code: 
  
  
  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,
  )
  lowerCAmelCase__   	=				logging.getLogger(__name__)
  
  lowerCAmelCase__   	=				int(1_00_00)  # Hardcoded max length to avoid infinite loop
  
  lowerCAmelCase__   	=				{
      '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
  lowerCAmelCase__   	=				'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 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]		):
         np.random.seed(args.seed		)
         torch.manual_seed(args.seed		)
         if args.n_gpu > 0:
                torch.cuda.manual_seed_all(args.seed		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Dict		):
         if args.temperature > 0.7:
                logger.info("CTRL typically works better with lower temperatures (and lower top_k)."		)
  
         _A   : int       					=				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 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						List[str]		):
         # kwargs = {"language": None, "mask_token_id": None}
  
         # Set the language
         _A   : Union[str, Any]       					=				hasattr(model.config       ,							"use_lang_emb"		) and model.config.use_lang_emb
         if hasattr(model.config       ,							"lang2id"		) and use_lang_emb:
                _A   : Optional[Any]       					=				model.config.langaid.keys()
                if args.xlm_language in available_languages:
                       _A   : Tuple       					=				args.xlm_language
                else:
                       _A   : Optional[int]       					=				None
                       while language not in available_languages:
                              _A   : Tuple       					=				input("Using XLM. Select language in " + str(list(UpperCamelCase__		)		) + " >>> "		)
  
                _A   : List[Any]       					=				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 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						List[Any]		):
         _A   : List[Any]       					=				args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
         _A   : Optional[int]       					=				prefix + prompt_text
         return prompt_text
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Tuple		):
         _A   : Optional[Any]       					=				args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
         _A   : Any       					=				prefix + prompt_text
         return prompt_text
  
  
  lowerCAmelCase__   	=				{
      'ctrl': prepare_ctrl_input,
      'xlm': prepare_xlm_input,
      'xlnet': prepare_xlnet_input,
      'transfo-xl': prepare_transfoxl_input,
  }
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int		):
         if length < 0 and max_sequence_length > 0:
                _A   : Optional[int]       					=				max_sequence_length
         elif 0 < max_sequence_length < length:
                _A   : Tuple       					=				max_sequence_length  # No generation bigger than model size
         elif length < 0:
                _A   : Optional[Any]       					=				MAX_LENGTH  # avoid infinite loop
         return length
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Tuple		):
         _A   : Dict       					=				None
         if hasattr(UpperCamelCase__       ,							"hidden_size"		):
                _A   : str       					=				model_config.hidden_size
         elif hasattr(UpperCamelCase__       ,							"n_embed"		):
                _A   : List[str]       					=				model_config.n_embed
         elif hasattr(UpperCamelCase__       ,							"n_embd"		):
                _A   : Optional[Any]       					=				model_config.n_embd
  
         _A   : Optional[Any]       					=				None
         if hasattr(UpperCamelCase__       ,							"num_attention_heads"		):
                _A   : Tuple       					=				model_config.num_attention_heads
         elif hasattr(UpperCamelCase__       ,							"n_head"		):
                _A   : Any       					=				model_config.n_head
  
         if embedding_size is None or num_head is None or num_head == 0:
                raise ValueError("Check the model config"		)
  
         _A   : int       					=				int(embedding_size / num_head		)
         if hasattr(UpperCamelCase__       ,							"n_layer"		):
                _A   : Tuple       					=				model_config.n_layer
         elif hasattr(UpperCamelCase__       ,							"num_hidden_layers"		):
                _A   : str       					=				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 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Tuple		):
         _A					,    _A					,    _A   : Optional[int]       					=				sparse_model_config(model.config		)
         if model.config.model_type == "bloom":
                _A   : Any       					=				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:
                _A   : Union[str, Any]       					=				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 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Any		):
         _A   : List[str]       					=				len(UpperCamelCase__		)
         _A   : Tuple       					=				tokenizer.batch_encode_plus(UpperCamelCase__       ,							return_tensors="pt"		)
         _A   : Any       					=				dummy_input.to(model.device		)
         if model.config.use_cache:
                _A   : Tuple       					=				generate_past_key_values(UpperCamelCase__       ,							UpperCamelCase__       ,							1		)
         _A   : Any       					=				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   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				("_optimized", "_default")
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[Any]:
              _A   : Optional[int]       					=				optimized
              _A   : Optional[Any]       					=				default
  
  
       def __call__(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       str:
              if kwargs["past_key_values"] is None and self._default.config.use_cache:
                     _A   : Optional[Any]       					=				generate_past_key_values(self._default				,		kwargs["input_ids"].shape[0]				,		0)
              kwargs.pop("position_ids"				,		__lowerCamelCase)
              for k in list(kwargs.keys()):
                     if kwargs[k] is None or isinstance(kwargs[k]				,		__lowerCamelCase):
                            kwargs.pop(__lowerCamelCase)
              _A   : List[Any]       					=				self._optimized(**__lowerCamelCase)
              _A   : Any       					=				outputs[0]
              _A   : Any       					=				outputs[1]
              _A   : Tuple       					=				CausalLMOutputWithPast(
                  loss=__lowerCamelCase				,		logits=__lowerCamelCase				,		past_key_values=__lowerCamelCase				,		hidden_states=__lowerCamelCase				,		attentions=__lowerCamelCase				,		)
              return fixed_output
  
