'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_      	=	logging.get_logger(__name__)

UpperCamelCase_      	=	{
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Any     		= 'yolos'




						def __init__(    self :       Tuple							,							UpperCamelCase_ :       Union[str, Any]=7_68							,							UpperCamelCase_ :       Optional[int]=12							,							UpperCamelCase_ :       Optional[int]=12							,							UpperCamelCase_ :       Optional[int]=30_72							,							UpperCamelCase_ :       List[str]="gelu"							,							UpperCamelCase_ :       Tuple=0.0							,							UpperCamelCase_ :       Any=0.0							,							UpperCamelCase_ :       str=0.02							,							UpperCamelCase_ :       Dict=1e-12							,							UpperCamelCase_ :       Union[str, Any]=[5_12, 8_64]							,							UpperCamelCase_ :       Dict=16							,							UpperCamelCase_ :       Union[str, Any]=3							,							UpperCamelCase_ :       Any=True							,							UpperCamelCase_ :       Dict=1_00							,							UpperCamelCase_ :       Optional[int]=True							,							UpperCamelCase_ :       int=False							,							UpperCamelCase_ :       str=1							,							UpperCamelCase_ :       List[Any]=5							,							UpperCamelCase_ :       Dict=2							,							UpperCamelCase_ :       Union[str, Any]=5							,							UpperCamelCase_ :       str=2							,							UpperCamelCase_ :       Any=0.1							,							**UpperCamelCase_ :       str							,							)     ->				Optional[int]:
													super().__init__(**UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :List[Any]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :List[Any]						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Tuple						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :List[Any]						    = attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :Dict						    = initializer_range
													SCREAMING_SNAKE_CASE__     :Any						    = layer_norm_eps
													SCREAMING_SNAKE_CASE__     :int						    = image_size
													SCREAMING_SNAKE_CASE__     :List[Any]						    = patch_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = num_channels
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = qkv_bias
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = num_detection_tokens
													SCREAMING_SNAKE_CASE__     :List[Any]						    = use_mid_position_embeddings
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = auxiliary_loss
													# Hungarian matcher
													SCREAMING_SNAKE_CASE__     :int						    = class_cost
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = bbox_cost
													SCREAMING_SNAKE_CASE__     :List[Any]						    = giou_cost
													# Loss coefficients
													SCREAMING_SNAKE_CASE__     :List[Any]						    = bbox_loss_coefficient
													SCREAMING_SNAKE_CASE__     :int						    = giou_loss_coefficient
													SCREAMING_SNAKE_CASE__     :str						    = eos_coefficient




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Dict     		= version.parse('1.11'					)




						@property
						def      __lowerCamelCase       (    self :       Any      )     ->				Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
													    ]      )




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				float:
													return 1e-4




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				int:
													return 12







'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase_      	=	datasets.logging.get_logger(__name__)

UpperCamelCase_      	=	'''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

UpperCamelCase_      	=	'''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCamelCase_      	=	'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,							citation=_CITATION							,							homepage='https://unbabel.github.io/COMET/html/index.html'							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
													        {
													            'sources': datasets.Value('string'							,							id='sequence'      ),
													            'predictions': datasets.Value('string'							,							id='sequence'      ),
													            'references': datasets.Value('string'							,							id='sequence'      ),
													        }      )							,							codebase_urls=['https://github.com/Unbabel/COMET']							,							reference_urls=[
													        'https://github.com/Unbabel/COMET',
													        'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
													        'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
													    ]							,							)




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       List[str]      )     ->				Dict:
													if self.config_name == "default":
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'      )      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model(self.config_name      )      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Optional[Any]=False      )     ->				Any:
													if gpus is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = 1 if torch.cuda.is_available() else 0
													SCREAMING_SNAKE_CASE__     :Dict						    = {'src': sources, 'mt': predictions, 'ref': references}
													SCREAMING_SNAKE_CASE__     :List[str]						    = [dict(zip(UpperCamelCase_							,							UpperCamelCase_      )      ) for t in zip(*data.values()      )]
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.scorer.predict(UpperCamelCase_							,							gpus=UpperCamelCase_							,							progress_bar=UpperCamelCase_      )
													return {"mean_score": mean_score, "scores": scores}






