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 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Any:




       '''simple docstring'''






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

       _A=						restore_checkpoint(lowerCAmelCase_							,       lowerCAmelCase_ )
       _A=						checkpoint_dict['target']

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

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

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

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

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

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

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

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

       # Seq Relationship Layers
       _A=						torch.tensor(
           pretrained_model_params['classification']['output_kernel'] )
       _A=						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)
import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


UpperCAmelCase_    		=				collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    CheckpointState:




       '''simple docstring'''






       print(F"Reading saved model from {model_file}" )
       _A=						torch.load(lowerCAmelCase_							,       map_location=lambda lowerCAmelCase_							,       lowerCAmelCase_ : default_restore_location(lowerCAmelCase_							,       'cpu' ) )
       return CheckpointState(**lowerCAmelCase_ )





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__							):
            _A=						src_file



     def       a__	(       self							):
            raise NotImplementedError





     @staticmethod
     def       a__	(       lowerCAmelCase__					,   *lowerCAmelCase__					,   **lowerCAmelCase__							):
            if comp_type.startswith('c'							):
                   return DPRContextEncoderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            if comp_type.startswith('q'							):
                   return DPRQuestionEncoderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            if comp_type.startswith('r'							):
                   return DPRReaderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            else:
                   raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'							)





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.ctx_encoder, 'ctx_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
            for key, value in saved_state.model_dict.items():
                   if key.startswith(lowerCAmelCase__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.question_encoder, 'question_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
            for key, value in saved_state.model_dict.items():
                   if key.startswith(lowerCAmelCase__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR reader from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{
                'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
            }
            for key, value in saved_state.model_dict.items():
                   if key.startswith('encoder.'							) and not key.startswith('encoder.encode_proj'							):
                          _A=						'encoder.bert_model.' + key[len('encoder.'							) :]
                   _A=						value
            model.span_predictor.load_state_dict(lowerCAmelCase__							)
            return model


def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       _A=						Path(lowerCAmelCase_ )
       dest_dir.mkdir(exist_ok=lowerCAmelCase_ )

       _A=						DPRState.from_type(lowerCAmelCase_							,       src_file=lowerCAmelCase_ )
       _A=						dpr_state.load_dpr_model()
       model.save_pretrained(lowerCAmelCase_ )
       model.from_pretrained(lowerCAmelCase_ )  # sanity check


if __name__ == "__main__":
      UpperCAmelCase_    		=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
      )
      parser.add_argument(
          '''--src''',
          type=str,
          help=(
              '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
              ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
              ''' \'retriever\' checkpoints.'''
          ),
      )
      parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
      UpperCAmelCase_    		=				parser.parse_args()

      UpperCAmelCase_    		=				Path(args.src)
      UpperCAmelCase_    		=				F"converted-{src_file.name}" if args.dest is None else args.dest
      UpperCAmelCase_    		=				Path(dest_dir)
      assert src_file.exists()
      assert (
          args.type is not None
      ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
      convert(args.type, src_file, dest_dir)