




'''simple docstring'''







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

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


_lowerCamelCase							:						str								=  logging.get_logger(__name__)

_lowerCamelCase							:						List[str]								=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase							:						Any								=  {
    '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      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase=False,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase="<s>",  __UpperCamelCase="</s>",  __UpperCamelCase="<unk>",  __UpperCamelCase="<sep>",  __UpperCamelCase="<pad>",  __UpperCamelCase="<cls>",  __UpperCamelCase="<mask>",  __UpperCamelCase=["<eop>", "<eod>"],  **__UpperCamelCase,  ):
							_snake_case	       =       AddedToken(__UpperCamelCase,  lstrip=__UpperCamelCase,  rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase,  __UpperCamelCase ) else mask_token

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

							_snake_case	       =       3
							_snake_case	       =       do_lower_case
							_snake_case	       =       remove_space
							_snake_case	       =       keep_accents
							_snake_case	       =       vocab_file
							_snake_case	       =       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." )
							_snake_case	       =       jieba
							_snake_case	       =       str.maketrans(" \n",  "\u2582\u2583" )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_snake_case	       =       [self.sep_token_id]
							_snake_case	       =       [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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_snake_case	       =       [self.sep_token_id]
							_snake_case	       =       [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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							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(__UpperCamelCase ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
							_snake_case	       =       os.path.join(
							    __UpperCamelCase,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

							return (out_vocab_file,)






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  *__UpperCamelCase,  **__UpperCamelCase ):
							_snake_case	       =       [
							    " ".join([x.translate(self.translator ) for x in self.jieba.cut(__UpperCamelCase,  cut_all=__UpperCamelCase )] )
							    for text in batch_text_or_text_pairs
							]
							return super()._batch_encode_plus(__UpperCamelCase,  *__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							_snake_case	       =       super()._decode(*__UpperCamelCase,  **__UpperCamelCase )
							_snake_case	       =       text.replace(" ",  "" ).replace("\u2582",  " " ).replace("\u2583",  "\n" )
							return text













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,
)
snake_case			 =						logging.getLogger(__name__)


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






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


def      lowerCamelCase__	(   ):

  """simple docstring"""






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

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

  parser.add_argument("--seed"  ,   type=lowercase  ,   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"  ,   )
  SCREAMING_SNAKE_CASE				:					str		  =			parser.parse_args()

  SCREAMING_SNAKE_CASE				:					Dict		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			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(lowercase )

  # Initialize the model and tokenizer
  SCREAMING_SNAKE_CASE				:					Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
  SCREAMING_SNAKE_CASE				:					int		  =			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(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			args.prompt if args.prompt else input("Model prompt >>> " )

  SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(lowercase  ,   return_tensors="pt"  ,   add_special_tokens=lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			{key: value.to(args.device ) for key, value in inputs.items()}

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

  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for generated_sequence_idx, generated_sequence in enumerate(lowercase ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			generated_sequence.tolist()

    # Decode text
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tokenizer.decode(lowercase  ,   clean_up_tokenization_spaces=lowercase  ,   add_special_tokens=lowercase )

    # Remove all text after the stop token
    SCREAMING_SNAKE_CASE				:					List[str]		  =			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
    SCREAMING_SNAKE_CASE				:					str		  =			(
        prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]  ,   clean_up_tokenization_spaces=lowercase ) ) :]
    )

    generated_sequences.append(lowercase )
    print(lowercase )

  return generated_sequences


if __name__ == "__main__":
   main()






