

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						:int            = logging.getLogger(__name__)

def 			__snake_case	(   __UpperCamelCase  :   int						):



      """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 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							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"							,)
      A_	     =							parser.parse_args()

      A_	     =							torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
      A_	     =							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
      A_	     =							AutoTokenizer.from_pretrained(args.model_name_or_path						)
      A_	     =							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						)
      A_	     =							args.prompt if args.prompt else input("Model prompt >>> "						)

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

      A_	     =							model.generate(
          **__UpperCamelCase							,max_length=args.length + len(inputs["input_ids"][0]						)							,penalty_alpha=args.penalty_alpha							,top_k=args.k							,)

      A_	     =							[]
      for generated_sequence_idx, generated_sequence in enumerate(__UpperCamelCase						):
            print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''						)
            A_	     =							generated_sequence.tolist()

            # Decode text
            A_	     =							tokenizer.decode(__UpperCamelCase							,clean_up_tokenization_spaces=__UpperCamelCase							,add_special_tokens=__UpperCamelCase						)

            # Remove all text after the stop token
            A_	     =							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
            A_	     =							(
                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()


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

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


__a						:Optional[Any]            = logging.get_logger(__name__)

__a						:Union[str, Any]            = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__a						:Union[str, Any]            = {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__a						:str            = {
    'microsoft/biogpt': 1024,
}

def 			__snake_case	(   __UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							set()
      A_	     =							word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            A_	     =							char
      return pairs



class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Tuple					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Any					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: str					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: List[Any]					=					['input_ids', 'attention_mask']





      def __init__(      self       :							Union[str, Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							List[Any]="<unk>"	, UpperCAmelCase       :							Dict="<s>"	, UpperCAmelCase       :							Tuple="</s>"	, UpperCAmelCase       :							Optional[int]="</s>"	, UpperCAmelCase       :							int="<pad>"	, **UpperCAmelCase       :							List[str]	, ):
            super().__init__(
                bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, **UpperCAmelCase	, )

            try:
                  import sacremoses
            except ImportError:
                  raise ImportError(
                      "You need to install sacremoses to use BioGptTokenizer. "
                      "See https://pypi.org/project/sacremoses/ for installation."  )

            A_	     =							"en"
            A_	     =							sacremoses
            # cache of sm.MosesTokenizer instance
            A_	     =							{}
            A_	     =							{}
            with open(UpperCAmelCase	, encoding="utf-8"  ) as vocab_handle:
                  A_	     =							json.load(UpperCAmelCase  )
            A_	     =							{v: k for k, v in self.encoder.items()}
            with open(UpperCAmelCase	, encoding="utf-8"  ) as merges_handle:
                  A_	     =							merges_handle.read().split("\n"  )[:-1]
            A_	     =							[tuple(merge.split()[:2]  ) for merge in merges]
            A_	     =							dict(zip(UpperCAmelCase	, range(len(UpperCAmelCase  )  )  )  )
            A_	     =							{}





      @property
      def 		__A  (      self       :							int  ):
            return len(self.encoder  )





      def 		__A  (      self       :							Union[str, Any]  ):
            return dict(self.encoder	, **self.added_tokens_encoder  )





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[Any]  ):
            if lang not in self.cache_moses_tokenizer:
                  A_	     =							self.sm.MosesTokenizer(lang=UpperCAmelCase  )
                  A_	     =							moses_tokenizer
            return self.cache_moses_tokenizer[lang].tokenize(
                UpperCAmelCase	, aggressive_dash_splits=UpperCAmelCase	, return_str=UpperCAmelCase	, escape=UpperCAmelCase  )





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[Any]  ):
            if lang not in self.cache_moses_detokenizer:
                  A_	     =							self.sm.MosesDetokenizer(lang=UpperCAmelCase  )
                  A_	     =							moses_detokenizer
            return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase  )





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							int  ):
            A_	     =							tuple(token[:-1]  ) + (token[-1] + "</w>",)
            if token in self.cache:
                  return self.cache[token]
            A_	     =							get_pairs(UpperCAmelCase  )

            if not pairs:
                  return token + "</w>"

            while True:
                  A_	     =							min(UpperCAmelCase	, key=lambda UpperCAmelCase  : self.bpe_ranks.get(UpperCAmelCase	, float("inf"  )  )  )
                  if bigram not in self.bpe_ranks:
                        break
                  A_     ,    A_	     =							bigram
                  A_	     =							[]
                  A_	     =							0
                  while i < len(UpperCAmelCase  ):
                        try:
                              A_	     =							word.index(UpperCAmelCase	, UpperCAmelCase  )
                        except ValueError:
                              new_word.extend(word[i:]  )
                              break
                        else:
                              new_word.extend(word[i:j]  )
                              A_	     =							j

                        if word[i] == first and i < len(UpperCAmelCase  ) - 1 and word[i + 1] == second:
                              new_word.append(first + second  )
                              i += 2
                        else:
                              new_word.append(word[i]  )
                              i += 1
                  A_	     =							tuple(UpperCAmelCase  )
                  A_	     =							new_word
                  if len(UpperCAmelCase  ) == 1:
                        break
                  else:
                        A_	     =							get_pairs(UpperCAmelCase  )
            A_	     =							" ".join(UpperCAmelCase  )
            if word == "\n  </w>":
                  A_	     =							"\n</w>"
            A_	     =							word
            return word





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[Any]=False  ):
            if bypass_tokenizer:
                  A_	     =							text.split()
            else:
                  A_	     =							self.moses_tokenize(UpperCAmelCase	, self.lang  )

            A_	     =							[]
            for token in text:
                  if token:
                        split_tokens.extend(list(self.bpe(UpperCAmelCase  ).split(" "  )  )  )

            return split_tokens





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Union[str, Any]  ):
            return self.encoder.get(UpperCAmelCase	, self.encoder.get(self.unk_token  )  )





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							List[str]  ):
            return self.decoder.get(UpperCAmelCase	, self.unk_token  )





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Any  ):
            A_	     =							[t.replace(" "	, ""  ).replace("</w>"	, " "  ) for t in tokens]
            A_	     =							"".join(UpperCAmelCase  ).split()
            # detokenize
            A_	     =							self.moses_detokenize(UpperCAmelCase	, self.lang  )
            return text





      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None  ):
            if token_ids_a is None:
                  return [self.sep_token_id] + token_ids_a
            A_	     =							[self.sep_token_id]
            return sep + token_ids_a + sep + token_ids_a





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None	, UpperCAmelCase       :							bool = False  ):
            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase	, token_ids_a=UpperCAmelCase	, already_has_special_tokens=UpperCAmelCase  )
            # no bos used in fairseq
            if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  ))
            return [1] + ([0] * len(UpperCAmelCase  ))





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None  ):
            A_	     =							[self.sep_token_id]

            # no bos used in fairseq
            if token_ids_a is None:
                  return len(token_ids_a + sep  ) * [0]
            return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            if not os.path.isdir(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

            with open(UpperCAmelCase	, "w"	, encoding="utf-8"  ) as f:
                  f.write(json.dumps(self.encoder	, indent=2	, sort_keys=UpperCAmelCase	, ensure_ascii=UpperCAmelCase  ) + "\n"  )

            A_	     =							0
            with open(UpperCAmelCase	, "w"	, encoding="utf-8"  ) as writer:
                  for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	, key=lambda UpperCAmelCase  : kv[1]  ):
                        if index != token_index:
                              logger.warning(
                                  f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                  " Please check that the tokenizer is not corrupted!"  )
                              A_	     =							token_index
                        writer.write(" ".join(UpperCAmelCase  ) + "\n"  )
                        index += 1

            return vocab_file, merge_file





      def __getstate__(      self       :							Union[str, Any]  ):
            A_	     =							self.__dict__.copy()
            A_	     =							None
            return state







      def __setstate__(      self       :							List[Any]	, UpperCAmelCase       :							Dict  ):
            A_	     =							d

            try:
                  import sacremoses
            except ImportError:
                  raise ImportError(
                      "You need to install sacremoses to use XLMTokenizer. "
                      "See https://pypi.org/project/sacremoses/ for installation."  )

            A_	     =							sacremoses