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__  (		lowercase				):
   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__  (		):
   lowerCAmelCase_:       int         =  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"       ,							)
   lowerCAmelCase_:       str         =  parser.parse_args()

   lowerCAmelCase_:       Tuple         =  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"				)
   lowerCAmelCase_:       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
   lowerCAmelCase_:       Any         =  AutoTokenizer.from_pretrained(args.model_name_or_path				)
   lowerCAmelCase_:       List[str]         =  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				)
   lowerCAmelCase_:       Dict         =  args.prompt if args.prompt else input("Model prompt >>> "				)

   lowerCAmelCase_:       Dict         =  tokenizer(lowercase       ,							return_tensors="pt"       ,							add_special_tokens=lowercase				)
   lowerCAmelCase_:       Union[str, Any]         =  {key: value.to(args.device				) for key, value in inputs.items()}

   lowerCAmelCase_:       Optional[Any]         =  model.generate(
       **lowercase       ,							max_length=args.length + len(inputs["input_ids"][0]				)       ,							penalty_alpha=args.penalty_alpha       ,							top_k=args.k       ,							)

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

      # Decode text
      lowerCAmelCase_:       Tuple         =  tokenizer.decode(lowercase       ,							clean_up_tokenization_spaces=lowercase       ,							add_special_tokens=lowercase				)

      # Remove all text after the stop token
      lowerCAmelCase_:       Optional[int]         =  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
      lowerCAmelCase_:       Tuple         =  (
          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()
from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

class 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float




    @property
    def     _a						(						self   ):
       return sizeof(self.value   )





def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      lowerCAmelCase_:       int         =  fixed_dimension

   dimension -= num_token_to_add
   return dimension
def 		snake_case__  (		lowercase       ,							lowercase				):
   return num_parameters * dtype.size







def 		snake_case__  (		lowercase				):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(lowercase				)
   except (ValueError, OSError, KeyError):
      lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
      try:
         lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass
      try:
         lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor