


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_  :    Optional[int]      =			logging.getLogger(__name__)





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

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

 parser.add_argument("--seed" ,    type=SCREAMING_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" ,    )
 lowerCamelCase_								=  parser.parse_args()

 lowerCamelCase_								=  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
 lowerCamelCase_								=  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(SCREAMING_SNAKE_CASE_		)

 # Initialize the model and tokenizer
 lowerCamelCase_								=  AutoTokenizer.from_pretrained(args.model_name_or_path		)
 lowerCamelCase_								=  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(SCREAMING_SNAKE_CASE_		)
 lowerCamelCase_								=  args.prompt if args.prompt else input("Model prompt >>> "		)

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

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

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

  # Decode text
  lowerCamelCase_								=  tokenizer.decode(SCREAMING_SNAKE_CASE_ ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ ,    add_special_tokens=SCREAMING_SNAKE_CASE_		)

  # Remove all text after the stop token
  lowerCamelCase_								=  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_								=  (
      prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0] ,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_		)		) :]
  )

  generated_sequences.append(SCREAMING_SNAKE_CASE_		)
  print(SCREAMING_SNAKE_CASE_		)

 return generated_sequences


if __name__ == "__main__":
     main()







'''simple docstring'''

import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
a_  :    Any      =			"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
a_  :    Optional[Any]      =			"""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
a_  :    List[str]      =			""""""

# TODO: Add the licence for the dataset here if you can find it
a_  :    Dict      =			""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
a_  :    Optional[int]      =			{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}





class 					snake_case   (   datasets.GeneratorBasedBuilder      ):





  """simple docstring"""




  _lowerCamelCase		   =  datasets.Version("1.1.0"      )

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  _lowerCamelCase		   =  [
      datasets.BuilderConfig(name="first_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a first domain"      ),
      datasets.BuilderConfig(name="second_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a second domain"      ),
  ]

  _lowerCamelCase		   =  "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.






  def   snake_case		(   self					):







   """simple docstring"""
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option1": datasets.Value("string"					),
            "answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option2": datasets.Value("string"					),
            "second_domain_answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION      ,  features=UpperCamelCase      ,  homepage=_HOMEPAGE      ,  license=_LICENSE      ,  citation=_CITATION      ,  )






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   lowerCamelCase_								=  _URLS[self.config.name]
   lowerCamelCase_								=  dl_manager.download_and_extract(UpperCamelCase					)
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "train.jsonl"					),
               "split": "train",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "dev.jsonl"					),
               "split": "dev",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.TEST      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "test.jsonl"					),
               "split": "test"
           }      ,  ),
   ]







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(UpperCamelCase      ,  encoding="utf-8"					) as f:
    for key, row in enumerate(UpperCamelCase					):
     lowerCamelCase_								=  json.loads(UpperCamelCase					)
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }



