code: 
  
  
  
  """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,
  )
  _lowerCAmelCase					:Optional[int]  =      logging.getLogger(__name__)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Dict							):
      np.random.seed(args.seed							)
      torch.manual_seed(args.seed							)
      if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)
  
  
  
  
  
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						List[str]       							=					argparse.ArgumentParser()
      parser.add_argument(
          '''--model_name_or_path'''  , default=UpperCamelCase__  , type=UpperCamelCase__  , required=UpperCamelCase__  , )
      parser.add_argument('''--prompt'''  , type=UpperCamelCase__  , default=''''''							)
      parser.add_argument('''--length'''  , type=UpperCamelCase__  , default=20							)
      parser.add_argument('''--stop_token'''  , type=UpperCamelCase__  , default=UpperCamelCase__  , help='''Token at which text generation is stopped'''							)
      parser.add_argument(
          '''--temperature'''  , type=UpperCamelCase__  , default=1.0  , help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''  , )
      parser.add_argument(
          '''--repetition_penalty'''  , type=UpperCamelCase__  , default=1.0  , help='''primarily useful for CTRL model; in that case, use 1.2'''							)
      parser.add_argument('''--k'''  , type=UpperCamelCase__  , default=0							)
      parser.add_argument('''--penalty_alpha'''  , type=UpperCamelCase__  , default=0.0							)
      parser.add_argument('''--p'''  , type=UpperCamelCase__  , default=0.9							)
  
      parser.add_argument('''--prefix'''  , type=UpperCamelCase__  , default=''''''  , help='''Text added prior to input.'''							)
      parser.add_argument('''--padding_text'''  , type=UpperCamelCase__  , default=''''''  , help='''Deprecated, the use of `--prefix` is preferred.'''							)
      parser.add_argument('''--xlm_language'''  , type=UpperCamelCase__  , default=''''''  , help='''Optional language when used with the XLM model.'''							)
  
      parser.add_argument('''--seed'''  , type=UpperCamelCase__  , 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'''  , )
      _UpperCAmelCase							:						str       							=					parser.parse_args()
  
      _UpperCAmelCase							:						Optional[Any]       							=					torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
      _UpperCAmelCase							:						Any       							=					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(UpperCamelCase__							)
  
      # Initialize the model and tokenizer
      _UpperCAmelCase							:						Tuple       							=					AutoTokenizer.from_pretrained(args.model_name_or_path							)
      _UpperCAmelCase							:						Optional[int]       							=					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(UpperCamelCase__							)
      _UpperCAmelCase							:						str       							=					args.prompt if args.prompt else input('''Model prompt >>> '''							)
  
      _UpperCAmelCase							:						Tuple       							=					tokenizer(UpperCamelCase__  , return_tensors='''pt'''  , add_special_tokens=UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					{key: value.to(args.device							) for key, value in inputs.items()}
  
      _UpperCAmelCase							:						List[Any]       							=					model.generate(
          **UpperCamelCase__  , max_length=args.length + len(inputs['''input_ids'''][0]							)  , penalty_alpha=args.penalty_alpha  , top_k=args.k  , )
  
      _UpperCAmelCase							:						Tuple       							=					[]
      for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__							):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
          _UpperCAmelCase							:						Optional[Any]       							=					generated_sequence.tolist()
  
          # Decode text
          _UpperCAmelCase							:						Dict       							=					tokenizer.decode(UpperCamelCase__  , clean_up_tokenization_spaces=UpperCamelCase__  , add_special_tokens=UpperCamelCase__							)
  
          # Remove all text after the stop token
          _UpperCAmelCase							:						Any       							=					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							:						List[Any]       							=					(
              prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]  , clean_up_tokenization_spaces=UpperCamelCase__							)							) :]
          )
  
          generated_sequences.append(UpperCamelCase__							)
          print(UpperCamelCase__							)
  
      return generated_sequences
  
  
  if __name__ == "__main__":
    main()
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  # Lint as: python3
  import sys
  from collections.abc import Mapping
  from typing import TYPE_CHECKING
  
  import numpy as np
  import pyarrow as pa
  
  from .. import config
  from ..utils.py_utils import map_nested
  from .formatting import TensorFormatter
  
  
  if TYPE_CHECKING:
    import tensorflow as tf
  class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A=None ,			**A	)							->      List[str]:
           super().__init__(features=A	)
           _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           import tensorflow as tf
  
           if isinstance(A ,			A	) and column:
               if all(
                   isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                   return tf.stack(A	)
               elif all(
                   isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                   for x in column	):
                   # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                   return tf.ragged.stack(A	)
  
           return column
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           import tensorflow as tf
  
           if value is None:
               return value
  
           _UpperCAmelCase							:						Any       							=					{}
  
           if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
               _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
           elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
               _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image
  
               if isinstance(A ,			PIL.Image.Image	):
                   _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)
  
           return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
           import tensorflow as tf
  
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch
  
               if isinstance(A ,			torch.Tensor	):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
           if hasattr(A ,			'''__array__'''	) and not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A ,			np.ndarray	):
               if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
           elif isinstance(A ,			(list, tuple)	):
               return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
           return self._tensorize(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
           _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
           _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
           return self.recursive_tensorize(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
           _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
           _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
           _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
           _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
           return column
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
           _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
           _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
           for column_name in batch:
               _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
           return batch
  
  
  