





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


_lowerCAmelCase						=	logging.get_logger(__name__)

_lowerCAmelCase						=	{'''vocab_file''': '''vocab.txt'''}

_lowerCAmelCase						=	{
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

_lowerCAmelCase						=	{
    '''tau/splinter-base''': 512,
    '''tau/splinter-base-qass''': 512,
    '''tau/splinter-large''': 512,
    '''tau/splinter-large-qass''': 512,
}

_lowerCAmelCase						=	{
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}


class 					__magic_name__  (							_SCREAMING_SNAKE_CASE							):





     """simple docstring"""



     __UpperCamelCase					    =     VOCAB_FILES_NAMES
     __UpperCamelCase					    =     PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase					    =     PRETRAINED_INIT_CONFIGURATION
     __UpperCamelCase					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase					    =     SplinterTokenizer



     def __init__(							self       :Union[str, Any]			,      snake_case       :Optional[int]=None			,      snake_case       :Optional[Any]=None			,      snake_case       :Union[str, Any]=True			,      snake_case       :Dict="[UNK]"			,      snake_case       :Union[str, Any]="[SEP]"			,      snake_case       :List[Any]="[PAD]"			,      snake_case       :int="[CLS]"			,      snake_case       :str="[MASK]"			,      snake_case       :Tuple="[QUESTION]"			,      snake_case       :int=True			,      snake_case       :int=None			,      **snake_case       :Optional[int]			,      ):





        '''simple docstring'''




        super().__init__(
            UpperCamelCase__			,      tokenizer_file=UpperCamelCase__			,      do_lower_case=UpperCamelCase__			,      unk_token=UpperCamelCase__			,      sep_token=UpperCamelCase__			,      pad_token=UpperCamelCase__			,      cls_token=UpperCamelCase__			,      mask_token=UpperCamelCase__			,      tokenize_chinese_chars=UpperCamelCase__			,      strip_accents=UpperCamelCase__			,      additional_special_tokens=(question_token,)			,      **UpperCamelCase__			,      )

        A_					:				Tuple          =			json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
        if (
            pre_tok_state.get("lowercase"			,      UpperCamelCase__  ) != do_lower_case
            or pre_tok_state.get("strip_accents"			,      UpperCamelCase__  ) != strip_accents
        ):
           A_					:				Optional[int]          =			getattr(UpperCamelCase__			,      pre_tok_state.pop("type"  )  )
           A_					:				int          =			do_lower_case
           A_					:				List[Any]          =			strip_accents
           A_					:				int          =			pre_tok_class(**UpperCamelCase__  )

        A_					:				Union[str, Any]          =			do_lower_case



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        return self.convert_tokens_to_ids(self.question_token  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :List[int]			,      snake_case       :Optional[List[int]] = None  ):





        '''simple docstring'''




        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        A_					:				Optional[Any]          =			[self.cls_token_id]
        A_					:				Union[str, Any]          =			[self.sep_token_id]
        A_					:				Optional[Any]          =			[self.question_token_id] + [self.convert_tokens_to_ids("."  )]
        if self.padding_side == "right":
           # Input is question-then-context
           return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
        else:
           # Input is context-then-question
           return cls + token_ids_a + sep + token_ids_a + question_suffix + sep



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]			,      snake_case       :List[int]			,      snake_case       :Optional[List[int]] = None  ):





        '''simple docstring'''




        A_					:				int          =			[self.sep_token_id]
        A_					:				List[Any]          =			[self.cls_token_id]
        A_					:				int          =			[self.question_token_id] + [self.convert_tokens_to_ids("."  )]
        if token_ids_a is None:
           return len(cls + token_ids_a + sep  ) * [0]

        if self.padding_side == "right":
           # Input is question-then-context
           return len(cls + token_ids_a + question_suffix + sep  ) * [0] + len(token_ids_a + sep  ) * [1]
        else:
           # Input is context-then-question
           return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + question_suffix + sep  ) * [1]





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :str			,      snake_case       :Optional[str] = None  ):





        '''simple docstring'''




        A_					:				int          =			self._tokenizer.model.save(UpperCamelCase__			,      name=UpperCamelCase__  )
        return tuple(UpperCamelCase__  )







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						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]				,				_lowerCAmelCase : List[Any]	) ->						Any:
   # Initialise PyTorch model
   A_					:				Tuple          =			FNetConfig.from_json_file(_lowerCAmelCase	)
   print(f"Building PyTorch model from configuration: {config}"	)
   A_					:				Tuple          =			FNetForPreTraining(_lowerCAmelCase	)

   A_					:				List[str]          =			restore_checkpoint(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Any          =			checkpoint_dict["target"]

   # Embeddings
   # Position IDs
   A_					:				Optional[Any]          =			fnet_pretraining_model.state_dict()

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

   # Encoder Layers
   for layer in range(config.num_hidden_layers	):
      A_					:				List[str]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["scale"]	)
      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["bias"]	)

      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
      A_					:				List[Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

      A_					:				Optional[int]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
      A_					:				int          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

      A_					:				Any          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]	)
      A_					:				Tuple          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]	)

   # Pooler Layers
   A_					:				Optional[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]	).T
   A_					:				List[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]	)

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

   # Seq Relationship Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]	)
   A_					:				str          =			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							:      Tuple						=	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							:      Optional[int]						=	parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
