code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __SCREAMING_SNAKE_CASE  		=						{
      "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
      "processing_donut": ["DonutProcessor"],
  }
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __SCREAMING_SNAKE_CASE  		=						[
         "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DonutSwinModel",
         "DonutSwinPreTrainedModel",
     ]
  
  try:
     if not is_vision_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __SCREAMING_SNAKE_CASE  		=						["DonutFeatureExtractor"]
     __SCREAMING_SNAKE_CASE  		=						["DonutImageProcessor"]
  
  
  if TYPE_CHECKING:
     from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
     from .processing_donut import DonutProcessor
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_donut_swin import (
            DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            DonutSwinModel,
            DonutSwinPreTrainedModel,
        )
  
     try:
        if not is_vision_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .feature_extraction_donut import DonutFeatureExtractor
        from .image_processing_donut import DonutImageProcessor
  
  else:
     import sys
  
     __SCREAMING_SNAKE_CASE  		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
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()
  