


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE				:						Optional[Any]			   = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:						List[Any]			   = {
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}






class    UpperCamelCase    (						UpperCamelCase__   ):


   '''simple docstring'''







   lowercase    :		Optional[Any]	  ="""mega"""
   def __init__(   self      ,							UpperCamelCase_=3_0522      ,							UpperCamelCase_=128      ,							UpperCamelCase_=4      ,							UpperCamelCase_=256      ,							UpperCamelCase_=16      ,							UpperCamelCase_=True      ,							UpperCamelCase_=64      ,							UpperCamelCase_=False      ,							UpperCamelCase_=-1      ,							UpperCamelCase_=None      ,							UpperCamelCase_=True      ,							UpperCamelCase_="scalenorm"      ,							UpperCamelCase_=True      ,							UpperCamelCase_="silu"      ,							UpperCamelCase_="softmax"      ,							UpperCamelCase_=0.1      ,							UpperCamelCase_=0.1      ,							UpperCamelCase_=0.1      ,							UpperCamelCase_=False      ,							UpperCamelCase_=True      ,							UpperCamelCase_=256      ,							UpperCamelCase_=True      ,							UpperCamelCase_=0.1      ,							UpperCamelCase_=2048      ,							UpperCamelCase_=False      ,							UpperCamelCase_=2      ,							UpperCamelCase_=0.02      ,							UpperCamelCase_=0.2      ,							UpperCamelCase_=0.02      ,							UpperCamelCase_=1.0      ,							UpperCamelCase_=1      ,							UpperCamelCase_=0      ,							UpperCamelCase_=2      ,							UpperCamelCase_="rotary"      ,							UpperCamelCase_=None      ,							UpperCamelCase_=True      ,							UpperCamelCase_=True      ,							**UpperCamelCase_      ,							):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__      ,							bos_token_id=SCREAMING_SNAKE_CASE__      ,							eos_token_id=SCREAMING_SNAKE_CASE__      ,							**SCREAMING_SNAKE_CASE__						)

        lowercase_				:Tuple       			=				vocab_size
        lowercase_				:Union[str, Any]       			=				hidden_size
        lowercase_				:Union[str, Any]       			=				num_hidden_layers
        lowercase_				:List[str]       			=				activation
        lowercase_				:int       			=				attention_activation
        lowercase_				:Union[str, Any]       			=				intermediate_size
        lowercase_				:Optional[int]       			=				ema_projection_size
        lowercase_				:int       			=				bidirectional
        lowercase_				:Dict       			=				shared_representation_size
        lowercase_				:Tuple       			=				use_chunking
        lowercase_				:Optional[int]       			=				chunk_size
        lowercase_				:Dict       			=				truncation
        lowercase_				:List[str]       			=				normalize_before_mega
        lowercase_				:Tuple       			=				normalization_type
        lowercase_				:List[Any]       			=				norm_affine
        lowercase_				:Tuple       			=				dropout_prob
        lowercase_				:Any       			=				hidden_dropout_prob
        lowercase_				:Dict       			=				attention_probs_dropout_prob
        lowercase_				:Optional[Any]       			=				use_feature_dropout
        lowercase_				:Any       			=				use_normalized_ffn
        lowercase_				:Tuple       			=				nffn_hidden_size
        lowercase_				:str       			=				normalize_before_ffn
        lowercase_				:str       			=				nffn_activation_dropout_prob
        lowercase_				:Optional[int]       			=				max_positions
        lowercase_				:Optional[int]       			=				add_token_type_embeddings
        lowercase_				:str       			=				type_vocab_size
        lowercase_				:Optional[int]       			=				initializer_range
        lowercase_				:int       			=				ema_delta_alpha_range
        lowercase_				:Optional[Any]       			=				ema_beta_range
        lowercase_				:Tuple       			=				ema_gamma_omega_range
        lowercase_				:Optional[int]       			=				relative_positional_bias
        lowercase_				:int       			=				use_cache
        lowercase_				:List[str]       			=				classifier_dropout
        lowercase_				:Dict       			=				add_lm_hidden_dense_layer
        lowercase_				:int       			=				1  # not used but required by Hugging Face






class    UpperCamelCase    (						UpperCamelCase__   ):


   '''simple docstring'''







   @property
   def 						UpperCamelCase     (   self						):
        if self.task == "multiple-choice":
             lowercase_				:Any       			=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
             lowercase_				:Dict       			=				{0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]						)







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   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     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__":
     UpperCamelCase								=     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.')
     UpperCamelCase								=     parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
