from typing import TYPE_CHECKING

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


lowerCAmelCase__							= {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= ['CodeGenTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
        'CodeGenForCausalLM',
        'CodeGenModel',
        'CodeGenPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
    from .tokenization_codegen import CodeGenTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_codegen_fast import CodeGenTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_codegen import (
            CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
            CodeGenForCausalLM,
            CodeGenModel,
            CodeGenPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

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       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       # Initialise PyTorch model
       lowerCAmelCase__     	=					FNetConfig.from_json_file(lowerCAmelCase__	)
       print(F"""Building PyTorch model from configuration: {config}"""	)
       lowerCAmelCase__     	=					FNetForPreTraining(lowerCAmelCase__	)

       lowerCAmelCase__     	=					restore_checkpoint(lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					checkpoint_dict['target']

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

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

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

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

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

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

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

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

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