code: 
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  __a   				=      logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
        A				:							int    							=     ['input_ids', 'attention_mask']
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__="[PAD]"							,						SCREAMING_SNAKE_CASE__="[BOS]"							,						SCREAMING_SNAKE_CASE__="[EOS]"							,						SCREAMING_SNAKE_CASE__="[MASK]"							,						SCREAMING_SNAKE_CASE__="[CLS]"							,						SCREAMING_SNAKE_CASE__="[SEP]"							,						SCREAMING_SNAKE_CASE__=2048							,						**SCREAMING_SNAKE_CASE__							,						):
              lowercase			:   Union[str, Any]					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else pad_token
              lowercase			:   List[str]					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else bos_token
              lowercase			:   List[str]					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else eos_token
              lowercase			:   Union[str, Any]					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else mask_token
              lowercase			:   Dict					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else cls_token
              lowercase			:   Union[str, Any]					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else sep_token
  
              super().__init__(
                  pad_token=SCREAMING_SNAKE_CASE__							,						bos_token=SCREAMING_SNAKE_CASE__							,						eos_token=SCREAMING_SNAKE_CASE__							,						mask_token=SCREAMING_SNAKE_CASE__							,						cls_token=SCREAMING_SNAKE_CASE__							,						sep_token=SCREAMING_SNAKE_CASE__							,						model_max_length=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__							,						)
  
              lowercase			:   List[Any]					   =		2**8  # utf is 8 bits
  
              # define special tokens dict
              lowercase			:   Dict[str, int]					   =		{
                  self.pad_token: 0,
                  self.bos_token: 1,
                  self.eos_token: 2,
                  self.mask_token: 3,
                  self.cls_token: 4,
                  self.sep_token: 5,
              }
              lowercase			:   List[str]					   =		len(self.special_tokens_encoder		)
              lowercase			:   Dict[int, str]					   =		{v: k for k, v in self.special_tokens_encoder.items()}
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   List[Any]					   =		self.special_tokens_encoder.copy()
              vocab.update(self.added_tokens_encoder		)
              for i in range(self._utf_vocab_size		):
                    lowercase			:   List[Any]					   =		chr(SCREAMING_SNAKE_CASE__		)
                    lowercase			:   Optional[Any]					   =		i + len(self.special_tokens_encoder		)
              return vocab
  
  
        @property
        def        __lowerCamelCase			(							self		):
              return self._utf_vocab_size + self._num_special_tokens
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = False		):
              if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=SCREAMING_SNAKE_CASE__							,						token_ids_a=SCREAMING_SNAKE_CASE__							,						already_has_special_tokens=SCREAMING_SNAKE_CASE__		)
  
              # normal case: some special tokens
              if token_ids_a is None:
                    return [1] + [0] * len(SCREAMING_SNAKE_CASE__		) + [1]
              return [1] + ([0] * len(SCREAMING_SNAKE_CASE__		)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__		)) + [1]
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None		):
              if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              else:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   int					   =		[chr(SCREAMING_SNAKE_CASE__		) for i in text.encode('''utf-8'''		)]
              return tokens
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              if token in self.special_tokens_encoder:
                    lowercase			:   Optional[Any]					   =		self.special_tokens_encoder[token]
              elif token in self.added_tokens_encoder:
                    lowercase			:   List[Any]					   =		self.added_tokens_encoder[token]
              elif len(SCREAMING_SNAKE_CASE__		) != 1:
                    lowercase			:   List[str]					   =		self.unk_token_id
              else:
                    lowercase			:   Dict					   =		ord(SCREAMING_SNAKE_CASE__		) + self._num_special_tokens
              return token_id
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              if index in self.special_tokens_decoder:
                    lowercase			:   Optional[Any]					   =		self.special_tokens_decoder[index]
              elif index in self.added_tokens_decoder:
                    lowercase			:   Tuple					   =		self.added_tokens_decoder[index]
              else:
                    lowercase			:   Tuple					   =		chr(index - self._num_special_tokens		)
              return token
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   List[str]					   =		B''''''
              for token in tokens:
                    if token in self.special_tokens_decoder:
                          lowercase			:   Dict					   =		self.special_tokens_decoder[token].encode('''utf-8'''		)
                    elif token in self.added_tokens_decoder:
                          lowercase			:   Any					   =		self.special_tokens_decoder[token].encode('''utf-8'''		)
                    elif token in self.special_tokens_encoder:
                          lowercase			:   Optional[int]					   =		token.encode('''utf-8'''		)
                    elif token in self.added_tokens_encoder:
                          lowercase			:   Optional[int]					   =		token.encode('''utf-8'''		)
                    else:
                          lowercase			:   List[str]					   =		bytes([ord(SCREAMING_SNAKE_CASE__		)]		)
                    bstring += tok_string
              lowercase			:   List[str]					   =		bstring.decode('''utf-8'''							,						errors='''replace'''		)
              return string
  
  
  
  
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None		):
              return ()
  
  
style_context: 
  
  
  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   				=      logging.getLogger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """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 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		argparse.ArgumentParser()
        parser.add_argument(
            '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
        parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
        parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
        parser.add_argument('''--stop_token''',    type=_UpperCamelCase,    default=_UpperCamelCase,    help='''Token at which text generation is stopped'''     )
        parser.add_argument(
            '''--temperature''',    type=_UpperCamelCase,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
        parser.add_argument(
            '''--repetition_penalty''',    type=_UpperCamelCase,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''     )
        parser.add_argument('''--k''',    type=_UpperCamelCase,    default=0     )
        parser.add_argument('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
        parser.add_argument('''--p''',    type=_UpperCamelCase,    default=0.9     )
  
        parser.add_argument('''--prefix''',    type=_UpperCamelCase,    default='''''',    help='''Text added prior to input.'''     )
        parser.add_argument('''--padding_text''',    type=_UpperCamelCase,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''     )
        parser.add_argument('''--xlm_language''',    type=_UpperCamelCase,    default='''''',    help='''Optional language when used with the XLM model.'''     )
  
        parser.add_argument('''--seed''',    type=_UpperCamelCase,    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''',    )
        lowercase			:   Any					   =		parser.parse_args()
  
        lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
        lowercase			:   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(_UpperCamelCase     )
  
        # Initialize the model and tokenizer
        lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
        lowercase			:   List[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(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )
  
        lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}
  
        lowercase			:   Optional[int]					   =		model.generate(
            **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )
  
        lowercase			:   Union[str, Any]					   =		[]
        for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
              lowercase			:   Optional[Any]					   =		generated_sequence.tolist()
  
              # Decode text
              lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )
  
              # Remove all text after the stop token
              lowercase			:   Optional[int]					   =		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
              lowercase			:   Optional[Any]					   =		(
                  prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
              )
  
              generated_sequences.append(_UpperCamelCase     )
              print(_UpperCamelCase     )
  
        return generated_sequences
  
  
  if __name__ == "__main__":
    main()
  
  