiliary_loss
													SCREAMING_SNAKE_CASE__     :Dict						    = feature_strides
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = output_auxiliary_logits
													SCREAMING_SNAKE_CASE__     :List[str]						    = decoder_layers

													super().__init__(**UpperCamelCase_      )




						@classmethod
						def      __lowerCamelCase       (    cls :       Any							,							UpperCamelCase_ :       PretrainedConfig							,							**UpperCamelCase_ :       Optional[Any]      )     ->				Tuple:
													return cls(
													    backbone_config=UpperCamelCase_							,							**UpperCamelCase_							,							)




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Dict[str, any]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = copy.deepcopy(self.__dict__      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.backbone_config.to_dict()
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.__class__.model_type
													return output







'''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


UpperCamelCase_      	=	'''\
@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",
}
'''

UpperCamelCase_      	=	'''\
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
'''

UpperCamelCase_      	=	'''
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 _SCREAMING_SNAKE_CASE(							datasets.Metric					):




						def      __lowerCamelCase       (    self :       Any      )     ->				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      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Tuple=None							,							UpperCamelCase_ :       Tuple=True							,							UpperCamelCase_ :       str=False      )     ->				Optional[Any]:
													if rouge_types is None:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

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

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

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

													return result






