code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import Optional, Union
  
  from ...file_utils import TensorType
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Tuple     	=			'MarkupLMFeatureExtractor'
     lowerCAmelCase_				:   Dict     	=			('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
     lowerCAmelCase_				:   str     	=			True
  
  
  
  
     def __call__(		self							:str				,  UpperCamelCase__							:Union[str, Any]=None				,  UpperCamelCase__							:Any=None				,  UpperCamelCase__							:str=None				,  UpperCamelCase__							:List[Any]=None				,  UpperCamelCase__							:int=None				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Union[bool, str, PaddingStrategy] = False				,  UpperCamelCase__							:Union[bool, str, TruncationStrategy] = None				,  UpperCamelCase__							:Optional[int] = None				,  UpperCamelCase__							:int = 0				,  UpperCamelCase__							:Optional[int] = None				,  UpperCamelCase__							:Optional[bool] = None				,  UpperCamelCase__							:Optional[bool] = None				,  UpperCamelCase__							:bool = False				,  UpperCamelCase__							:bool = False				,  UpperCamelCase__							:bool = False				,  UpperCamelCase__							:bool = False				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Optional[Union[str, TensorType]] = None				,  **UpperCamelCase__							:List[Any]				,  ):
            # first, create nodes and xpaths
            if self.parse_html:
                   if html_strings is None:
                          raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"       )
  
                   if nodes is not None or xpaths is not None or node_labels is not None:
                          raise ValueError(
                              "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"       )
  
                   _a          =				self.feature_extractor(UpperCamelCase__       )
                   _a          =				features["nodes"]
                   _a          =				features["xpaths"]
            else:
                   if html_strings is not None:
                          raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."       )
                   if nodes is None or xpaths is None:
                          raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"       )
  
          # # second, apply the tokenizer
            if questions is not None and self.parse_html:
                   if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                          _a          =				[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)
  
            _a          =				self.tokenizer(
                text=questions if questions is not None else nodes				,  text_pair=nodes if questions is not None else None				,  xpaths=UpperCamelCase__				,  node_labels=UpperCamelCase__				,  add_special_tokens=UpperCamelCase__				,  padding=UpperCamelCase__				,  truncation=UpperCamelCase__				,  max_length=UpperCamelCase__				,  stride=UpperCamelCase__				,  pad_to_multiple_of=UpperCamelCase__				,  return_token_type_ids=UpperCamelCase__				,  return_attention_mask=UpperCamelCase__				,  return_overflowing_tokens=UpperCamelCase__				,  return_special_tokens_mask=UpperCamelCase__				,  return_offsets_mapping=UpperCamelCase__				,  return_length=UpperCamelCase__				,  verbose=UpperCamelCase__				,  return_tensors=UpperCamelCase__				,  **UpperCamelCase__				,  )
  
            return encoded_inputs
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Dict				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
            return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any				,  *UpperCamelCase__							:Union[str, Any]				,  **UpperCamelCase__							:Tuple       ):
            return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            _a          =				self.tokenizer.model_input_names
            return tokenizer_input_names
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  __SCREAMING_SNAKE_CASE					=						{
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  __SCREAMING_SNAKE_CASE					=						[
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."					)
         parser.add_argument("model_name",		type=a,		default=a,		help="The name of the model to try."					)
         parser.add_argument(
             "--tokenizer_name",		type=a,		default=a,		help="The name of the tokenizer (if different from the model."					)
         parser.add_argument("--is_causal",		type=a,		default=a,		help="Whether or not the model is causal."					)
         parser.add_argument(
             "--model_revision",		type=a,		default=a,		help="The revision to use for the model checkpoint."					)
         parser.add_argument("--torch_dtype",		type=a,		default=a,		help="The dtype for the model."					)
         parser.add_argument("--disk_offload",		action="store_true"					)
  
         _a          =				parser.parse_args()
  
         # Sanitize args
         if args.model_name in DEFAULT_MODELS:
                _a          =				DEFAULT_MODELS[args.model_name]
                _a          =				defaults["model"]
                if args.tokenizer_name is None:
                       _a          =				defaults.get("tokenizer",		args.model_name					)
                if args.is_causal is None:
                       _a          =				defaults["is_causal"]
                if args.model_revision is None:
                       _a          =				defaults.get("model_revision",		"main"					)
  
         if args.is_causal is None:
                raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."					)
         if args.tokenizer_name is None:
                _a          =				args.model_name
         if args.model_revision is None:
                _a          =				"main"
  
         return args
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         transformers.utils.logging.set_verbosity_error()
         _a          =				parse_args()
  
         if args.torch_dtype is None:
                _a          =				AutoConfig.from_pretrained(args.model_name					)
                _a          =				getattr(a,		"torch_dtype",		torch.floataa					)
         else:
                _a          =				getattr(a,		args.torch_dtype					)
         _a          =				AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
         _a          =				{
             "torch_dtype": torch_dtype,
             "revision": args.model_revision,
         }
         if args.disk_offload:
                _a          =				"tmp_offload"
                _a          =				True
  
         _a          =				start_measure()
         _a          =				model_cls.from_pretrained(args.model_name,		device_map="auto",		**a					)
         _a          =				end_measure(a					)
         log_measures(a,		"Model loading"					)
  
         _a          =				compute_module_sizes(a					)
         _a          =				{v: 0 for v in model.hf_device_map.values()}
         for module, device in model.hf_device_map.items():
                device_size[device] += module_sizes[module]
         _a          =				"\n".join([F'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]					)
         print(F'\nTheoretical use:\n{message}'					)
  
         _a          =				AutoTokenizer.from_pretrained(args.tokenizer_name					)
  
         _a          =				start_measure()
         _a          =				[]
         _a          =				[]
         _a          =				[]
         for prompt in PROMPTS:
                _a          =				tokenizer(a,		return_tensors="pt"					).to(0					)
                _a          =				inputs["input_ids"][0].tolist()
                _a          =				time.time()
                _a          =				model.generate(inputs["input_ids"]					)
                _a          =				time.time()
                _a          =				outputs[0].tolist()
                _a          =				len(a					) if outputs[: len(a					)] != tokens else len(a					) - len(a					)
                _a          =				after_generate - before_generate
  
                _a          =				tokenizer.decode(a,		skip_special_tokens=a					)
                texts_outs.append(a					)
                generation_times.append(a					)
                gen_tokens.append(a					)
                print(F'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'					)
  
         _a          =				end_measure(a					)
         log_measures(a,		"Model generation"					)
  
         _a          =				[gen / tok for gen, tok in zip(a,		a					)]
         _a          =				sum(a					) / len(a					)
         print(F'Average time of generation per token: {avg_gen:.2f}s'					)
         print(F'First generation (avg time per token): {generation_times_per_token[0]:.2f}s'					)
         _a          =				sum(generation_times_per_token[1:]					) / (len(a					) - 1)
         print(F'Average time of generation per token (excluding the first): {avg_gen:.2f}s'					)
  
  
  if __name__ == "__main__":
     main()
  