code: 
  
  
  
  
  
  
  """simple docstring"""
  import argparse
  import inspect
  import logging
  from typing import Tuple
  
  import numpy as np
  import torch
  
  from transformers import (
      AutoTokenizer,
      BloomForCausalLM,
      BloomTokenizerFast,
      CTRLLMHeadModel,
      CTRLTokenizer,
      GenerationMixin,
      GPTaLMHeadModel,
      GPTaTokenizer,
      GPTJForCausalLM,
      LlamaForCausalLM,
      LlamaTokenizer,
      OpenAIGPTLMHeadModel,
      OpenAIGPTTokenizer,
      OPTForCausalLM,
      TransfoXLLMHeadModel,
      TransfoXLTokenizer,
      XLMTokenizer,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
      XLNetTokenizer,
  )
  from transformers.modeling_outputs import CausalLMOutputWithPast
  
  
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
      datefmt="""%m/%d/%Y %H:%M:%S""",
      level=logging.INFO,
  )
  snake_case   :   Optional[Any]    		=		logging.getLogger(__name__)
  
  snake_case   :   Dict    		=		int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop
  
  snake_case   :   Optional[int]    		=		{
      """gpt2""": (GPTaLMHeadModel, GPTaTokenizer),
      """ctrl""": (CTRLLMHeadModel, CTRLTokenizer),
      """openai-gpt""": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
      """xlnet""": (XLNetLMHeadModel, XLNetTokenizer),
      """transfo-xl""": (TransfoXLLMHeadModel, TransfoXLTokenizer),
      """xlm""": (XLMWithLMHeadModel, XLMTokenizer),
      """gptj""": (GPTJForCausalLM, AutoTokenizer),
      """bloom""": (BloomForCausalLM, BloomTokenizerFast),
      """llama""": (LlamaForCausalLM, LlamaTokenizer),
      """opt""": (OPTForCausalLM, GPTaTokenizer),
  }
  
  # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
  # in https://github.com/rusiaaman/XLNet-gen#methodology
  # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
  snake_case   :   List[Any]    		=		"""In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
  
  
  def        A	(							__snake_case:   Union[str, Any]     )						->		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	(							__snake_case:   Tuple   ,    __snake_case:   Dict   ,    __snake_case:   Dict   ,    __snake_case:   List[str]     )						->		str:
  
         """simple docstring"""
  
  
  
  
  
         if args.temperature > 0.7:
                logger.info('CTRL typically works better with lower temperatures (and lower top_k).'     )
  
         __magic_name__										=						tokenizer.encode(UpperCAmelCase_   ,    add_special_tokens=UpperCAmelCase_     )
         if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()     ):
                logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'     )
         return prompt_text
  
  
  def        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   Tuple     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						hasattr(model.config   ,    'use_lang_emb'     ) and model.config.use_lang_emb
         if hasattr(model.config   ,    'lang2id'     ) and use_lang_emb:
                __magic_name__										=						model.config.langaid.keys()
                if args.xlm_language in available_languages:
                       __magic_name__										=						args.xlm_language
                else:
                       __magic_name__										=						None
                       while language not in available_languages:
                              __magic_name__										=						input('Using XLM. Select language in ' + str(list(UpperCAmelCase_     )     ) + ' >>> '     )
  
                __magic_name__										=						model.config.langaid[language]
                # kwargs["language"] = tokenizer.lang2id[language]
  
         # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
         # XLM masked-language modeling (MLM) models need masked token
         # is_xlm_mlm = "mlm" in args.model_name_or_path
         # if is_xlm_mlm:
         #     kwargs["mask_token_id"] = tokenizer.mask_token_id
  
         return prompt_text
  
  
  def        A	(							__snake_case:   List[str]   ,    __snake_case:   int   ,    __snake_case:   Optional[int]   ,    __snake_case:   List[Any]     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
         __magic_name__										=						prefix + prompt_text
         return prompt_text
  
  
  def        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   int   ,    __snake_case:   int     )						->		Tuple:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
         __magic_name__										=						prefix + prompt_text
         return prompt_text
  
