



'''simple docstring'''






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__:     List[str]										=    logging.get_logger(__name__)

UpperCamelCase__:     Any										=    {
    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
    "BridgeTower/bridgetower-base-itm-mlm": (
        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
    ),
}

class        SCREAMING_SNAKE_CASE(							A__    ):







 """simple docstring"""






 lowerCamelCase__    				= """bridgetower_vision_model"""







 def __init__(		self    :  Tuple	,    __snake_case    :  Any=768	,    __snake_case    :  str=12	,    __snake_case    :  Any=3	,    __snake_case    :  Optional[int]=16	,    __snake_case    :  Optional[int]=288	,    __snake_case    :  int=1	,    __snake_case    :  Union[str, Any]=1E-05	,    __snake_case    :  Optional[Any]=False	,    __snake_case    :  Tuple=True	,    __snake_case    :  List[Any]=False	,    **__snake_case    :  int	,    )    ->	Optional[Any]:
        super().__init__(**__a	)
        UpperCAmelCase				: Any											=	hidden_size
        UpperCAmelCase				: Dict											=	num_hidden_layers
        UpperCAmelCase				: int											=	num_channels
        UpperCAmelCase				: Optional[int]											=	patch_size
        UpperCAmelCase				: int											=	image_size
        UpperCAmelCase				: Tuple											=	initializer_factor
        UpperCAmelCase				: List[str]											=	layer_norm_eps
        UpperCAmelCase				: Optional[int]											=	stop_gradient
        UpperCAmelCase				: Tuple											=	share_layernorm
        UpperCAmelCase				: List[str]											=	remove_last_layer







 @classmethod
 def   A			(		cls    :  Dict	,    __snake_case    :  Union[str, Any]	,    **__snake_case    :  str	)    ->	"PretrainedConfig":
        UpperCAmelCase							,	UpperCAmelCase				: str											=	cls.get_config_dict(__a	,    **__a	)

        if config_dict.get('''model_type'''	) == "bridgetower":
               UpperCAmelCase				: Any											=	config_dict['''text_config''']

        if "model_type" in config_dict and hasattr(cls	,    '''model_type'''	) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                   F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""	)

        return cls.from_dict(__a	,    **__a	)

class        SCREAMING_SNAKE_CASE(							A__    ):







 """simple docstring"""






 lowerCamelCase__    				= """bridgetower_text_model"""







 def __init__(		self    :  Dict	,    __snake_case    :  List[str]=50265	,    __snake_case    :  Union[str, Any]=768	,    __snake_case    :  Optional[Any]=12	,    __snake_case    :  str=12	,    __snake_case    :  Optional[Any]=1	,    __snake_case    :  List[Any]=3072	,    __snake_case    :  Dict="gelu"	,    __snake_case    :  Optional[int]=0.1	,    __snake_case    :  Optional[int]=0.1	,    __snake_case    :  Any=514	,    __snake_case    :  Optional[Any]=1	,    __snake_case    :  Optional[int]=1E-05	,    __snake_case    :  Any=1	,    __snake_case    :  List[Any]=0	,    __snake_case    :  int=2	,    __snake_case    :  Union[str, Any]="absolute"	,    __snake_case    :  Union[str, Any]=True	,    **__snake_case    :  Optional[int]	,    )    ->	Optional[int]:
        super().__init__(**__a	)

        UpperCAmelCase				: Optional[Any]											=	vocab_size
        UpperCAmelCase				: Any											=	hidden_size
        UpperCAmelCase				: Optional[Any]											=	num_hidden_layers
        UpperCAmelCase				: Dict											=	num_attention_heads
        UpperCAmelCase				: Optional[int]											=	hidden_act
        UpperCAmelCase				: Any											=	initializer_factor
        UpperCAmelCase				: str											=	intermediate_size
        UpperCAmelCase				: int											=	hidden_dropout_prob
        UpperCAmelCase				: Tuple											=	attention_probs_dropout_prob
        UpperCAmelCase				: List[Any]											=	max_position_embeddings
        UpperCAmelCase				: Any											=	type_vocab_size
        UpperCAmelCase				: Optional[int]											=	layer_norm_eps
        UpperCAmelCase				: Optional[Any]											=	position_embedding_type
        UpperCAmelCase				: List[str]											=	use_cache
        UpperCAmelCase				: int											=	pad_token_id
        UpperCAmelCase				: Optional[int]											=	bos_token_id
        UpperCAmelCase				: Tuple											=	eos_token_id







