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"""
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
  
  
  _lowerCAmelCase					:Union[str, Any]  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:List[str]  =      {
      'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
      'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
      'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
      'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
      'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             ='''gpt2'''
       a__             =['''past_key_values''']
       a__             ={
           '''hidden_size''': '''n_embd''',
           '''max_position_embeddings''': '''n_positions''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }
  
  
  
  
  
       def __init__(      self ,			A=5_0_2_5_7 ,			A=1_0_2_4 ,			A=7_6_8 ,			A=1_2 ,			A=1_2 ,			A=None ,			A="gelu_new" ,			A=0.1 ,			A=0.1 ,			A=0.1 ,			A=1E-5 ,			A=0.02 ,			A="cls_index" ,			A=True ,			A=None ,			A=True ,			A=0.1 ,			A=True ,			A=True ,			A=5_0_2_5_6 ,			A=5_0_2_5_6 ,			A=False ,			A=False ,			**A ,			)							->      Union[str, Any]:
           _UpperCAmelCase							:						Optional[int]       							=					vocab_size
           _UpperCAmelCase							:						Union[str, Any]       							=					n_positions
           _UpperCAmelCase							:						Optional[Any]       							=					n_embd
           _UpperCAmelCase							:						Tuple       							=					n_layer
           _UpperCAmelCase							:						Any       							=					n_head
           _UpperCAmelCase							:						Union[str, Any]       							=					n_inner
           _UpperCAmelCase							:						Optional[int]       							=					activation_function
           _UpperCAmelCase							:						Optional[int]       							=					resid_pdrop
           _UpperCAmelCase							:						Dict       							=					embd_pdrop
           _UpperCAmelCase							:						List[Any]       							=					attn_pdrop
           _UpperCAmelCase							:						Tuple       							=					layer_norm_epsilon
           _UpperCAmelCase							:						Optional[int]       							=					initializer_range
           _UpperCAmelCase							:						List[str]       							=					summary_type
           _UpperCAmelCase							:						Tuple       							=					summary_use_proj
           _UpperCAmelCase							:						List[Any]       							=					summary_activation
           _UpperCAmelCase							:						Optional[int]       							=					summary_first_dropout
           _UpperCAmelCase							:						int       							=					summary_proj_to_labels
           _UpperCAmelCase							:						Union[str, Any]       							=					scale_attn_weights
           _UpperCAmelCase							:						Any       							=					use_cache
           _UpperCAmelCase							:						Tuple       							=					scale_attn_by_inverse_layer_idx
           _UpperCAmelCase							:						Optional[Any]       							=					reorder_and_upcast_attn
  
           _UpperCAmelCase							:						Union[str, Any]       							=					bos_token_id
           _UpperCAmelCase							:						Optional[int]       							=					eos_token_id
  
           super().__init__(bos_token_id=A ,			eos_token_id=A ,			**A	)
  
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A = "default" ,			A = None ,			A = False ,			)							->      str:
           super().__init__(A ,			task=A ,			patching_specs=A ,			use_past=A	)
           if not getattr(self._config ,			'''pad_token_id''' ,			A	):
               # TODO: how to do that better?
               _UpperCAmelCase							:						Optional[int]       							=					0
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      Mapping[str, Mapping[int, str]]:
           _UpperCAmelCase							:						List[str]       							=					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
           if self.use_past:
               self.fill_with_past_key_values_(A ,			direction='''inputs'''	)
               _UpperCAmelCase							:						Union[str, Any]       							=					{0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
               _UpperCAmelCase							:						Tuple       							=					{0: '''batch''', 1: '''sequence'''}
  
           return common_inputs
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return self._config.n_layer
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return self._config.n_head
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = -1 ,			A = -1 ,			A = False ,			A = None ,			)							->      Mapping[str, Any]:
           _UpperCAmelCase							:						int       							=					super(A ,			self	).generate_dummy_inputs(
               A ,			batch_size=A ,			seq_length=A ,			is_pair=A ,			framework=A	)
  
           # We need to order the input in the way they appears in the forward()
           _UpperCAmelCase							:						List[Any]       							=					OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)
  
           # Need to add the past_keys
           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
               else:
                   import torch
  
                   _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[Any]       							=					common_inputs['''input_ids'''].shape
                   # Not using the same length for past_key_values
                   _UpperCAmelCase							:						Optional[Any]       							=					seqlen + 2
                   _UpperCAmelCase							:						Union[str, Any]       							=					(
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   _UpperCAmelCase							:						List[Any]       							=					[
                       (torch.zeros(A	), torch.zeros(A	)) for _ in range(self.num_layers	)
                   ]
  
           _UpperCAmelCase							:						int       							=					common_inputs['''attention_mask''']
           if self.use_past:
               _UpperCAmelCase							:						int       							=					ordered_inputs['''attention_mask'''].dtype
               _UpperCAmelCase							:						Dict       							=					torch.cat(
                   [ordered_inputs['''attention_mask'''], torch.ones(A ,			A ,			dtype=A	)] ,			dim=1	)
  
           return ordered_inputs
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return 1_3
  
  
  