






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
	import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Union[str, Any]								={'vocab_file': 'vocab.txt'}

_UpperCamelCase			:	int								={
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

_UpperCamelCase			:	Dict								={
    'openbmb/cpm-ant-10b': 1024,
}



def  a__ (__lowercase							:Union[str, Any]   )		-> Optional[Any]:
				_A	:				Any   =      collections.OrderedDict()
				with open(__lowercase					,  '''r'''					,  encoding='''utf-8'''   ) as reader:
								_A	:				List[Any]   =      reader.readlines()
				for index, token in enumerate(__lowercase   ):
								_A	:				Union[str, Any]   =      token.rstrip('''\n'''   )
								_A	:				Union[str, Any]   =      index
				return vocab






class 						UpperCAmelCase__ (							__snake_case							):







		def __init__(					self       ,A__       ,A__="<unk>"       ,A__=200		):
						_A	:				Union[str, Any]   =      vocab
						_A	:				Any   =      unk_token
						_A	:				Tuple   =      max_input_chars_per_word







		def  A__      (					self       ,A__		):
						_A	:				Optional[int]   =      list(A__		)
						if len(A__		) > self.max_input_chars_per_word:
										return [self.unk_token]

						_A	:				int   =      0
						_A	:				str   =      []
						while start < len(A__		):
										_A	:				List[Any]   =      len(A__		)
										_A	:				List[Any]   =      None
										while start < end:
														_A	:				str   =      ''''''.join(chars[start:end]		)
														if substr in self.vocab:
																		_A	:				Dict   =      substr
																		break
														end -= 1
										if cur_substr is None:
														sub_tokens.append(self.unk_token		)
														start += 1
										else:
														sub_tokens.append(A__		)
														_A	:				Optional[int]   =      end

						return sub_tokens






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   VOCAB_FILES_NAMES
		__snake_case      :       Tuple     =   PRETRAINED_VOCAB_FILES_MAP
		__snake_case      :       Any     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__snake_case      :       List[Any]     =   ["input_ids", "attention_mask"]
		__snake_case      :       List[str]     =   False







		def __init__(					self       ,A__       ,A__="<d>"       ,A__="</d>"       ,A__="<s>"       ,A__="</s>"       ,A__="<pad>"       ,A__="<unk>"       ,A__="</n>"       ,A__="</_>"       ,A__="left"       ,**A__       ,):
						requires_backends(self       ,['''jieba''']		)
						super().__init__(
						    bod_token=A__       ,eod_token=A__       ,bos_token=A__       ,eos_token=A__       ,pad_token=A__       ,unk_token=A__       ,line_token=A__       ,space_token=A__       ,padding_side=A__       ,**A__       ,)
						_A	:				Optional[int]   =      bod_token
						_A	:				Dict   =      eod_token
						_A	:				Tuple   =      load_vocab(A__		)
						_A	:				Optional[int]   =      self.encoder[space_token]
						_A	:				List[str]   =      self.encoder[line_token]

						del self.encoder[space_token]
						del self.encoder[line_token]

						_A	:				Tuple   =      collections.OrderedDict(sorted(self.encoder.items()       ,key=lambda A__		: x[1]		)		)
						_A	:				Union[str, Any]   =      {v: k for k, v in self.encoder.items()}

						_A	:				Tuple   =      WordpieceTokenizer(vocab=self.encoder       ,unk_token=self.unk_token		)







		@property
		def  A__      (					self		):
						return self.encoder[self.bod_token]







		@property
		def  A__      (					self		):
						return self.encoder[self.eod_token]







		@property
		def  A__      (					self		):
						return self.encoder["\n"]







		@property
		def  A__      (					self		):
						return len(self.encoder		)







		def  A__      (					self		):
						return dict(self.encoder       ,**self.added_tokens_encoder		)







		def  A__      (					self       ,A__		):
						_A	:				Optional[int]   =      []
						for x in jieba.cut(A__       ,cut_all=A__		):
										output_tokens.extend(self.wordpiece_tokenizer.tokenize(A__		)		)
						return output_tokens







		def  A__      (					self       ,A__       ,**A__		):
						_A	:				Any   =      [i for i in token_ids if i >= 0]
						_A	:				str   =      [
						    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
						]
						return super()._decode(A__       ,**A__		)







		def  A__      (					self       ,A__		):
						return token in self.encoder







		def  A__      (					self       ,A__		):
						return "".join(A__		)







		def  A__      (					self       ,A__		):
						return self.encoder.get(A__       ,self.encoder.get(self.unk_token		)		)







		def  A__      (					self       ,A__		):
						return self.decoder.get(A__       ,self.unk_token		)







		def  A__      (					self       ,A__       ,A__ = None		):
						if os.path.isdir(A__		):
										_A	:				List[str]   =      os.path.join(
										    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
						else:
										_A	:				str   =      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
						_A	:				str   =      0
						if " " in self.encoder:
										_A	:				List[Any]   =      self.encoder[''' ''']
										del self.encoder[" "]
						if "\n" in self.encoder:
										_A	:				Optional[Any]   =      self.encoder['''\n''']
										del self.encoder["\n"]
						_A	:				Tuple   =      collections.OrderedDict(sorted(self.encoder.items()       ,key=lambda A__		: x[1]		)		)
						with open(A__       ,'''w'''       ,encoding='''utf-8'''		) as writer:
										for token, token_index in self.encoder.items():
														if index != token_index:
																		logger.warning(
																		    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																		    ''' Please check that the vocabulary is not corrupted!'''		)
																		_A	:				Optional[int]   =      token_index
														writer.write(token + '''\n'''		)
														index += 1
						return (vocab_file,)







		def  A__      (					self       ,A__       ,A__ = None		):
						if token_ids_a is None:
										return [self.bos_token_id] + token_ids_a
						return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = False		):

						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__		)

						if token_ids_a is not None:
										return [1] + ([0] * len(A__		)) + [1] + ([0] * len(A__		))
						return [1] + ([0] * len(A__		))











from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



