




'''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     snake_case_  (       a__		:     Dict   ,a__		:     Tuple   ,a__		:     Union[str, Any]      ):



    """simple docstring"""




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

    __lowercase          =						restore_checkpoint(a__   ,a__      )
    __lowercase          =						checkpoint_dict["""target"""]

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

    __lowercase          =						state_dict["""fnet.embeddings.position_ids"""]
    __lowercase          =						{"""fnet.embeddings.position_ids""": position_ids}
    # Embedding Layers
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )

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

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

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

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

    # Pooler Layers
    __lowercase          =						torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
    __lowercase          =						torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )

    # Masked LM Layers
    __lowercase          =						torch.tensor(
        pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
    __lowercase          =						torch.tensor(
        pretrained_model_params["""predictions_dense"""]["""bias"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
    __lowercase          =						torch.tensor(
        pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
    __lowercase          =						torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )

    # Seq Relationship Layers
    __lowercase          =						torch.tensor(
        pretrained_model_params["""classification"""]["""output_kernel"""]      )
    __lowercase          =						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__":
  A							:				Tuple			     =	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.""")
  A							:				Optional[Any]			     =	parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)










'''simple docstring'''
import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




