





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Any					     =     ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

      _snake_case		:	Dict					     =     (
          ('''layer.''', '''layer_'''),
          ('''word_embeddings.weight''', '''word_embeddings'''),
          ('''position_embeddings.weight''', '''position_embeddings'''),
          ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
          ('''.''', '''/'''),
          ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
          ('''LayerNorm/bias''', '''LayerNorm/beta'''),
          ('''weight''', '''kernel'''),
      )

      if not os.path.isdir(lowerCAmelCase_	):
            os.makedirs(lowerCAmelCase_	)

      _snake_case		:	Tuple					     =     model.state_dict()

      def to_tf_var_name(lowerCAmelCase_	):
            for patt, repl in iter(lowerCAmelCase_	):
                  _snake_case		:	List[Any]					     =     name.replace(lowerCAmelCase_			,      lowerCAmelCase_	)
            return f'''bert/{name}'''

      def create_tf_var(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):
            _snake_case		:	Optional[int]					     =     tf.dtypes.as_dtype(tensor.dtype	)
            _snake_case		:	Any					     =     tf.get_variable(dtype=lowerCAmelCase_			,      shape=tensor.shape			,      name=lowerCAmelCase_			,      initializer=tf.zeros_initializer()	)
            session.run(tf.variables_initializer([tf_var]	)	)
            session.run(lowerCAmelCase_	)
            return tf_var

      tf.reset_default_graph()
      with tf.Session() as session:
            for var_name in state_dict:
                  _snake_case		:	Dict					     =     to_tf_var_name(lowerCAmelCase_	)
                  _snake_case		:	Tuple					     =     state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose	):
                        _snake_case		:	Any					     =     torch_tensor.T
                  _snake_case		:	Dict					     =     create_tf_var(tensor=lowerCAmelCase_			,      name=lowerCAmelCase_			,      session=lowerCAmelCase_	)
                  tf.keras.backend.set_value(lowerCAmelCase_			,      lowerCAmelCase_	)
                  _snake_case		:	Optional[Any]					     =     session.run(lowerCAmelCase_	)
                  print(f'''Successfully created {tf_name}: {np.allclose(lowerCAmelCase_			,      lowerCAmelCase_	)}'''	)

            _snake_case		:	Optional[Any]					     =     tf.train.Saver(tf.trainable_variables()	)
            saver.save(lowerCAmelCase_			,      os.path.join(lowerCAmelCase_			,      model_name.replace('''-'''			,      '''_'''	) + '''.ckpt'''	)	)




def    _a    (							lowerCAmelCase_=None	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     argparse.ArgumentParser()
      parser.add_argument('''--model_name'''			,      type=lowerCAmelCase_			,      required=lowerCAmelCase_			,      help='''model name e.g. bert-base-uncased'''	)
      parser.add_argument(
          '''--cache_dir'''			,      type=lowerCAmelCase_			,      default=lowerCAmelCase_			,      required=lowerCAmelCase_			,      help='''Directory containing pytorch model'''	)
      parser.add_argument('''--pytorch_model_path'''			,      type=lowerCAmelCase_			,      required=lowerCAmelCase_			,      help='''/path/to/<pytorch-model-name>.bin'''	)
      parser.add_argument('''--tf_cache_dir'''			,      type=lowerCAmelCase_			,      required=lowerCAmelCase_			,      help='''Directory in which to save tensorflow model'''	)
      _snake_case		:	Any					     =     parser.parse_args(lowerCAmelCase_	)

      _snake_case		:	Tuple					     =     BertModel.from_pretrained(
          pretrained_model_name_or_path=args.model_name			,      state_dict=torch.load(args.pytorch_model_path	)			,      cache_dir=args.cache_dir			,      )

      convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase_			,      ckpt_dir=args.tf_cache_dir			,      model_name=args.model_name	)


if __name__ == "__main__":
   main()









'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        super().__init__(
            lowercase__     ,     lowercase__     ,     tokenizer_file=lowercase__     ,     unk_token=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     add_prefix_space=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