  
       def __getattr__(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
              return getattr(self._default				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       int:
              return self._default.prepare_inputs_for_generation(
                  __lowerCamelCase				,		past_key_values=__lowerCamelCase				,		inputs_embeds=__lowerCamelCase				,		use_cache=__lowerCamelCase				,		**__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Tuple[Tuple[torch.Tensor]]:
              return self._default._reorder_cache(__lowerCamelCase				,		__lowerCamelCase)
  
  
  
  
  def 							_UpperCAmelCase       ():
         _A   : Union[str, Any]       					=				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=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("--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("--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"		)
         _A   : Union[str, Any]       					=				parser.parse_args()
  
         _A   : List[str]       					=				torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
         _A   : 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
         try:
                _A   : Tuple       					=				args.model_type.lower()
                _A					,    _A   : str       					=				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 :)"		)
  
         _A   : Tuple       					=				tokenizer_class.from_pretrained(args.model_name_or_path		)
         if tokenizer.pad_token is None:
                _A   : str       					=				tokenizer.eos_token
         _A   : Union[str, Any]       					=				model_class.from_pretrained(args.model_name_or_path		)
         model.to(args.device		)
  
         if args.fpaa:
                model.half()
         _A   : Dict       					=				getattr(model.config       ,							"max_position_embeddings"       ,							0		)
         _A   : List[Any]       					=				adjust_length_to_model(args.length       ,							max_sequence_length=UpperCamelCase__		)
         logger.info(UpperCamelCase__		)
  
         _A   : List[Any]       					=				args.prompt if args.prompt else input("Model prompt >>> "		)
  
         # Different models need different input formatting and/or extra arguments
         _A   : List[str]       					=				args.model_type in PREPROCESSING_FUNCTIONS.keys()
         if requires_preprocessing:
                _A   : List[Any]       					=				PREPROCESSING_FUNCTIONS.get(args.model_type		)
                _A   : Tuple       					=				prepare_input(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
                if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                       _A   : Tuple       					=				{"add_space_before_punct_symbol": True}
                else:
                       _A   : Dict       					=				{}
  
                _A   : List[Any]       					=				tokenizer.encode(
                    UpperCamelCase__       ,							add_special_tokens=UpperCamelCase__       ,							return_tensors="pt"       ,							**UpperCamelCase__		)
         else:
                _A   : int       					=				args.prefix if args.prefix else args.padding_text
                _A   : Any       					=				tokenizer.encode(prefix + prompt_text       ,							add_special_tokens=UpperCamelCase__       ,							return_tensors="pt"		)
         _A   : str       					=				encoded_prompt.to(args.device		)
  
         if encoded_prompt.size()[-1] == 0:
                _A   : Union[str, Any]       					=				None
         else:
                _A   : Any       					=				encoded_prompt
  
         if args.jit:
                _A   : Union[str, Any]       					=				["enable jit"]
                _A   : List[Any]       					=				prepare_jit_inputs(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__		)
                _A   : Union[str, Any]       					=				False
                if hasattr(UpperCamelCase__       ,							"forward"		):
                       _A   : List[Any]       					=				inspect.signature(model.forward		)
                else:
                       _A   : Union[str, Any]       					=				inspect.signature(model.__call__		)
                _A   : str       					=				tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__       ,							UpperCamelCase__		) is not None		)
                _A   : Any       					=				torch.jit.trace(UpperCamelCase__       ,							UpperCamelCase__       ,							strict=UpperCamelCase__		)
                _A   : Dict       					=				torch.jit.freeze(traced_model.eval()		)
                traced_model(*UpperCamelCase__		)
                traced_model(*UpperCamelCase__		)
  
                _A   : int       					=				_ModelFallbackWrapper(UpperCamelCase__       ,							UpperCamelCase__		)
  
         _A   : Union[str, Any]       					=				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_()
  
         _A   : List[str]       					=				[]
  
         for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__		):
                print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="		)
                _A   : Optional[Any]       					=				generated_sequence.tolist()
  
                # Decode text
                _A   : List[Any]       					=				tokenizer.decode(UpperCamelCase__       ,							clean_up_tokenization_spaces=UpperCamelCase__		)
  
                # Remove all text after the stop token
                _A   : int       					=				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
                _A   : int       					=				(
                    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: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetrFeatureExtractor']
      lowerCAmelCase__   	=				['DetrImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetrForObjectDetection',
          'DetrForSegmentation',
          'DetrModel',
          'DetrPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_detr import DetrFeatureExtractor
          from .image_processing_detr import DetrImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_detr import (
              DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetrForObjectDetection,
              DetrForSegmentation,
              DetrModel,
              DetrPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  