  
  snake_case   :   int    		=		{
      """ctrl""": prepare_ctrl_input,
      """xlm""": prepare_xlm_input,
      """xlnet""": prepare_xlnet_input,
      """transfo-xl""": prepare_transfoxl_input,
  }
  
  
  def        A	(							__snake_case:   List[Any]   ,    __snake_case:   Optional[int]     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         if length < 0 and max_sequence_length > 0:
                __magic_name__										=						max_sequence_length
         elif 0 < max_sequence_length < length:
                __magic_name__										=						max_sequence_length  # No generation bigger than model size
         elif length < 0:
                __magic_name__										=						MAX_LENGTH  # avoid infinite loop
         return length
  
  
  def        A	(							__snake_case:   Union[str, Any]     )						->		Tuple:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						None
         if hasattr(UpperCAmelCase_   ,    'hidden_size'     ):
                __magic_name__										=						model_config.hidden_size
         elif hasattr(UpperCAmelCase_   ,    'n_embed'     ):
                __magic_name__										=						model_config.n_embed
         elif hasattr(UpperCAmelCase_   ,    'n_embd'     ):
                __magic_name__										=						model_config.n_embd
  
         __magic_name__										=						None
         if hasattr(UpperCAmelCase_   ,    'num_attention_heads'     ):
                __magic_name__										=						model_config.num_attention_heads
         elif hasattr(UpperCAmelCase_   ,    'n_head'     ):
                __magic_name__										=						model_config.n_head
  
         if embedding_size is None or num_head is None or num_head == 0:
                raise ValueError('Check the model config'     )
  
         __magic_name__										=						int(embedding_size / num_head     )
         if hasattr(UpperCAmelCase_   ,    'n_layer'     ):
                __magic_name__										=						model_config.n_layer
         elif hasattr(UpperCAmelCase_   ,    'num_hidden_layers'     ):
                __magic_name__										=						model_config.num_hidden_layers
         else:
                raise ValueError('Number of hidden layers couldn\'t be determined from the model config'     )
  
         return num_layer, num_head, num_embedding_size_per_head
  
  
  def        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Optional[int]   ,    __snake_case:   List[Any]     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						sparse_model_config(model.config     )
         if model.config.model_type == "bloom":
                __magic_name__										=						tuple(
                    (
                        torch.empty(int(num_attention_heads * batch_size     )   ,    UpperCAmelCase_   ,    UpperCAmelCase_     )
                        .to(model.dtype     )
                        .to(model.device     ),
                        torch.empty(int(num_attention_heads * batch_size     )   ,    UpperCAmelCase_   ,    UpperCAmelCase_     )
                        .to(model.dtype     )
                        .to(model.device     ),
                    )
                    for _ in range(UpperCAmelCase_     )     )
         else:
                __magic_name__										=						tuple(
                    (
                        torch.empty(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_     )
                        .to(model.dtype     )
                        .to(model.device     ),
                        torch.empty(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_     )
                        .to(model.dtype     )
                        .to(model.device     ),
                    )
                    for _ in range(UpperCAmelCase_     )     )
         return past_key_values
  
  
  def        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Dict   ,    __snake_case:   Union[str, Any]     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						len(UpperCAmelCase_     )
         __magic_name__										=						tokenizer.batch_encode_plus(UpperCAmelCase_   ,    return_tensors='pt'     )
         __magic_name__										=						dummy_input.to(model.device     )
         if model.config.use_cache:
                __magic_name__										=						generate_past_key_values(UpperCAmelCase_   ,    UpperCAmelCase_   ,    1     )
         __magic_name__										=						torch.cat(
             [
                 torch.zeros(dummy_input['attention_mask'].shape[0]   ,    1     )
                 .to(dummy_input['attention_mask'].dtype     )
                 .to(model.device     ),
                 dummy_input['attention_mask'],
             ]   ,    -1   ,    )
         return dummy_input
  
