


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






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)