 @classmethod
 def   A			(		cls    :  int	,    __snake_case    :  Optional[Any]	,    **__snake_case    :  str	)    ->	"PretrainedConfig":
        UpperCAmelCase							,	UpperCAmelCase				: str											=	cls.get_config_dict(__a	,    **__a	)

        if config_dict.get('''model_type'''	) == "bridgetower":
               UpperCAmelCase				: List[str]											=	config_dict['''text_config''']

        if "model_type" in config_dict and hasattr(cls	,    '''model_type'''	) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                   F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""	)

        return cls.from_dict(__a	,    **__a	)

class        SCREAMING_SNAKE_CASE(							A__    ):







 """simple docstring"""






 lowerCamelCase__    				= """bridgetower"""







 def __init__(		self    :  Optional[int]	,    __snake_case    :  int=True	,    __snake_case    :  Any="gelu"	,    __snake_case    :  str=768	,    __snake_case    :  Dict=1	,    __snake_case    :  Dict=1E-05	,    __snake_case    :  Any=False	,    __snake_case    :  Dict="add"	,    __snake_case    :  Union[str, Any]=12	,    __snake_case    :  Any=6	,    __snake_case    :  Optional[Any]=False	,    __snake_case    :  str=False	,    __snake_case    :  str=None	,    __snake_case    :  Dict=None	,    **__snake_case    :  int	,    )    ->	Union[str, Any]:
        UpperCAmelCase				: List[str]											=	kwargs.pop('''text_config_dict'''	,    __a	)
        UpperCAmelCase				: str											=	kwargs.pop('''vision_config_dict'''	,    __a	)

        super().__init__(**__a	)
        UpperCAmelCase				: Optional[int]											=	share_cross_modal_transformer_layers
        UpperCAmelCase				: str											=	hidden_act
        UpperCAmelCase				: Optional[int]											=	hidden_size
        UpperCAmelCase				: List[str]											=	initializer_factor
        UpperCAmelCase				: Optional[Any]											=	layer_norm_eps
        UpperCAmelCase				: str											=	share_link_tower_layers
        UpperCAmelCase				: Dict											=	link_tower_type
        UpperCAmelCase				: Any											=	num_attention_heads
        UpperCAmelCase				: Any											=	num_hidden_layers
        UpperCAmelCase				: Tuple											=	tie_word_embeddings
        UpperCAmelCase				: Tuple											=	init_layernorm_from_vision_encoder

        if text_config is None:
               UpperCAmelCase				: int											=	{}
               logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'''	)

        if vision_config is None:
               UpperCAmelCase				: List[Any]											=	{}
               logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''	)

        UpperCAmelCase				: str											=	BridgeTowerTextConfig(**__a	)
        UpperCAmelCase				: Tuple											=	BridgeTowerVisionConfig(**__a	)







 @classmethod
 def   A			(		cls    :  List[Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  List[Any]	,    **__snake_case    :  Optional[int]	)    ->	Dict:
        return cls(text_config=text_config.to_dict()	,    vision_config=vision_config.to_dict()	,    **__a	)







 def   A			(		self    :  Any	)    ->	Dict:
        UpperCAmelCase				: Any											=	copy.deepcopy(self.__dict__	)
        UpperCAmelCase				: int											=	self.text_config.to_dict()
        UpperCAmelCase				: List[Any]											=	self.vision_config.to_dict()
        UpperCAmelCase				: Tuple											=	self.__class__.model_type
        return output










'''simple docstring'''






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase__:     int										=    "\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

UpperCamelCase__:     Dict										=    "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

UpperCamelCase__:     str										=    "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Tuple						)   ->     Dict:
							return float((preds == labels).mean()						)


