



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  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[int]					   =		FNetConfig.from_json_file(__snake_case     )
      print(F"""Building PyTorch model from configuration: {config}"""     )
      UpperCAmelCase_	:						Optional[int]					   =		FNetForPreTraining(__snake_case     )

      UpperCAmelCase_	:						Union[str, Any]					   =		restore_checkpoint(__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						int					   =		checkpoint_dict["target"]

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

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

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

            UpperCAmelCase_	:						Optional[Any]					   =		torch.tensor(
                pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["intermediate"]["kernel"]     ).T
            UpperCAmelCase_	:						int					   =		torch.tensor(
                pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["intermediate"]["bias"]     )

            UpperCAmelCase_	:						int					   =		torch.tensor(
                pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["output"]["kernel"]     ).T
            UpperCAmelCase_	:						List[Any]					   =		torch.tensor(
                pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["output"]["bias"]     )

            UpperCAmelCase_	:						str					   =		torch.tensor(
                pretrained_model_params["encoder"][F"""encoder_{layer}"""]["output_layer_norm"]["scale"]     )
            UpperCAmelCase_	:						Dict					   =		torch.tensor(
                pretrained_model_params["encoder"][F"""encoder_{layer}"""]["output_layer_norm"]["bias"]     )

      # Pooler Layers
      UpperCAmelCase_	:						Tuple					   =		torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]     ).T
      UpperCAmelCase_	:						Union[str, Any]					   =		torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]     )

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

      # Seq Relationship Layers
      UpperCAmelCase_	:						str					   =		torch.tensor(
          pretrained_model_params["classification"]["output_kernel"]     )
      UpperCAmelCase_	:						List[str]					   =		torch.tensor(
          pretrained_model_params["classification"]["output_bias"]     )

      # Load State Dict
      fnet_pretraining_model.load_state_dict(__snake_case     )

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


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)





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


__lowerCamelCase								=      logging.get_logger(__name__)

__lowerCamelCase								=      {
    '''MCG-NJU/videomae-base''': '''https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json''',
}


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       _lowerCamelCase           =					"""videomae"""



       def __init__(  self  ,lowercase=224  ,lowercase=16  ,lowercase=3  ,lowercase=16  ,lowercase=2  ,lowercase=768  ,lowercase=12  ,lowercase=12  ,lowercase=3072  ,lowercase="gelu"  ,lowercase=0.0  ,lowercase=0.0  ,lowercase=0.02  ,lowercase=1E-12  ,lowercase=True  ,lowercase=True  ,lowercase=6  ,lowercase=384  ,lowercase=4  ,lowercase=1536  ,lowercase=True  ,**lowercase  ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_	:						Optional[int]					   =		image_size
             UpperCAmelCase_	:						Optional[Any]					   =		patch_size
             UpperCAmelCase_	:						Tuple					   =		num_channels
             UpperCAmelCase_	:						Optional[int]					   =		num_frames
             UpperCAmelCase_	:						str					   =		tubelet_size

             UpperCAmelCase_	:						Any					   =		hidden_size
             UpperCAmelCase_	:						List[str]					   =		num_hidden_layers
             UpperCAmelCase_	:						List[str]					   =		num_attention_heads
             UpperCAmelCase_	:						Union[str, Any]					   =		intermediate_size
             UpperCAmelCase_	:						List[str]					   =		hidden_act
             UpperCAmelCase_	:						Tuple					   =		hidden_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		attention_probs_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		initializer_range
             UpperCAmelCase_	:						Dict					   =		layer_norm_eps
             UpperCAmelCase_	:						Tuple					   =		qkv_bias
             UpperCAmelCase_	:						int					   =		use_mean_pooling

             UpperCAmelCase_	:						List[Any]					   =		decoder_num_attention_heads
             UpperCAmelCase_	:						Optional[int]					   =		decoder_hidden_size
             UpperCAmelCase_	:						Any					   =		decoder_num_hidden_layers
             UpperCAmelCase_	:						Dict					   =		decoder_intermediate_size
             UpperCAmelCase_	:						str					   =		norm_pix_loss
