






"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


A_			=							logging.get_logger(__name__)

A_			=							{'''vocab_file''': '''vocab.txt'''}

A_			=							{
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

A_			=							{
    '''tau/splinter-base''': 512,
    '''tau/splinter-base-qass''': 512,
    '''tau/splinter-large''': 512,
    '''tau/splinter-large-qass''': 512,
}

A_			=							{
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}






class 							__SCREAMING_SNAKE_CASE  ( UpperCamelCase						):
							snake_case_   =							VOCAB_FILES_NAMES
							snake_case_   =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_   =							PRETRAINED_INIT_CONFIGURATION
							snake_case_   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_   =							SplinterTokenizer


							def __init__(	self					:  Any	,			snake_case					:  Any=None	,			snake_case					:  int=None	,			snake_case					:  Dict=True	,			snake_case					:  Union[str, Any]="[UNK]"	,			snake_case					:  List[str]="[SEP]"	,			snake_case					:  int="[PAD]"	,			snake_case					:  List[str]="[CLS]"	,			snake_case					:  List[Any]="[MASK]"	,			snake_case					:  Tuple="[QUESTION]"	,			snake_case					:  Optional[Any]=True	,			snake_case					:  Tuple=None	,			**snake_case					:  Dict	,			):
													'''simple docstring'''





													super().__init__(
													    UpperCAmelCase_	,			tokenizer_file=UpperCAmelCase_	,			do_lower_case=UpperCAmelCase_	,			unk_token=UpperCAmelCase_	,			sep_token=UpperCAmelCase_	,			pad_token=UpperCAmelCase_	,			cls_token=UpperCAmelCase_	,			mask_token=UpperCAmelCase_	,			tokenize_chinese_chars=UpperCAmelCase_	,			strip_accents=UpperCAmelCase_	,			additional_special_tokens=(question_token,)	,			**UpperCAmelCase_	,			)

													A__  :     Tuple						     =     json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
													if (
													    pre_tok_state.get("""lowercase"""	,			UpperCAmelCase_							) != do_lower_case
													    or pre_tok_state.get("""strip_accents"""	,			UpperCAmelCase_							) != strip_accents
													):
																			A__  :     List[Any]						     =     getattr(UpperCAmelCase_	,			pre_tok_state.pop("""type"""							)							)
																			A__  :     List[str]						     =     do_lower_case
																			A__  :     Union[str, Any]						     =     strip_accents
																			A__  :     Tuple						     =     pre_tok_class(**UpperCAmelCase_							)

													A__  :     Optional[Any]						     =     do_lower_case


							@property
							def   _UpperCamelCase				(	self					:  Dict							):
													'''simple docstring'''





													return self.convert_tokens_to_ids(self.question_token							)


							def   _UpperCamelCase				(	self					:  List[Any]	,			snake_case					:  List[int]	,			snake_case					:  Optional[List[int]] = None							):
													'''simple docstring'''





													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

													A__  :     Optional[Any]						     =     [self.cls_token_id]
													A__  :     Optional[int]						     =     [self.sep_token_id]
													A__  :     Any						     =     [self.question_token_id] + [self.convert_tokens_to_ids("""."""							)]
													if self.padding_side == "right":
																			# Input is question-then-context
																			return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
													else:
																			# Input is context-then-question
																			return cls + token_ids_a + sep + token_ids_a + question_suffix + sep


							def   _UpperCamelCase				(	self					:  int	,			snake_case					:  List[int]	,			snake_case					:  Optional[List[int]] = None							):
													'''simple docstring'''





													A__  :     Tuple						     =     [self.sep_token_id]
													A__  :     str						     =     [self.cls_token_id]
													A__  :     List[str]						     =     [self.question_token_id] + [self.convert_tokens_to_ids("""."""							)]
													if token_ids_a is None:
																			return len(cls + token_ids_a + sep							) * [0]

													if self.padding_side == "right":
																			# Input is question-then-context
																			return len(cls + token_ids_a + question_suffix + sep							) * [0] + len(token_ids_a + sep							) * [1]
													else:
																			# Input is context-then-question
																			return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + question_suffix + sep							) * [1]


							def   _UpperCamelCase				(	self					:  Union[str, Any]	,			snake_case					:  str	,			snake_case					:  Optional[str] = None							):
													'''simple docstring'''





													A__  :     Optional[Any]						     =     self._tokenizer.model.save(UpperCAmelCase_	,			name=UpperCAmelCase_							)
													return tuple(UpperCAmelCase_							)














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()






