




import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_  ):
      # Initialise PyTorch model
      _A							:  str    =		FunnelConfig.from_json_file(snake_case_  )
      print(f'''Building PyTorch model from configuration: {config}'''  )
      _A							:  int    =		FunnelBaseModel(snake_case_  ) if base_model else FunnelModel(snake_case_  )

      # Load weights from tf checkpoint
      load_tf_weights_in_funnel(snake_case_,snake_case_,snake_case_  )

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''  )
      torch.save(model.state_dict(),snake_case_  )


if __name__ == "__main__":
  _snake_case     :   Any            = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
  )
  _snake_case     :   Any            = parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
  )







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