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Dict="binary"						)   ->     Any:
							UpperCAmelCase				: str											=	simple_accuracy(_lowerCAmelCase     ,   _lowerCAmelCase						)
							UpperCAmelCase				: str											=	float(fa_score(y_true=_lowerCAmelCase     ,   y_pred=_lowerCAmelCase     ,   average=_lowerCAmelCase						)						)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		str						)   ->     List[str]:
							UpperCAmelCase				: Optional[int]											=	{}
							for id_pred, label in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														UpperCAmelCase				: Union[str, Any]											=	f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
														UpperCAmelCase				: List[str]											=	id_pred['''prediction''']
														if question_id in question_map:
																					question_map[question_id].append((pred, label)						)
														else:
																					UpperCAmelCase				: Union[str, Any]											=	[(pred, label)]
							UpperCAmelCase							,	UpperCAmelCase				: Dict											=	[], []
							for question, preds_labels in question_map.items():
														UpperCAmelCase							,	UpperCAmelCase				: List[Any]											=	zip(*_lowerCAmelCase						)
														UpperCAmelCase				: int											=	fa_score(y_true=_lowerCAmelCase     ,   y_pred=_lowerCAmelCase     ,   average='''macro'''						)
														fas.append(_lowerCAmelCase						)
														UpperCAmelCase				: Dict											=	int(sum(pred == label for pred, label in preds_labels						) == len(_lowerCAmelCase						)						)
														ems.append(_lowerCAmelCase						)
							UpperCAmelCase				: int											=	float(sum(_lowerCAmelCase						) / len(_lowerCAmelCase						)						)
							UpperCAmelCase				: Optional[int]											=	sum(_lowerCAmelCase						) / len(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	float(fa_score(y_true=_lowerCAmelCase     ,   y_pred=[id_pred['''prediction'''] for id_pred in ids_preds]						)						)
							return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}




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







	"""simple docstring"""






	def   A			(		self    :  Optional[Any]	)    ->	Dict:
								if self.config_name not in [
								    "boolq",
								    "cb",
								    "copa",
								    "multirc",
								    "record",
								    "rte",
								    "wic",
								    "wsc",
								    "wsc.fixed",
								    "axb",
								    "axg",
								]:
															raise KeyError(
															    '''You should supply a configuration name selected in '''
															    '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''	)
								return datasets.MetricInfo(
								    description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(self._get_feature_types()	)	,    codebase_urls=[]	,    reference_urls=[]	,    format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None	,    )







	def   A			(		self    :  Union[str, Any]	)    ->	Dict:
								if self.config_name == "record":
															return {
															    "predictions": {
															        "idx": {
															            "passage": datasets.Value('''int64'''	),
															            "query": datasets.Value('''int64'''	),
															        },
															        "prediction_text": datasets.Value('''string'''	),
															    },
															    "references": {
															        "idx": {
															            "passage": datasets.Value('''int64'''	),
															            "query": datasets.Value('''int64'''	),
															        },
															        "answers": datasets.Sequence(datasets.Value('''string'''	)	),
															    },
															}
								elif self.config_name == "multirc":
															return {
															    "predictions": {
															        "idx": {
															            "answer": datasets.Value('''int64'''	),
															            "paragraph": datasets.Value('''int64'''	),
															            "question": datasets.Value('''int64'''	),
															        },
															        "prediction": datasets.Value('''int64'''	),
															    },
															    "references": datasets.Value('''int64'''	),
															}
								else:
															return {
															    "predictions": datasets.Value('''int64'''	),
															    "references": datasets.Value('''int64'''	),
															}







	def   A			(		self    :  Dict	,    __snake_case    :  str	,    __snake_case    :  str	)    ->	Optional[int]:
								if self.config_name == "axb":
															return {"matthews_correlation": matthews_corrcoef(__snake_case	,    __snake_case	)}
								elif self.config_name == "cb":
															return acc_and_fa(__snake_case	,    __snake_case	,    fa_avg='''macro'''	)
								elif self.config_name == "record":
															UpperCAmelCase				: Tuple											=	[
															    {
															        '''qas''': [
															            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
															            for ref in references
															        ]
															    }
															]
															UpperCAmelCase				: int											=	{pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
															return evaluate_record(__snake_case	,    __snake_case	)[0]
								elif self.config_name == "multirc":
															return evaluate_multirc(__snake_case	,    __snake_case	)
								elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
															return {"accuracy": simple_accuracy(__snake_case	,    __snake_case	)}
								else:
															raise KeyError(
															    '''You should supply a configuration name selected in '''
															    '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''	)





