




import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase_   							=      '\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

UpperCAmelCase_   							=      '\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


UpperCAmelCase_   							=      '\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION)
class        lowerCamelCase__(   datasets.Metric):
      def      lowerCAmelCase__		(							self:					List[str]  ):
             return datasets.MetricInfo(
                 description=_DESCRIPTION       ,			citation=_CITATION       ,			inputs_description=_KWARGS_DESCRIPTION       ,			features=datasets.Features(
                     {
                         """predictions""": datasets.Value("""string"""  ),
                         """references""": datasets.Value("""string"""  ),
                     }  )       ,			homepage="""https://github.com/moussaKam/FrugalScore"""       ,			)
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					Optional[int]  ):
             if self.config_name == "default":
                    __lowerCamelCase  =		"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
             else:
                    __lowerCamelCase  =		self.config_name
             __lowerCamelCase  =		AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		AutoTokenizer.from_pretrained(UpperCamelCase_  )




      def      lowerCAmelCase__		(							self:					Any       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					Any       ,			UpperCamelCase_:					Optional[Any]=32       ,			UpperCamelCase_:					Union[str, Any]=1_28       ,			UpperCamelCase_:					Optional[int]=None       ,			):
             assert len(UpperCamelCase_  ) == len(
                 UpperCamelCase_  ), "predictions and references should have the same number of sentences."
             if device is not None:
                    assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                    __lowerCamelCase  =		"""gpu""" if torch.cuda.is_available() else """cpu"""
             __lowerCamelCase  =		TrainingArguments(
                 """trainer"""       ,			fpaa=(device == """gpu""")       ,			per_device_eval_batch_size=UpperCamelCase_       ,			report_to="""all"""       ,			no_cuda=(device == """cpu""")       ,			log_level="""warning"""       ,			)
             __lowerCamelCase  =		{"""sentence1""": predictions, """sentence2""": references}
             __lowerCamelCase  =		datasets.Dataset.from_dict(UpperCamelCase_  )

             def tokenize_function(UpperCamelCase_:					Optional[Any]  ):
                    return self.tokenizer(
                        data["""sentence1"""]       ,			data["""sentence2"""]       ,			max_length=UpperCamelCase_       ,			truncation=UpperCamelCase_       ,			padding=UpperCamelCase_  )

             __lowerCamelCase  =		raw_datasets.map(UpperCamelCase_       ,			batched=UpperCamelCase_  )
             tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]  )
             __lowerCamelCase  =		Trainer(self.model       ,			UpperCamelCase_       ,			tokenizer=self.tokenizer  )
             __lowerCamelCase  =		trainer.predict(UpperCamelCase_  )
             return {"scores": list(predictions.predictions.squeeze(-1  )  )}







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__			(		A__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __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(A__      )

       # Save PreTrained
       print(f'Saving pretrained model to {save_path}'      )
       fnet_pretraining_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      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.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

