


from typing import Mapping

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


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Optional[Any]      ='umt5'
      UpperCamelCase__							:  Optional[Any]      =['past_key_values']






      def __init__(					self						:     Any			,   lowercase_						:     Optional[Any]=25_0112			,   lowercase_						:     Optional[Any]=512			,   lowercase_						:     Dict=64			,   lowercase_						:     str=1024			,   lowercase_						:     Optional[int]=8			,   lowercase_						:     Union[str, Any]=None			,   lowercase_						:     Union[str, Any]=6			,   lowercase_						:     List[str]=32			,   lowercase_						:     Dict=128			,   lowercase_						:     List[Any]=0.1			,   lowercase_						:     List[Any]=1E-6			,   lowercase_						:     Tuple=1.0			,   lowercase_						:     str="gated-gelu"			,   lowercase_						:     int=True			,   lowercase_						:     List[Any]=True			,   lowercase_						:     Optional[int]="T5Tokenizer"			,   lowercase_						:     List[str]=True			,   lowercase_						:     Optional[Any]=0			,   lowercase_						:     str=1			,   lowercase_						:     int=0			,   **lowercase_						:     List[Any]			,   )					->						Tuple:
             """simple docstring"""




             super().__init__(
                 is_encoder_decoder=lowercase_			,   tokenizer_class=lowercase_			,   tie_word_embeddings=lowercase_			,   pad_token_id=lowercase_			,   eos_token_id=lowercase_			,   decoder_start_token_id=lowercase_			,   **lowercase_			,   )
             _lowerCamelCase       :		List[str]		=vocab_size
             _lowerCamelCase       :		int		=d_model
             _lowerCamelCase       :		Any		=d_kv
             _lowerCamelCase       :		str		=d_ff
             _lowerCamelCase       :		Union[str, Any]		=num_layers
             _lowerCamelCase       :		Union[str, Any]		=(
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             _lowerCamelCase       :		Any		=num_heads
             _lowerCamelCase       :		List[str]		=relative_attention_num_buckets
             _lowerCamelCase       :		Union[str, Any]		=relative_attention_max_distance
             _lowerCamelCase       :		List[Any]		=dropout_rate
             _lowerCamelCase       :		str		=layer_norm_epsilon
             _lowerCamelCase       :		Union[str, Any]		=initializer_factor
             _lowerCamelCase       :		Optional[Any]		=feed_forward_proj
             _lowerCamelCase       :		List[str]		=use_cache

             _lowerCamelCase       :		Tuple		=self.feed_forward_proj.split('-' )
             _lowerCamelCase       :		Union[str, Any]		=act_info[-1]
             _lowerCamelCase       :		Any		=act_info[0] == 'gated'

             if len(lowercase_ ) > 1 and act_info[0] != "gated" or len(lowercase_ ) > 2:
                    raise ValueError(
                        F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                        'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                        '\'gated-gelu\' or \'relu\'' )

             if feed_forward_proj == "gated-gelu":
                    _lowerCamelCase       :		int		='gelu_new'






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						Any:
             """simple docstring"""




             return self.d_model






      @property
      def        lowerCamelCase       (					self						:     Optional[int] )					->						Optional[int]:
             """simple docstring"""




             return self.num_heads






      @property
      def        lowerCamelCase       (					self						:     int )					->						int:
             """simple docstring"""




             return self.num_layers




class A  (   UpperCamelCase_     ):






      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def        lowerCamelCase       (					self						:     str )					->						Mapping[str, Mapping[int, str]]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={
                 'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                 'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
             }
             if self.use_past:
                    _lowerCamelCase       :		str		='past_encoder_sequence + sequence'
                    _lowerCamelCase       :		Union[str, Any]		={0: 'batch'}
                    _lowerCamelCase       :		int		={0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                    _lowerCamelCase       :		List[str]		={0: 'batch', 1: 'decoder_sequence'}
                    _lowerCamelCase       :		List[str]		={0: 'batch', 1: 'decoder_sequence'}

             if self.use_past:
                    self.fill_with_past_key_values_(lowercase_			,   direction='inputs' )

             return common_inputs






      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def        lowerCamelCase       (					self						:     Tuple )					->						int:
             """simple docstring"""




             return 13






      @property
      def        lowerCamelCase       (					self						:     Optional[int] )					->						float:
             """simple docstring"""




             return 5E-4






from sklearn.metrics import mean_squared_error

import datasets


lowerCamelCase											=       '\\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'

lowerCamelCase											=       '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCamelCase											=       '\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     ):






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             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        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             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        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=None			,   lowercase_						:     Union[str, Any]="uniform_average"			,   lowercase_						:     Tuple=True )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=mean_squared_error(
                 lowercase_			,   lowercase_			,   sample_weight=lowercase_			,   multioutput=lowercase_			,   squared=lowercase_ )

             return {"mse": mse}


