




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 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Optional[Any]:
       lowerCamelCase       :				Any      			= FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE   )
       print(f'''Building PyTorch model from configuration: {config}'''   )
       lowerCamelCase       :				List[Any]      			= FNetForPreTraining(_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				str      			= restore_checkpoint(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Optional[Any]      			= checkpoint_dict["target"]

       # Embeddings
       # Position IDs
       lowerCamelCase       :				Tuple      			= fnet_pretraining_model.state_dict()

       lowerCamelCase       :				Dict      			= state_dict["fnet.embeddings.position_ids"]
       lowerCamelCase       :				Tuple      			= {"fnet.embeddings.position_ids": position_ids}
       # Embedding Layers
       lowerCamelCase       :				Tuple      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]   )
       lowerCamelCase       :				Optional[int]      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]   )
       lowerCamelCase       :				Optional[int]      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]   )
       lowerCamelCase       :				str      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]   ).T
       lowerCamelCase       :				Tuple      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]   )
       lowerCamelCase       :				Union[str, Any]      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]   )
       lowerCamelCase       :				Optional[Any]      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]   )

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

              lowerCamelCase       :				Dict      			= 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       :				str      			= torch.tensor(
                  pretrained_model_params["encoder"][f'''feed_forward_{layer}''']["output"]["kernel"]   ).T
              lowerCamelCase       :				Optional[int]      			= torch.tensor(
                  pretrained_model_params["encoder"][f'''feed_forward_{layer}''']["output"]["bias"]   )

              lowerCamelCase       :				Optional[Any]      			= torch.tensor(
                  pretrained_model_params["encoder"][f'''encoder_{layer}''']["output_layer_norm"]["scale"]   )
              lowerCamelCase       :				Dict      			= torch.tensor(
                  pretrained_model_params["encoder"][f'''encoder_{layer}''']["output_layer_norm"]["bias"]   )

       # Pooler Layers
       lowerCamelCase       :				Optional[Any]      			= torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]   ).T
       lowerCamelCase       :				Optional[int]      			= torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]   )

       # Masked LM Layers
       lowerCamelCase       :				Any      			= torch.tensor(
           pretrained_model_params["predictions_dense"]["kernel"]   ).T
       lowerCamelCase       :				Optional[Any]      			= torch.tensor(
           pretrained_model_params["predictions_dense"]["bias"]   )
       lowerCamelCase       :				Optional[int]      			= torch.tensor(
           pretrained_model_params["predictions_layer_norm"]["scale"]   )
       lowerCamelCase       :				Optional[int]      			= torch.tensor(
           pretrained_model_params["predictions_layer_norm"]["bias"]   )
       lowerCamelCase       :				Dict      			= torch.tensor(
           pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]   )
       lowerCamelCase       :				Tuple      			= torch.tensor(
           pretrained_model_params["predictions_output"]["output_bias"]   )
       lowerCamelCase       :				Optional[Any]      			= torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]   )

       # Seq Relationship Layers
       lowerCamelCase       :				str      			= torch.tensor(
           pretrained_model_params["classification"]["output_kernel"]   )
       lowerCamelCase       :				Optional[int]      			= torch.tensor(
           pretrained_model_params["classification"]["output_bias"]   )

       # Load State Dict
       fnet_pretraining_model.load_state_dict(_SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__	:				Optional[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.')
       SCREAMING_SNAKE_CASE__	:				str  =						parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       {
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       SCREAMING_SNAKE_CASE__	:				List[str]  =       [
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






