"""simple docstring"""


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,
)
__UpperCamelCase       :    Union[str, Any]         =		logging.getLogger(__name__)


def   __UpperCAmelCase     (	_snake_case		:       int       ):
   np.random.seed(args.seed       )
   torch.manual_seed(args.seed       )
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       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=2_0       )
   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=4_2,				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							     =       parser.parse_args()

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

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

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

   _lowercase							     =       []
   for generated_sequence_idx, generated_sequence in enumerate(_snake_case       ):
      print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""       )
      _lowercase							     =       generated_sequence.tolist()

      # Decode text
      _lowercase							     =       tokenizer.decode(_snake_case,				clean_up_tokenization_spaces=_snake_case,				add_special_tokens=_snake_case       )

      # Remove all text after the stop token
      _lowercase							     =       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							     =       (
          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()
"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__UpperCamelCase       :    str         =		logging.get_logger(__name__)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       List[Any]   ,						*_lowercase			:       Optional[Any]   ,						**_lowercase			:       str		)						->    None:
       warnings.warn(
           "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use ConvNextImageProcessor instead."   ,						_lowercase   ,						)
       super().__init__(*_lowercase   ,						**_lowercase		)