


'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


UpperCAmelCase_      :		Optional[Any]          =		"\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

UpperCAmelCase_      :		str          =		"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


UpperCAmelCase_      :		Optional[int]          =		"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,							_KWARGS_DESCRIPTION  )
class 					a			(				datasets.Metric  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    return datasets.MetricInfo(
        description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(self._get_feature_types()      )		,      reference_urls=[
            'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
        ]		,      )




 def   __UpperCamelCase   (    self      )      ->    Dict:
    if self.config_name == "multilist":
       return {
           "predictions": datasets.Sequence(datasets.Value('float'      )      ),
           "references": datasets.Sequence(datasets.Value('float'      )      ),
       }
    else:
       return {
           "predictions": datasets.Value('float'      ),
           "references": datasets.Value('float'      ),
       }




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_=None		,      lowerCamelCase_="uniform_average"		,      lowerCamelCase_=True      )      ->    List[str]:
    _a					:  List[str]						=  mean_squared_error(
        lowerCamelCase_		,      lowerCamelCase_		,      sample_weight=lowerCamelCase_		,      multioutput=lowerCamelCase_		,      squared=lowerCamelCase_      )

    return {"mse": mse}






'''simple docstring'''




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


UpperCAmelCase_      :		Dict          =		logging.get_logger(__name__)

UpperCAmelCase_      :		List[str]          =		{
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		"""visual_bert"""




 def __init__(    self		,      lowerCamelCase_=3_0_5_2_2		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=2		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-12		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      lowerCamelCase_=1		,      lowerCamelCase_=0		,      lowerCamelCase_=2		,      **lowerCamelCase_		,      )      ->    Optional[Any]:
    super().__init__(pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_		,      **lowerCamelCase_      )

    _a					:  List[str]						=  vocab_size
    _a					:  Union[str, Any]						=  max_position_embeddings
    _a					:  Tuple						=  hidden_size
    _a					:  Tuple						=  visual_embedding_dim
    _a					:  Optional[Any]						=  num_hidden_layers
    _a					:  str						=  num_attention_heads
    _a					:  Union[str, Any]						=  intermediate_size
    _a					:  List[str]						=  hidden_act
    _a					:  str						=  hidden_dropout_prob
    _a					:  List[Any]						=  attention_probs_dropout_prob
    _a					:  Optional[Any]						=  initializer_range
    _a					:  List[Any]						=  type_vocab_size
    _a					:  Any						=  layer_norm_eps
    _a					:  Tuple						=  bypass_transformer
    _a					:  str						=  special_visual_initialize


