code: 
  
  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_		:			Any										=   logging.getLogger(__name__)
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     List[str]:
  
  
  
  
  
  
      '''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       __a       (   )      ->     Optional[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			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=2_0      )
      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=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'''       ,   )
      __UpperCAmelCase									=			parser.parse_args()
  
      __UpperCAmelCase									=			torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''      )
      __UpperCAmelCase									=			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
      __UpperCAmelCase									=			AutoTokenizer.from_pretrained(args.model_name_or_path      )
      __UpperCAmelCase									=			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__      )
      __UpperCAmelCase									=			args.prompt if args.prompt else input('''Model prompt >>> '''      )
  
      __UpperCAmelCase									=			tokenizer(lowercase__       ,   return_tensors='''pt'''       ,   add_special_tokens=lowercase__      )
      __UpperCAmelCase									=			{key: value.to(args.device      ) for key, value in inputs.items()}
  
      __UpperCAmelCase									=			model.generate(
          **lowercase__       ,   max_length=args.length + len(inputs['''input_ids'''][0]      )       ,   penalty_alpha=args.penalty_alpha       ,   top_k=args.k       ,   )
  
      __UpperCAmelCase									=			[]
      for generated_sequence_idx, generated_sequence in enumerate(lowercase__      ):
          print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''      )
          __UpperCAmelCase									=			generated_sequence.tolist()
  
          # Decode text
          __UpperCAmelCase									=			tokenizer.decode(lowercase__       ,   clean_up_tokenization_spaces=lowercase__       ,   add_special_tokens=lowercase__      )
  
          # Remove all text after the stop token
          __UpperCAmelCase									=			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
          __UpperCAmelCase									=			(
              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()
  
  
  
  
  
style_context: 
  
  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										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # 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_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       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')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # 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
              __UpperCAmelCase									=			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"
              __UpperCAmelCase									=			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=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # 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
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # 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(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  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"],
                      }
  
  
  
  
  