code: 
  
  
  import json
  import os
  from functools import lru_cache
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  
  import numpy as np
  import regex as re
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  if TYPE_CHECKING:
      if is_torch_available():
          import torch
      if is_tf_available():
          import tensorflow as tf
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  
  
  __snake_case             =	logging.get_logger(__name__)
  
  __snake_case             =	{
      '''vocab_file''': '''vocab.json''',
      '''merges_file''': '''merges.txt''',
  }
  
  __snake_case             =	{
      '''vocab_file''': {
          '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
      },
      '''merges_file''': {
          '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
      },
  }
  
  __snake_case             =	{
      '''Salesforce/codegen-350M-mono''': 2_0_4_8,
  }
  
  
  
  
  
  
  @lru_cache()
  def 		_A		(		)				->   Dict:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = (
           list(range(ord('!'   )				,     ord('~'   ) + 1   )   ) + list(range(ord('¡'   )				,     ord('¬'   ) + 1   )   ) + list(range(ord('®'   )				,     ord('ÿ'   ) + 1   )   )
       )
       __UpperCamelCase   = bs[:]
       __UpperCamelCase   = 0
       for b in range(2**8   ):
            if b not in bs:
                 bs.append(_lowercase   )
                 cs.append(2**8 + n   )
                 n += 1
       __UpperCamelCase   = [chr(_lowercase   ) for n in cs]
       return dict(zip(_lowercase				,     _lowercase   )   )
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   Union[str, Any]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = set()
       __UpperCamelCase   = word[0]
       for char in word[1:]:
            pairs.add((prev_char, char)   )
            __UpperCamelCase   = char
       return pairs
  
  
  
  class   __lowerCamelCase (_a	):
         _lowercase       							=    VOCAB_FILES_NAMES
         _lowercase       							=    PRETRAINED_VOCAB_FILES_MAP
         _lowercase       							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         _lowercase       							=    ["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
  
         def __init__( self:   Dict,A_:   Union[str, Any],A_:   Union[str, Any],A_:   Dict="replace",A_:   Tuple="<|endoftext|>",A_:   Optional[int]="<|endoftext|>",A_:   Dict="<|endoftext|>",A_:   Any=None,A_:   str=False,A_:   Union[str, Any]=False,**A_:   int,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else bos_token
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else eos_token
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else unk_token
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else pad_token
              super().__init__(
                  errors=A_,unk_token=A_,bos_token=A_,eos_token=A_,pad_token=A_,add_prefix_space=A_,add_bos_token=A_,**A_,)
              __UpperCamelCase   = add_bos_token
  
              with open(A_,encoding='utf-8'				) as vocab_handle:
                   __UpperCamelCase   = json.load(A_				)
              __UpperCamelCase   = {v: k for k, v in self.encoder.items()}
              __UpperCamelCase   = errors  # how to handle errors in decoding
              __UpperCamelCase   = bytes_to_unicode()
              __UpperCamelCase   = {v: k for k, v in self.byte_encoder.items()}
              with open(A_,encoding='utf-8'				) as merges_handle:
                   __UpperCamelCase   = merges_handle.read().split('\n'				)[1:-1]
              __UpperCamelCase   = [tuple(merge.split()				) for merge in bpe_merges]
              __UpperCamelCase   = dict(zip(A_,range(len(A_				)				)				)				)
              __UpperCamelCase   = {}
              __UpperCamelCase   = add_prefix_space
  
              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              __UpperCamelCase   = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'				)
  
  
  
  
  
  
  
         @property
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return len(self.encoder				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return dict(self.encoder,**self.added_tokens_encoder				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   str,A_:   int				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if token in self.cache:
                   return self.cache[token]
              __UpperCamelCase   = tuple(A_				)
              __UpperCamelCase   = get_pairs(A_				)
  
              if not pairs:
                   return token
  
              while True:
                   __UpperCamelCase   = min(A_,key=lambda A_				: self.bpe_ranks.get(A_,float('inf'				)				)				)
                   if bigram not in self.bpe_ranks:
                        break
                   __UpperCamelCase,						__UpperCamelCase   = bigram
                   __UpperCamelCase   = []
                   __UpperCamelCase   = 0
                   while i < len(A_				):
                        try:
                             __UpperCamelCase   = word.index(A_,A_				)
                        except ValueError:
                             new_word.extend(word[i:]				)
                             break
                        else:
                             new_word.extend(word[i:j]				)
                             __UpperCamelCase   = j
  
                        if word[i] == first and i < len(A_				) - 1 and word[i + 1] == second:
                             new_word.append(first + second				)
                             i += 2
                        else:
                             new_word.append(word[i]				)
                             i += 1
                   __UpperCamelCase   = tuple(A_				)
                   __UpperCamelCase   = new_word
                   if len(A_				) == 1:
                        break
                   else:
                        __UpperCamelCase   = get_pairs(A_				)
              __UpperCamelCase   = ' '.join(A_				)
              __UpperCamelCase   = word
              return word
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   Optional[int],A_:   Union[str, Any]=None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if self.add_bos_token:
                   __UpperCamelCase   = [self.bos_token_id]
              else:
                   __UpperCamelCase   = []
  
              __UpperCamelCase   = bos_token_ids + token_ids_a
  
              if token_ids_a is None:
                   return output
  
              return output + bos_token_ids + token_ids_a
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = []
              for token in re.findall(self.pat,A_				):
                   __UpperCamelCase   = ''.join(
                       self.byte_encoder[b] for b in token.encode('utf-8'				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                   bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_				).split(' '				)				)
              return bpe_tokens
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any,A_:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return self.encoder.get(A_,self.encoder.get(self.unk_token				)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any,A_:   int				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return self.decoder.get(A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ''.join(A_				)
              __UpperCamelCase   = bytearray([self.byte_decoder[c] for c in text]				).decode('utf-8',errors=self.errors				)
              return text
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   str,A_:   Optional[str] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if not os.path.isdir(A_				):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                   return
              __UpperCamelCase   = os.path.join(
                  A_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
              __UpperCamelCase   = os.path.join(
                  A_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']				)
  
              with open(A_,'w',encoding='utf-8'				) as f:
                   f.write(json.dumps(self.encoder,indent=2,sort_keys=A_,ensure_ascii=A_				) + '\n'				)
  
              __UpperCamelCase   = 0
              with open(A_,'w',encoding='utf-8'				) as writer:
                   writer.write('#version: 0.2\n'				)
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),key=lambda A_				: kv[1]				):
                        if index != token_index:
                             logger.warning(
                                 F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                 ' Please check that the tokenizer is not corrupted!'				)
                             __UpperCamelCase   = token_index
                        writer.write(' '.join(A_				) + '\n'				)
                        index += 1
  
              return vocab_file, merge_file
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   Union[str, Any],A_:   List[Any]=False,**A_:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = kwargs.pop('add_prefix_space',self.add_prefix_space				)
              if is_split_into_words or add_prefix_space:
                   __UpperCamelCase   = ' ' + text
              return (text, kwargs)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],A_:   bool = False,A_:   bool = None,A_:   Optional[List[str]] = None,**A_:   int,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = super()._decode(
                  token_ids=A_,skip_special_tokens=A_,clean_up_tokenization_spaces=A_,**A_,)
  
              if truncate_before_pattern is not None and len(A_				) > 0:
                   __UpperCamelCase   = self.truncate(A_,A_				)
  
              return decoded_text
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   List[Any],A_:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              def find_re(A_:   Dict,A_:   Optional[int],A_:   Tuple				):
                   __UpperCamelCase   = pattern.search(A_,A_				)
                   return m.start() if m else -1
  
              __UpperCamelCase   = [re.compile(A_,re.MULTILINE				) for pattern in truncate_before_pattern]
  
              __UpperCamelCase   = list(re.finditer('^print',A_,re.MULTILINE				)				)
  
              if len(A_				) > 1:
                   __UpperCamelCase   = completion[: prints[1].start()]
  
              __UpperCamelCase   = list(re.finditer('^def',A_,re.MULTILINE				)				)
  
              if len(A_				) > 1:
                   __UpperCamelCase   = completion[: defs[1].start()]
  
              __UpperCamelCase   = 0
  
              __UpperCamelCase   = [
                  pos for pos in [find_re(A_,A_,A_				) for terminal in terminals] if pos != -1
              ]
  
              if len(A_				) > 0:
                   return completion[: min(A_				)]
              else:
                   return completion
  
  
  
style_context: 
  
  
  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 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = json.load(open(_lowercase				,     '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(_lowercase   )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )
  
       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
       model.save_pretrained(_lowercase   )
  
  
  if __name__ == "__main__":
      __snake_case             =	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.'''
      )
      __snake_case             =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  