
from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_         =  {
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}
class        _UpperCAmelCase							(						snake_case__							):
      __SCREAMING_SNAKE_CASE				:							int							 =		MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
      __SCREAMING_SNAKE_CASE				:							int							 =		"mobilebert"




      def __init__(							self				,    lowercase_=3_0_5_2_2				,    lowercase_=5_1_2				,    lowercase_=2_4				,    lowercase_=4				,    lowercase_=5_1_2				,    lowercase_="relu"				,    lowercase_=0.0				,    lowercase_=0.1				,    lowercase_=5_1_2				,    lowercase_=2				,    lowercase_=0.0_2				,    lowercase_=1E-12				,    lowercase_=0				,    lowercase_=1_2_8				,    lowercase_=True				,    lowercase_=True				,    lowercase_=1_2_8				,    lowercase_=False				,    lowercase_=True				,    lowercase_=4				,    lowercase_="no_norm"				,    lowercase_=True				,    lowercase_=None				,    **lowercase_				,    )  ->   Optional[int]:
          super().__init__(pad_token_id=lowercase_				,    **lowercase_							)

          UpperCAmelCase       =   vocab_size
          UpperCAmelCase       =   hidden_size
          UpperCAmelCase       =   num_hidden_layers
          UpperCAmelCase       =   num_attention_heads
          UpperCAmelCase       =   hidden_act
          UpperCAmelCase       =   intermediate_size
          UpperCAmelCase       =   hidden_dropout_prob
          UpperCAmelCase       =   attention_probs_dropout_prob
          UpperCAmelCase       =   max_position_embeddings
          UpperCAmelCase       =   type_vocab_size
          UpperCAmelCase       =   initializer_range
          UpperCAmelCase       =   layer_norm_eps
          UpperCAmelCase       =   embedding_size
          UpperCAmelCase       =   trigram_input
          UpperCAmelCase       =   use_bottleneck
          UpperCAmelCase       =   intra_bottleneck_size
          UpperCAmelCase       =   use_bottleneck_attention
          UpperCAmelCase       =   key_query_shared_bottleneck
          UpperCAmelCase       =   num_feedforward_networks
          UpperCAmelCase       =   normalization_type
          UpperCAmelCase       =   classifier_activation

          if self.use_bottleneck:
              UpperCAmelCase       =   intra_bottleneck_size
          else:
              UpperCAmelCase       =   hidden_size

          UpperCAmelCase       =   classifier_dropout

class        _UpperCAmelCase							(						snake_case__							):




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





"""simple docstring"""

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       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

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

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

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

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

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

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

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

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

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



