


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 ()





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()

