from sklearn.metrics import mean_absolute_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 Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\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\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,     _KWARGS_DESCRIPTION						)
class 				a_ (						datasets.Metric						):






 '''simple docstring'''






 def 		__snake_case						(			self					:  Optional[Any]):







        '''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_absolute_error.html'
            ]			,    )




 def 		__snake_case						(			self					:  List[Any]):







        '''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 		__snake_case						(			self					:  str			,    lowercase__					:  Optional[int]			,    lowercase__					:  Optional[Any]			,    lowercase__					:  Dict=None			,    lowercase__					:  Optional[int]="uniform_average"):







        '''simple docstring'''



        lowerCAmelCase__     	=					mean_absolute_error(lowercase__			,    lowercase__			,    sample_weight=lowercase__			,    multioutput=lowercase__)

        return {"mae": mae_score}

import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       # Initialise PyTorch model
       lowerCAmelCase__     	=					FNetConfig.from_json_file(lowerCAmelCase__	)
       print(F"""Building PyTorch model from configuration: {config}"""	)
       lowerCAmelCase__     	=					FNetForPreTraining(lowerCAmelCase__	)

       lowerCAmelCase__     	=					restore_checkpoint(lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					checkpoint_dict['target']

       # Embeddings
       # Position IDs
       lowerCAmelCase__     	=					fnet_pretraining_model.state_dict()

       lowerCAmelCase__     	=					state_dict['fnet.embeddings.position_ids']
       lowerCAmelCase__     	=					{'fnet.embeddings.position_ids': position_ids}
       # Embedding Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['word']['embedding']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['type']['embedding']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

       # Encoder Layers
       for layer in range(config.num_hidden_layers	):
              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""encoder_{layer}"""]['mixing_layer_norm']['scale']	)
              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""encoder_{layer}"""]['mixing_layer_norm']['bias']	)

              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['kernel']	).T
              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['bias']	)

              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['output']['kernel']	).T
              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['output']['bias']	)

              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['scale']	)
              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['bias']	)

       # Pooler Layers
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

       # Masked LM Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_dense']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_dense']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_layer_norm']['scale']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_layer_norm']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['word']['embedding']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_output']['output_bias']	)
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

       # Seq Relationship Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['classification']['output_kernel']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['classification']['output_bias']	)

       # Load State Dict
       fnet_pretraining_model.load_state_dict(lowerCAmelCase__	)

       # Save PreTrained
       print(F"""Saving pretrained model to {save_path}"""	)
       fnet_pretraining_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
    lowerCAmelCase__							= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--fnet_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained FNet model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
    lowerCAmelCase__							= parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
