
"""simple docstring"""

import os

from ...utils import logging
from .utils import DataProcessor, InputExample


SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      def __init__(							self				,    lowercase_				,    lowercase_=None							)  ->   Optional[Any]:
          UpperCAmelCase       =   language
          UpperCAmelCase       =   train_language




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:
          UpperCAmelCase       =   self.language if self.train_language is None else self.train_language
          UpperCAmelCase       =   self._read_tsv(os.path.join(lowercase_				,    F"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"							)							)
          UpperCAmelCase       =   []
          for i, line in enumerate(lowercase_							):
              if i == 0:
                  continue
              UpperCAmelCase       =   F"train-{i}"
              UpperCAmelCase       =   line[0]
              UpperCAmelCase       =   line[1]
              UpperCAmelCase       =   'contradiction' if line[2] == 'contradictory' else line[2]
              if not isinstance(lowercase_				,    lowercase_							):
                  raise ValueError(F"Training input {text_a} is not a string"							)
              if not isinstance(lowercase_				,    lowercase_							):
                  raise ValueError(F"Training input {text_b} is not a string"							)
              if not isinstance(lowercase_				,    lowercase_							):
                  raise ValueError(F"Training label {label} is not a string"							)
              examples.append(InputExample(guid=lowercase_				,    text_a=lowercase_				,    text_b=lowercase_				,    label=lowercase_							)							)
          return examples




      def       a_ (							self				,    lowercase_							)  ->   Optional[int]:
          UpperCAmelCase       =   self._read_tsv(os.path.join(lowercase_				,    'XNLI-1.0/xnli.test.tsv'							)							)
          UpperCAmelCase       =   []
          for i, line in enumerate(lowercase_							):
              if i == 0:
                  continue
              UpperCAmelCase       =   line[0]
              if language != self.language:
                  continue
              UpperCAmelCase       =   F"test-{i}"
              UpperCAmelCase       =   line[6]
              UpperCAmelCase       =   line[7]
              UpperCAmelCase       =   line[1]
              if not isinstance(lowercase_				,    lowercase_							):
                  raise ValueError(F"Training input {text_a} is not a string"							)
              if not isinstance(lowercase_				,    lowercase_							):
                  raise ValueError(F"Training input {text_b} is not a string"							)
              if not isinstance(lowercase_				,    lowercase_							):
                  raise ValueError(F"Training label {label} is not a string"							)
              examples.append(InputExample(guid=lowercase_				,    text_a=lowercase_				,    text_b=lowercase_				,    label=lowercase_							)							)
          return examples




      def       a_ (							self							)  ->   List[Any]:
          return ["contradiction", "entailment", "neutral"]

SCREAMING_SNAKE_CASE_         =  {
    '''xnli''': XnliProcessor,
}

SCREAMING_SNAKE_CASE_         =  {
    '''xnli''': '''classification''',
}

SCREAMING_SNAKE_CASE_         =  {
    '''xnli''': 3,
}





"""simple docstring"""

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       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

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

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

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

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

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

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

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

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

    # Seq Relationship Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_kernel']	)
    UpperCAmelCase       =   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__":
   SCREAMING_SNAKE_CASE_         =  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.''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



