

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,
)
_lowerCamelCase								=logging.getLogger(__name__)







def       snake_case__    (       lowerCAmelCase_      ):





     """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"""







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

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

     parser.add_argument('--seed',   type=lowerCAmelCase_,   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										=parser.parse_args()

     SCREAMING_SNAKE_CASE										=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
     SCREAMING_SNAKE_CASE										=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(lowerCAmelCase_      )

     # Initialize the model and tokenizer
     SCREAMING_SNAKE_CASE										=AutoTokenizer.from_pretrained(args.model_name_or_path      )
     SCREAMING_SNAKE_CASE										=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(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=args.prompt if args.prompt else input('Model prompt >>> '      )

     SCREAMING_SNAKE_CASE										=tokenizer(lowerCAmelCase_,   return_tensors='pt',   add_special_tokens=lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										={key: value.to(args.device      ) for key, value in inputs.items()}

     SCREAMING_SNAKE_CASE										=model.generate(
         **lowerCAmelCase_,   max_length=args.length + len(inputs['input_ids'][0]      ),   penalty_alpha=args.penalty_alpha,   top_k=args.k,   )

     SCREAMING_SNAKE_CASE										=[]
     for generated_sequence_idx, generated_sequence in enumerate(lowerCAmelCase_      ):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='      )
          SCREAMING_SNAKE_CASE										=generated_sequence.tolist()

          # Decode text
          SCREAMING_SNAKE_CASE										=tokenizer.decode(lowerCAmelCase_,   clean_up_tokenization_spaces=lowerCAmelCase_,   add_special_tokens=lowerCAmelCase_      )

          # Remove all text after the stop token
          SCREAMING_SNAKE_CASE										=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										=(
              prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0],   clean_up_tokenization_spaces=lowerCAmelCase_      )      ) :]
          )

          generated_sequences.append(lowerCAmelCase_      )
          print(lowerCAmelCase_      )

     return generated_sequences


if __name__ == "__main__":
 main()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase								={
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=["XLMProphetNetTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=[
     "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMProphetNetDecoder",
     "XLMProphetNetEncoder",
     "XLMProphetNetForCausalLM",
     "XLMProphetNetForConditionalGeneration",
     "XLMProphetNetModel",
     "XLMProphetNetPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_prophetnet import (
      XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMProphetNetDecoder,
      XLMProphetNetEncoder,
      XLMProphetNetForCausalLM,
      XLMProphetNetForConditionalGeneration,
      XLMProphetNetModel,
      XLMProphetNetPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase								=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

