


'''simple docstring'''






import copy
import os
from typing import Union

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


__UpperCAmelCase       =    logging.get_logger(__name__)

__UpperCAmelCase       =    {
    """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 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				"""bridgetower_vision_model"""







							def __init__(		self ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=12 ,   lowerCamelCase_=3 ,   lowerCamelCase_=16 ,   lowerCamelCase_=2_88 ,   lowerCamelCase_=1 ,   lowerCamelCase_=1e-05 ,   lowerCamelCase_=False ,   lowerCamelCase_=True ,   lowerCamelCase_=False ,   **lowerCamelCase_ ,   )	->							Optional[Any]:
														super().__init__(**_lowercase       )
														lowerCAmelCase__	       =     hidden_size
														lowerCAmelCase__	       =     num_hidden_layers
														lowerCAmelCase__	       =     num_channels
														lowerCAmelCase__	       =     patch_size
														lowerCAmelCase__	       =     image_size
														lowerCAmelCase__	       =     initializer_factor
														lowerCAmelCase__	       =     layer_norm_eps
														lowerCAmelCase__	       =     stop_gradient
														lowerCAmelCase__	       =     share_layernorm
														lowerCAmelCase__	       =     remove_last_layer







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							"PretrainedConfig":
														lowerCAmelCase__       , lowerCAmelCase__	       =     cls.get_config_dict(_lowercase ,   **_lowercase       )

														if config_dict.get('''model_type'''       ) == "bridgetower":
																					lowerCAmelCase__	       =     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(_lowercase ,   **_lowercase       )




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[int]     						=				"""bridgetower_text_model"""







							def __init__(		self ,   lowerCamelCase_=5_02_65 ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=12 ,   lowerCamelCase_=12 ,   lowerCamelCase_=1 ,   lowerCamelCase_=30_72 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=5_14 ,   lowerCamelCase_=1 ,   lowerCamelCase_=1e-05 ,   lowerCamelCase_=1 ,   lowerCamelCase_=0 ,   lowerCamelCase_=2 ,   lowerCamelCase_="absolute" ,   lowerCamelCase_=True ,   **lowerCamelCase_ ,   )	->							Optional[int]:
														super().__init__(**_lowercase       )

														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     hidden_size
														lowerCAmelCase__	       =     num_hidden_layers
														lowerCAmelCase__	       =     num_attention_heads
														lowerCAmelCase__	       =     hidden_act
														lowerCAmelCase__	       =     initializer_factor
														lowerCAmelCase__	       =     intermediate_size
														lowerCAmelCase__	       =     hidden_dropout_prob
														lowerCAmelCase__	       =     attention_probs_dropout_prob
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     type_vocab_size
														lowerCAmelCase__	       =     layer_norm_eps
														lowerCAmelCase__	       =     position_embedding_type
														lowerCAmelCase__	       =     use_cache
														lowerCAmelCase__	       =     pad_token_id
														lowerCAmelCase__	       =     bos_token_id
														lowerCAmelCase__	       =     eos_token_id







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							"PretrainedConfig":
														lowerCAmelCase__       , lowerCAmelCase__	       =     cls.get_config_dict(_lowercase ,   **_lowercase       )

														if config_dict.get('''model_type'''       ) == "bridgetower":
																					lowerCAmelCase__	       =     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(_lowercase ,   **_lowercase       )




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				"""bridgetower"""







							def __init__(		self ,   lowerCamelCase_=True ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=1 ,   lowerCamelCase_=1e-05 ,   lowerCamelCase_=False ,   lowerCamelCase_="add" ,   lowerCamelCase_=12 ,   lowerCamelCase_=6 ,   lowerCamelCase_=False ,   lowerCamelCase_=False ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   **lowerCamelCase_ ,   )	->							List[Any]:
														# TODO: remove this once the Hub files are updated.
														lowerCAmelCase__	       =     kwargs.pop('''text_config_dict''' ,   _lowercase       )
														lowerCAmelCase__	       =     kwargs.pop('''vision_config_dict''' ,   _lowercase       )

														super().__init__(**_lowercase       )
														lowerCAmelCase__	       =     share_cross_modal_transformer_layers
														lowerCAmelCase__	       =     hidden_act
														lowerCAmelCase__	       =     hidden_size
														lowerCAmelCase__	       =     initializer_factor
														lowerCAmelCase__	       =     layer_norm_eps
														lowerCAmelCase__	       =     share_link_tower_layers
														lowerCAmelCase__	       =     link_tower_type
														lowerCAmelCase__	       =     num_attention_heads
														lowerCAmelCase__	       =     num_hidden_layers
														lowerCAmelCase__	       =     tie_word_embeddings
														lowerCAmelCase__	       =     init_layernorm_from_vision_encoder

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

														if vision_config is None:
																					lowerCAmelCase__	       =     {}
																					logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''       )

														lowerCAmelCase__	       =     BridgeTowerTextConfig(**_lowercase       )
														lowerCAmelCase__	       =     BridgeTowerVisionConfig(**_lowercase       )







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							List[str]:
														return cls(text_config=text_config.to_dict() ,   vision_config=vision_config.to_dict() ,   **_lowercase       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     copy.deepcopy(self.__dict__       )
														lowerCAmelCase__	       =     self.text_config.to_dict()
														lowerCAmelCase__	       =     self.vision_config.to_dict()
														lowerCAmelCase__	       =     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       =							'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  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},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

__UpperCAmelCase       =							'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

__UpperCAmelCase       =							'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''


def  _snake_case       (					A			,   A	)						->   List[Any]:
							return float((preds == labels).mean()	)


def  _snake_case       (					A			,   A			,   A="binary"	)						->   int:
							lowerCAmelCase__	       =     simple_accuracy(A			,   A	)
							lowerCAmelCase__	       =     float(fa_score(y_true=A			,   y_pred=A			,   average=A	)	)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


def  _snake_case       (					A			,   A	)						->   List[Any]:
							lowerCAmelCase__	       =     {}
							for id_pred, label in zip(A			,   A	):
														lowerCAmelCase__	       =     F"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
														lowerCAmelCase__	       =     id_pred['''prediction''']
														if question_id in question_map:
																					question_map[question_id].append((pred, label)	)
														else:
																					lowerCAmelCase__	       =     [(pred, label)]
							lowerCAmelCase__       , lowerCAmelCase__	       =     [], []
							for question, preds_labels in question_map.items():
														lowerCAmelCase__       , lowerCAmelCase__	       =     zip(*A	)
														lowerCAmelCase__	       =     fa_score(y_true=A			,   y_pred=A			,   average='''macro'''	)
														fas.append(A	)
														lowerCAmelCase__	       =     int(sum(pred == label for pred, label in preds_labels	) == len(A	)	)
														ems.append(A	)
							lowerCAmelCase__	       =     float(sum(A	) / len(A	)	)
							lowerCAmelCase__	       =     sum(A	) / len(A	)
							lowerCAmelCase__	       =     float(fa_score(y_true=A			,   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 		a__    (							datasets.Metric  ):





							'''simple docstring'''






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