






import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
a		:	str      					=			logging.getLogger(__name__)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
		np.random.seed(args.seed		)
		torch.manual_seed(args.seed		)
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed		)






def     lowerCamelCase__				(   ):
		__UpperCAmelCase							:     Dict									=  argparse.ArgumentParser()
		parser.add_argument(
		    """--model_name_or_path"""  ,		default=SCREAMING_SNAKE_CASE_  ,		type=SCREAMING_SNAKE_CASE_  ,		required=SCREAMING_SNAKE_CASE_  ,		)
		parser.add_argument("""--prompt"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=""""""		)
		parser.add_argument("""--length"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=20		)
		parser.add_argument("""--stop_token"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=SCREAMING_SNAKE_CASE_  ,		help="""Token at which text generation is stopped"""		)
		parser.add_argument(
		    """--temperature"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=1.0  ,		help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""  ,		)
		parser.add_argument(
		    """--repetition_penalty"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=1.0  ,		help="""primarily useful for CTRL model; in that case, use 1.2"""		)
		parser.add_argument("""--k"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=0		)
		parser.add_argument("""--penalty_alpha"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=0.0		)
		parser.add_argument("""--p"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=0.9		)

		parser.add_argument("""--prefix"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=""""""  ,		help="""Text added prior to input."""		)
		parser.add_argument("""--padding_text"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=""""""  ,		help="""Deprecated, the use of `--prefix` is preferred."""		)
		parser.add_argument("""--xlm_language"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=""""""  ,		help="""Optional language when used with the XLM model."""		)

		parser.add_argument("""--seed"""  ,		type=SCREAMING_SNAKE_CASE_  ,		default=42  ,		help="""random seed for initialization"""		)
		parser.add_argument("""--no_cuda"""  ,		action="""store_true"""  ,		help="""Avoid using CUDA when available"""		)
		parser.add_argument(
		    """--fp16"""  ,		action="""store_true"""  ,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""  ,		)
		__UpperCAmelCase							:     Optional[int]									=  parser.parse_args()

		__UpperCAmelCase							:     str									=  torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""		)
		__UpperCAmelCase							:     Union[str, Any]									=  0 if args.no_cuda else torch.cuda.device_count()

		logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""		)

		set_seed(SCREAMING_SNAKE_CASE_		)

		# Initialize the model and tokenizer
		__UpperCAmelCase							:     str									=  AutoTokenizer.from_pretrained(args.model_name_or_path		)
		__UpperCAmelCase							:     Union[str, Any]									=  AutoModelForCausalLM.from_pretrained(args.model_name_or_path		)

		# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
		# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
		model.to(args.device		)

		if args.fpaa:
				model.half()

		logger.info(SCREAMING_SNAKE_CASE_		)
		__UpperCAmelCase							:     List[Any]									=  args.prompt if args.prompt else input("""Model prompt >>> """		)

		__UpperCAmelCase							:     Any									=  tokenizer(SCREAMING_SNAKE_CASE_  ,		return_tensors="""pt"""  ,		add_special_tokens=SCREAMING_SNAKE_CASE_		)
		__UpperCAmelCase							:     Any									=  {key: value.to(args.device		) for key, value in inputs.items()}

		__UpperCAmelCase							:     List[Any]									=  model.generate(
		    **SCREAMING_SNAKE_CASE_  ,		max_length=args.length + len(inputs["""input_ids"""][0]		)  ,		penalty_alpha=args.penalty_alpha  ,		top_k=args.k  ,		)

		__UpperCAmelCase							:     List[Any]									=  []
		for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE_		):
				print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""		)
				__UpperCAmelCase							:     Dict									=  generated_sequence.tolist()

				# Decode text
				__UpperCAmelCase							:     Dict									=  tokenizer.decode(SCREAMING_SNAKE_CASE_  ,		clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_  ,		add_special_tokens=SCREAMING_SNAKE_CASE_		)

				# Remove all text after the stop token
				__UpperCAmelCase							:     Dict									=  text[: text.find(args.stop_token		) if args.stop_token else None]

				# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
				__UpperCAmelCase							:     Optional[Any]									=  (
				    prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]  ,		clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_		)		) :]
				)

				generated_sequences.append(SCREAMING_SNAKE_CASE_		)
				print(SCREAMING_SNAKE_CASE_		)

		return generated_sequences


if __name__ == "__main__":
							main()










import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


a		:	Any      					=			logging.get_logger(__name__)

a		:	Dict      					=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a		:	Optional[int]      					=			{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}
class    a   (			lowercase__       ):



      """simple docstring"""


      def __init__(				self :      List[Any]		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      Tuple=None		,				__lowercase :      Optional[int]=False		,				__lowercase :      Any=True		,				__lowercase :      List[str]=False		,				__lowercase :      Union[str, Any]="<s>"		,				__lowercase :      List[str]="</s>"		,				__lowercase :      Dict="<unk>"		,				__lowercase :      List[str]="<sep>"		,				__lowercase :      int="<pad>"		,				__lowercase :      Any="<cls>"		,				__lowercase :      int="<mask>"		,				__lowercase :      Tuple=["<eop>", "<eod>"]		,				**__lowercase :      Union[str, Any]		,				)							->				Optional[Any]:
        __UpperCAmelCase							:     Any									=  AddedToken(__lowercase		,				lstrip=__lowercase		,				rstrip=__lowercase ) if isinstance(__lowercase		,				__lowercase ) else mask_token

        super().__init__(
            vocab_file=__lowercase		,				tokenizer_file=__lowercase		,				do_lower_case=__lowercase		,				remove_space=__lowercase		,				keep_accents=__lowercase		,				bos_token=__lowercase		,				eos_token=__lowercase		,				unk_token=__lowercase		,				sep_token=__lowercase		,				pad_token=__lowercase		,				cls_token=__lowercase		,				mask_token=__lowercase		,				additional_special_tokens=__lowercase		,				**__lowercase		,				)

        __UpperCAmelCase							:     Optional[int]									=  3
        __UpperCAmelCase							:     Dict									=  do_lower_case
        __UpperCAmelCase							:     Union[str, Any]									=  remove_space
        __UpperCAmelCase							:     Tuple									=  keep_accents
        __UpperCAmelCase							:     int									=  vocab_file
        __UpperCAmelCase							:     Dict									=  False if not self.vocab_file else True

        try:
          import jieba
        except ModuleNotFoundError as error:
          raise error.__class__(
              """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
              """See https://pypi.org/project/jieba/ for installation.""" )
        __UpperCAmelCase							:     Dict									=  jieba
        __UpperCAmelCase							:     str									=  str.maketrans(""" \n"""		,				"""\u2582\u2583""" )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     List[str]									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [self.cls_token_id]
        if token_ids_a is None:
          return token_ids_a + sep + cls
        return token_ids_a + sep + token_ids_a + sep + cls
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     Dict									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [2]

        if token_ids_a is None:
          return len(token_ids_a + sep ) * [0] + cls_segment_id
        return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      Optional[str] = None )							->				Tuple[str]:
        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(__lowercase ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
          return
        __UpperCAmelCase							:     List[str]									=  os.path.join(
            __lowercase		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

        return (out_vocab_file,)
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Any		,				*__lowercase :      Dict		,				**__lowercase :      int )							->				Optional[Any]:
        __UpperCAmelCase							:     List[str]									=  [
            """ """.join([x.translate(self.translator ) for x in self.jieba.cut(__lowercase		,				cut_all=__lowercase )] )
            for text in batch_text_or_text_pairs
        ]
        return super()._batch_encode_plus(__lowercase		,				*__lowercase		,				**__lowercase )




      def      UpperCAmelCase				(				self :      int		,				*__lowercase :      List[Any]		,				**__lowercase :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     List[str]									=  super()._decode(*__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     List[Any]									=  text.replace(""" """		,				"""""" ).replace("""\u2582"""		,				""" """ ).replace("""\u2583"""		,				"""\n""" )
        return text


