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

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


if is_sentencepiece_available():
     from .tokenization_reformer import ReformerTokenizer
else:
     __magic_name__  :  Tuple     			=   None


__magic_name__  :  List[Any]     			=   logging.get_logger(__name__)


__magic_name__  :  str     			=   '▁'

__magic_name__  :  Union[str, Any]     			=   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__magic_name__  :  List[Any]     			=   {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

__magic_name__  :  List[str]     			=   {
    'google/reformer-crime-and-punishment': 5_2_4_2_8_8,
}


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  lowerCAmelCase_								=		VOCAB_FILES_NAMES
  lowerCAmelCase_								=		PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_								=		["""input_ids""", """attention_mask"""]
  lowerCAmelCase_								=		ReformerTokenizer

  def __init__(					self						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase="</s>"						,    __UpperCamelCase="<unk>"						,    __UpperCamelCase=[]						,    **__UpperCamelCase						,    ):
      super().__init__(
          __UpperCamelCase						,    tokenizer_file=__UpperCamelCase						,    eos_token=__UpperCamelCase						,    unk_token=__UpperCamelCase						,    additional_special_tokens=__UpperCamelCase						,    **__UpperCamelCase						,    )

      A_								=					vocab_file
      A_								=					False if not self.vocab_file else True

  def 					lowercase_						(					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
      A_								=					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,)








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,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Optional[Any]:
    np.random.seed(args.seed )
    torch.manual_seed(args.seed )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed )





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

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

    parser.add_argument("--seed"				,					type=snake_case__				,					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(snake_case__ )

    # 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(snake_case__ )
    A_								=					args.prompt if args.prompt else input("Model prompt >>> " )

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

    A_								=					model.generate(
        **snake_case__				,					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(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

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

        # 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=snake_case__ ) ) :]
        )

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