  
  
  class 					UpperCamelCase__   (							A_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   ("""_optimized""", """_default""")
  
  
  
  
  
  
  
      def __init__(		self   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						optimized
             __magic_name__										=						default
  
  
  
  
  
  
  
      def __call__(		self   :  Optional[Any]							,						*UpperCamelCase_   :  Dict							,						**UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             if kwargs["past_key_values"] is None and self._default.config.use_cache:
                    __magic_name__										=						generate_past_key_values(self._default							,						kwargs['input_ids'].shape[0]							,						0   )
             kwargs.pop('position_ids'							,						UpperCamelCase_   )
             for k in list(kwargs.keys()   ):
                    if kwargs[k] is None or isinstance(kwargs[k]							,						UpperCamelCase_   ):
                           kwargs.pop(UpperCamelCase_   )
             __magic_name__										=						self._optimized(**UpperCamelCase_   )
             __magic_name__										=						outputs[0]
             __magic_name__										=						outputs[1]
             __magic_name__										=						CausalLMOutputWithPast(
                 loss=UpperCamelCase_							,						logits=UpperCamelCase_							,						past_key_values=UpperCamelCase_							,						hidden_states=UpperCamelCase_							,						attentions=UpperCamelCase_							,						)
             return fixed_output
  
  
  
  
  
  
  
      def __getattr__(		self   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return getattr(self._default							,						UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]=None							,						UpperCamelCase_   :  int=None							,						UpperCamelCase_   :  List[str]=None							,						**UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self._default.prepare_inputs_for_generation(
                 UpperCamelCase_							,						past_key_values=UpperCamelCase_							,						inputs_embeds=UpperCamelCase_							,						use_cache=UpperCamelCase_							,						**UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  Tuple[Tuple[torch.Tensor]]							,						UpperCamelCase_   :  torch.Tensor   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self._default._reorder_cache(UpperCamelCase_							,						UpperCamelCase_   )
  
  
  def        A	(							)						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						argparse.ArgumentParser()
         parser.add_argument(
             '--model_type'   ,    default=UpperCAmelCase_   ,    type=UpperCAmelCase_   ,    required=UpperCAmelCase_   ,    help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()     )   ,    )
         parser.add_argument(
             '--model_name_or_path'   ,    default=UpperCAmelCase_   ,    type=UpperCAmelCase_   ,    required=UpperCAmelCase_   ,    help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()     )   ,    )
  
         parser.add_argument('--prompt'   ,    type=UpperCAmelCase_   ,    default=''     )
         parser.add_argument('--length'   ,    type=UpperCAmelCase_   ,    default=2_0     )
         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('--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=4_2   ,    help='random seed for initialization'     )
         parser.add_argument('--no_cuda'   ,    action='store_true'   ,    help='Avoid using CUDA when available'     )
         parser.add_argument('--num_return_sequences'   ,    type=UpperCAmelCase_   ,    default=1   ,    help='The number of samples to generate.'     )
         parser.add_argument(
             '--fp16'   ,    action='store_true'   ,    help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'   ,    )
         parser.add_argument('--jit'   ,    action='store_true'   ,    help='Whether or not to use jit trace to accelerate inference'     )
         __magic_name__										=						parser.parse_args()
  
         __magic_name__										=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'     )
         __magic_name__										=						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
         try:
                __magic_name__										=						args.model_type.lower()
                __magic_name__										=						MODEL_CLASSES[args.model_type]
         except KeyError:
                raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'     )
  
         __magic_name__										=						tokenizer_class.from_pretrained(args.model_name_or_path     )
         if tokenizer.pad_token is None:
                __magic_name__										=						tokenizer.eos_token
         __magic_name__										=						model_class.from_pretrained(args.model_name_or_path     )
         model.to(args.device     )
  
         if args.fpaa:
                model.half()
         __magic_name__										=						getattr(model.config   ,    'max_position_embeddings'   ,    0     )
         __magic_name__										=						adjust_length_to_model(args.length   ,    max_sequence_length=UpperCAmelCase_     )
         logger.info(UpperCAmelCase_     )
  
