





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      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _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__":
       lowercase     :			Union[str, Any]     						=	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.""")
       lowercase     :			Dict     						=	parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__   :						Optional[Any] 							=     {"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						List[Any] 							=     ["""ViltFeatureExtractor"""]
    A__   :						Any 							=     ["""ViltImageProcessor"""]
    A__   :						int 							=     ["""ViltProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						str 							=     [
        """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViltForImageAndTextRetrieval""",
        """ViltForImagesAndTextClassification""",
        """ViltForTokenClassification""",
        """ViltForMaskedLM""",
        """ViltForQuestionAnswering""",
        """ViltLayer""",
        """ViltModel""",
        """ViltPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_vilt import ViltFeatureExtractor
        from .image_processing_vilt import ViltImageProcessor
        from .processing_vilt import ViltProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vilt import (
            VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViltForImageAndTextRetrieval,
            ViltForImagesAndTextClassification,
            ViltForMaskedLM,
            ViltForQuestionAnswering,
            ViltForTokenClassification,
            ViltLayer,
            ViltModel,
            ViltPreTrainedModel,
        )


else:
    import sys

    A__   :						Any 							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure)



