from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					'vilt'




 def __init__(			self					:  Optional[Any]			,    lowercase__					:  Dict=30_522			,    lowercase__					:  Tuple=2			,    lowercase__					:  int=2			,    lowercase__					:  Optional[int]=40			,    lowercase__					:  Optional[int]=768			,    lowercase__					:  Tuple=12			,    lowercase__					:  Tuple=12			,    lowercase__					:  Union[str, Any]=3_072			,    lowercase__					:  int="gelu"			,    lowercase__					:  Tuple=0.0			,    lowercase__					:  Dict=0.0			,    lowercase__					:  Optional[int]=0.02			,    lowercase__					:  Tuple=1e-12			,    lowercase__					:  Dict=384			,    lowercase__					:  int=32			,    lowercase__					:  List[str]=3			,    lowercase__					:  int=True			,    lowercase__					:  List[str]=-1			,    lowercase__					:  Union[str, Any]=False			,    lowercase__					:  List[str]=-1			,    **lowercase__					:  int			,    ):







        '''simple docstring'''



        super().__init__(tie_word_embeddings=lowercase__			,    **lowercase__)

        lowerCAmelCase__     	=					vocab_size
        lowerCAmelCase__     	=					type_vocab_size
        lowerCAmelCase__     	=					modality_type_vocab_size
        lowerCAmelCase__     	=					max_position_embeddings

        lowerCAmelCase__     	=					hidden_size
        lowerCAmelCase__     	=					num_hidden_layers
        lowerCAmelCase__     	=					num_attention_heads
        lowerCAmelCase__     	=					intermediate_size
        lowerCAmelCase__     	=					hidden_act
        lowerCAmelCase__     	=					hidden_dropout_prob
        lowerCAmelCase__     	=					attention_probs_dropout_prob
        lowerCAmelCase__     	=					initializer_range
        lowerCAmelCase__     	=					layer_norm_eps

        lowerCAmelCase__     	=					image_size
        lowerCAmelCase__     	=					patch_size
        lowerCAmelCase__     	=					num_channels
        lowerCAmelCase__     	=					qkv_bias
        lowerCAmelCase__     	=					max_image_length
        lowerCAmelCase__     	=					num_images

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)