         __magic_name__										=						args.prompt if args.prompt else input('Model prompt >>> '     )
  
         # Different models need different input formatting and/or extra arguments
         __magic_name__										=						args.model_type in PREPROCESSING_FUNCTIONS.keys()
         if requires_preprocessing:
                __magic_name__										=						PREPROCESSING_FUNCTIONS.get(args.model_type     )
                __magic_name__										=						prepare_input(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_     )
  
                if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                       __magic_name__										=						{'''add_space_before_punct_symbol''': True}
                else:
                       __magic_name__										=						{}
  
                __magic_name__										=						tokenizer.encode(
                    UpperCAmelCase_   ,    add_special_tokens=UpperCAmelCase_   ,    return_tensors='pt'   ,    **UpperCAmelCase_     )
         else:
                __magic_name__										=						args.prefix if args.prefix else args.padding_text
                __magic_name__										=						tokenizer.encode(prefix + prompt_text   ,    add_special_tokens=UpperCAmelCase_   ,    return_tensors='pt'     )
         __magic_name__										=						encoded_prompt.to(args.device     )
  
         if encoded_prompt.size()[-1] == 0:
                __magic_name__										=						None
         else:
                __magic_name__										=						encoded_prompt
  
         if args.jit:
                __magic_name__										=						['''enable jit''']
                __magic_name__										=						prepare_jit_inputs(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_     )
                torch._C._jit_set_texpr_fuser_enabled(UpperCAmelCase_     )
                __magic_name__										=						False
                if hasattr(UpperCAmelCase_   ,    'forward'     ):
                       __magic_name__										=						inspect.signature(model.forward     )
                else:
                       __magic_name__										=						inspect.signature(model.__call__     )
                __magic_name__										=						tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCAmelCase_   ,    UpperCAmelCase_     ) is not None     )
                __magic_name__										=						torch.jit.trace(UpperCAmelCase_   ,    UpperCAmelCase_   ,    strict=UpperCAmelCase_     )
                __magic_name__										=						torch.jit.freeze(traced_model.eval()     )
                traced_model(*UpperCAmelCase_     )
                traced_model(*UpperCAmelCase_     )
  
                __magic_name__										=						_ModelFallbackWrapper(UpperCAmelCase_   ,    UpperCAmelCase_     )
  
         __magic_name__										=						model.generate(
             input_ids=UpperCAmelCase_   ,    max_length=args.length + len(encoded_prompt[0]     )   ,    temperature=args.temperature   ,    top_k=args.k   ,    top_p=args.p   ,    repetition_penalty=args.repetition_penalty   ,    do_sample=UpperCAmelCase_   ,    num_return_sequences=args.num_return_sequences   ,    )
  
         # Remove the batch dimension when returning multiple sequences
         if len(output_sequences.shape     ) > 2:
                output_sequences.squeeze_()
  
         __magic_name__										=						[]
  
         for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase_     ):
                print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
                __magic_name__										=						generated_sequence.tolist()
  
                # Decode text
                __magic_name__										=						tokenizer.decode(UpperCAmelCase_   ,    clean_up_tokenization_spaces=UpperCAmelCase_     )
  
                # Remove all text after the stop token
                __magic_name__										=						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
                __magic_name__										=						(
                    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]   ,    clean_up_tokenization_spaces=UpperCAmelCase_     )     ) :]
                )
  
                generated_sequences.append(UpperCAmelCase_     )
                print(UpperCAmelCase_     )
  
         return generated_sequences
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case   :   int    		=		logging.get_logger(__name__)
  
  snake_case   :   List[str]    		=		{
      """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
      # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """nystromformer"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						hidden_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_act
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						initializer_range
             __magic_name__										=						type_vocab_size
             __magic_name__										=						segment_means_seq_len
             __magic_name__										=						num_landmarks
             __magic_name__										=						conv_kernel_size
             __magic_name__										=						inv_coeff_init_option
             __magic_name__										=						layer_norm_eps
             super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )