


'''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       UpperCAmelCase__ (     UpperCAmelCase_   :    List[Any]				,   UpperCAmelCase_   :    Optional[Any]				,   UpperCAmelCase_   :    Dict							)					->		List[str]:
     # Initialise PyTorch model
     __lowerCamelCase       :     Union[str, Any]        =		FNetConfig.from_json_file(UpperCAmelCase_							)
     print(F'Building PyTorch model from configuration: {config}'							)
     __lowerCamelCase       :     List[str]        =		FNetForPreTraining(UpperCAmelCase_							)

     __lowerCamelCase       :     Optional[Any]        =		restore_checkpoint(UpperCAmelCase_				,   UpperCAmelCase_							)
     __lowerCamelCase       :     List[str]        =		checkpoint_dict['target']

     # Embeddings
     # Position IDs
     __lowerCamelCase       :     Union[str, Any]        =		fnet_pretraining_model.state_dict()

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

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

          __lowerCamelCase       :     int        =		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['intermediate']['kernel']							).T
          __lowerCamelCase       :     Dict        =		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['intermediate']['bias']							)

          __lowerCamelCase       :     List[str]        =		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['output']['kernel']							).T
          __lowerCamelCase       :     Any        =		torch.tensor(
              pretrained_model_params['encoder'][F'feed_forward_{layer}']['output']['bias']							)

          __lowerCamelCase       :     List[str]        =		torch.tensor(
              pretrained_model_params['encoder'][F'encoder_{layer}']['output_layer_norm']['scale']							)
          __lowerCamelCase       :     Optional[int]        =		torch.tensor(
              pretrained_model_params['encoder'][F'encoder_{layer}']['output_layer_norm']['bias']							)

     # Pooler Layers
     __lowerCamelCase       :     str        =		torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']							).T
     __lowerCamelCase       :     Any        =		torch.tensor(pretrained_model_params['encoder']['pooler']['bias']							)

     # Masked LM Layers
     __lowerCamelCase       :     Union[str, Any]        =		torch.tensor(
         pretrained_model_params['predictions_dense']['kernel']							).T
     __lowerCamelCase       :     Optional[Any]        =		torch.tensor(
         pretrained_model_params['predictions_dense']['bias']							)
     __lowerCamelCase       :     str        =		torch.tensor(
         pretrained_model_params['predictions_layer_norm']['scale']							)
     __lowerCamelCase       :     List[Any]        =		torch.tensor(
         pretrained_model_params['predictions_layer_norm']['bias']							)
     __lowerCamelCase       :     List[str]        =		torch.tensor(
         pretrained_model_params['encoder']['embedder']['word']['embedding']							)
     __lowerCamelCase       :     List[str]        =		torch.tensor(
         pretrained_model_params['predictions_output']['output_bias']							)
     __lowerCamelCase       :     List[Any]        =		torch.tensor(pretrained_model_params['predictions_output']['output_bias']							)

     # Seq Relationship Layers
     __lowerCamelCase       :     str        =		torch.tensor(
         pretrained_model_params['classification']['output_kernel']							)
     __lowerCamelCase       :     Dict        =		torch.tensor(
         pretrained_model_params['classification']['output_bias']							)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(UpperCAmelCase_							)

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


if __name__ == "__main__":
      A__		: int												=  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[int]												=  parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




'''simple docstring'''



import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
      A__		: Optional[int]												=  []

      A__		: List[Any]												=  os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      A__		: Optional[int]												=  glob.glob(pattern)
      # TODO: deal with TF/Flax too
      A__		: List[str]												=  [
          x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
      ]

      for test_file in test_files:
            A__		: Tuple												=  get_tester_classes(test_file)
            for tester_class in tester_classes:
                  # A few tester classes don't have `parent` parameter in `__init__`.
                  # TODO: deal this better
                  try:
                        A__		: Tuple												=  tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        A__		: Any												=  tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    A__		: List[Any]												=  None
                                    if k in ["vocab_size"]:
                                          A__		: Any												=  100
                                    elif k in ["max_position_embeddings"]:
                                          A__		: List[Any]												=  128
                                    elif k in ["hidden_size", "d_model"]:
                                          A__		: List[str]												=  40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          A__		: str												=  5
                                    if target is not None and v > target:
                                          failures.append(
                                              f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                              f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                              f''' than {target}.'''
                                          )

      if len(failures) > 0:
            raise Exception(f'''There were {len(failures)} failures:\n''' + """\n""".join(failures))






