"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xglm import XGLMTokenizer
else:
 lowercase__   			=      None


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__   			=      {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

lowercase__   			=      {
    'facebook/xglm-564M': 2048,
}




class   __snake_case			(	A_							):
      a__								=						VOCAB_FILES_NAMES
      a__								=						PRETRAINED_VOCAB_FILES_MAP
      a__								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__								=						["input_ids", "attention_mask"]
      a__								=						XGLMTokenizer



      def __init__(		self       ,     lowercase=None       ,     lowercase=None       ,     lowercase="<s>"       ,     lowercase="</s>"       ,     lowercase="</s>"       ,     lowercase="<s>"       ,     lowercase="<unk>"       ,     lowercase="<pad>"       ,     **lowercase       ,     )      ->     Optional[int]:





        '''simple docstring'''





        a__:	List[Any]     				=      7
        a__:	List[Any]     				=      [f'<madeupword{i}>' for i in range(self.num_madeup_words)]

        a__:	Any     				=      kwargs.get('additional_special_tokens'       ,     [])
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            A_       ,     tokenizer_file=A_       ,     bos_token=A_       ,     eos_token=A_       ,     sep_token=A_       ,     cls_token=A_       ,     unk_token=A_       ,     pad_token=A_       ,     **A_       ,     )

        a__:	List[Any]     				=      vocab_file
        a__:	Optional[int]     				=      False if not self.vocab_file else True



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return [self.sep_token_id] + token_ids_a
        a__:	Any     				=      [self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     List[int]:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      [self.sep_token_id]

        if token_ids_a is None:
          return len(sep + token_ids_a) * [0]
        return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     Tuple[str]:





        '''simple docstring'''





        if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.')

        if not os.path.isdir(A_):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
          return
        a__:	int     				=      os.path.join(
            A_       ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

        if os.path.abspath(self.vocab_file) != os.path.abspath(A_):
          copyfile(self.vocab_file       ,     A_)

        return (out_vocab_file,)






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		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 mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





