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,
  )
  _snake_case			:       Dict      =						logging.getLogger(__name__)
  
  def    __snake_case       (  __magic_name__	):
  
  
  
  
  
  
  
  				'''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    __snake_case       (  ):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				lowercase						    =			argparse.ArgumentParser()
  				parser.add_argument(
  				    "--model_name_or_path"		,						default=__magic_name__		,						type=__magic_name__		,						required=__magic_name__		,						)
  				parser.add_argument("--prompt"		,						type=__magic_name__		,						default=""	)
  				parser.add_argument("--length"		,						type=__magic_name__		,						default=20	)
  				parser.add_argument("--stop_token"		,						type=__magic_name__		,						default=__magic_name__		,						help="Token at which text generation is stopped"	)
  				parser.add_argument(
  				    "--temperature"		,						type=__magic_name__		,						default=1.0		,						help="temperature of 1.0 has no effect, lower tend toward greedy sampling"		,						)
  				parser.add_argument(
  				    "--repetition_penalty"		,						type=__magic_name__		,						default=1.0		,						help="primarily useful for CTRL model; in that case, use 1.2"	)
  				parser.add_argument("--k"		,						type=__magic_name__		,						default=0	)
  				parser.add_argument("--penalty_alpha"		,						type=__magic_name__		,						default=0.0	)
  				parser.add_argument("--p"		,						type=__magic_name__		,						default=0.9	)
  
  				parser.add_argument("--prefix"		,						type=__magic_name__		,						default=""		,						help="Text added prior to input."	)
  				parser.add_argument("--padding_text"		,						type=__magic_name__		,						default=""		,						help="Deprecated, the use of `--prefix` is preferred."	)
  				parser.add_argument("--xlm_language"		,						type=__magic_name__		,						default=""		,						help="Optional language when used with the XLM model."	)
  
  				parser.add_argument("--seed"		,						type=__magic_name__		,						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"		,						)
  				lowercase						    =			parser.parse_args()
  
  				lowercase						    =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"	)
  				lowercase						    =			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(__magic_name__	)
  
  				# Initialize the model and tokenizer
  				lowercase						    =			AutoTokenizer.from_pretrained(args.model_name_or_path	)
  				lowercase						    =			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(__magic_name__	)
  				lowercase						    =			args.prompt if args.prompt else input("Model prompt >>> "	)
  
  				lowercase						    =			tokenizer(__magic_name__		,						return_tensors="pt"		,						add_special_tokens=__magic_name__	)
  				lowercase						    =			{key: value.to(args.device	) for key, value in inputs.items()}
  
  				lowercase						    =			model.generate(
  				    **__magic_name__		,						max_length=args.length + len(inputs["input_ids"][0]	)		,						penalty_alpha=args.penalty_alpha		,						top_k=args.k		,						)
  
  				lowercase						    =			[]
  				for generated_sequence_idx, generated_sequence in enumerate(__magic_name__	):
  								print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''	)
  								lowercase						    =			generated_sequence.tolist()
  
  								# Decode text
  								lowercase						    =			tokenizer.decode(__magic_name__		,						clean_up_tokenization_spaces=__magic_name__		,						add_special_tokens=__magic_name__	)
  
  								# Remove all text after the stop token
  								lowercase						    =			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
  								lowercase						    =			(
  								    prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]		,						clean_up_tokenization_spaces=__magic_name__	)	) :]
  								)
  
  								generated_sequences.append(__magic_name__	)
  								print(__magic_name__	)
  
  				return generated_sequences
  
  
  if __name__ == "__main__":
  						main()
  
style_context: 
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _snake_case			:       List[Any]      =						logging.get_logger(__name__)
  
  _snake_case			:       Dict      =						{
      "google/reformer-crime-and-punishment": (
          "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
      ),
      "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
  }
  
  
  
  
  
  class      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     Any							      =				'''reformer'''
  						UpperCamelCase			:     List[str]							      =				['''past_buckets_states''']
  						UpperCamelCase			:     Optional[int]							      =				{}
  
  
  
  
  
  						def __init__( self       :Dict		,						lowerCAmelCase__       :Any=64		,						lowerCAmelCase__       :str=["local", "lsh", "local", "lsh", "local", "lsh"]		,						lowerCAmelCase__       :Optional[Any]=1.0		,						lowerCAmelCase__       :List[str]=True		,						lowerCAmelCase__       :Optional[Any]=[64, 64]		,						lowerCAmelCase__       :Tuple=[64, 192]		,						lowerCAmelCase__       :Union[str, Any]=0		,						lowerCAmelCase__       :Optional[Any]=2		,						lowerCAmelCase__       :List[str]=512		,						lowerCAmelCase__       :Any=None		,						lowerCAmelCase__       :int="relu"		,						lowerCAmelCase__       :Tuple=0.05		,						lowerCAmelCase__       :int=256		,						lowerCAmelCase__       :Optional[Any]=0.02		,						lowerCAmelCase__       :Optional[int]=False		,						lowerCAmelCase__       :Optional[Any]=1E-1_2		,						lowerCAmelCase__       :int=1		,						lowerCAmelCase__       :Optional[Any]=0		,						lowerCAmelCase__       :Any=0.05		,						lowerCAmelCase__       :Optional[int]=64		,						lowerCAmelCase__       :List[Any]=64		,						lowerCAmelCase__       :Tuple=0.0		,						lowerCAmelCase__       :Tuple=1		,						lowerCAmelCase__       :List[str]=0		,						lowerCAmelCase__       :Dict=4096		,						lowerCAmelCase__       :Tuple=12		,						lowerCAmelCase__       :Tuple=None		,						lowerCAmelCase__       :Union[str, Any]=1		,						lowerCAmelCase__       :int=0		,						lowerCAmelCase__       :Optional[Any]=320		,						lowerCAmelCase__       :Optional[Any]=False		,						lowerCAmelCase__       :Optional[int]=True		,						lowerCAmelCase__       :str=None		,						**lowerCAmelCase__       :Any		,						)     ->Any:
  										lowercase						    =			hash_seed
  										lowercase						    =			vocab_size
  										lowercase						    =			attention_head_size
  										lowercase						    =			hidden_size
  										lowercase						    =			num_attention_heads
  										lowercase						    =			num_hashes
  										lowercase						    =			len(lowerCAmelCase__    )
  										lowercase						    =			tuple(lowerCAmelCase__    ) if isinstance(lowerCAmelCase__		,						lowerCAmelCase__    ) else num_buckets
  										lowercase						    =			lsh_attn_chunk_length
  										lowercase						    =			local_attn_chunk_length
  										lowercase						    =			lsh_num_chunks_after
  										lowercase						    =			lsh_num_chunks_before
  										lowercase						    =			local_num_chunks_after
  										lowercase						    =			local_num_chunks_before
  										lowercase						    =			hidden_act
  										lowercase						    =			feed_forward_size
  										lowercase						    =			hidden_dropout_prob
  										lowercase						    =			lsh_attention_probs_dropout_prob
  										lowercase						    =			local_attention_probs_dropout_prob
  										lowercase						    =			max_position_embeddings
  										lowercase						    =			initializer_range
  										lowercase						    =			layer_norm_eps
  										lowercase						    =			axial_pos_embds
  										lowercase						    =			tuple(lowerCAmelCase__    )
  										lowercase						    =			tuple(lowerCAmelCase__    )
  										lowercase						    =			axial_norm_std
  										lowercase						    =			chunk_size_lm_head
  										lowercase						    =			attn_layers
  										lowercase						    =			use_cache
  										lowercase						    =			classifier_dropout
  										super().__init__(
  										    pad_token_id=lowerCAmelCase__		,						eos_token_id=lowerCAmelCase__		,						is_decoder=lowerCAmelCase__		,						tie_word_embeddings=lowerCAmelCase__		,						**lowerCAmelCase__		,						)
  