





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


lowercase     :			List[str]     						=	{
    """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"""},
}

lowercase     :			Union[str, Any]     						=	[
    """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      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.'''       )
       parser.add_argument('''model_name'''     ,							type=lowerCAmelCase     ,							default=lowerCAmelCase     ,							help='''The name of the model to try.'''       )
       parser.add_argument(
           '''--tokenizer_name'''     ,							type=lowerCAmelCase     ,							default=lowerCAmelCase     ,							help='''The name of the tokenizer (if different from the model.'''       )
       parser.add_argument('''--is_causal'''     ,							type=lowerCAmelCase     ,							default=lowerCAmelCase     ,							help='''Whether or not the model is causal.'''       )
       parser.add_argument(
           '''--model_revision'''     ,							type=lowerCAmelCase     ,							default=lowerCAmelCase     ,							help='''The revision to use for the model checkpoint.'''       )
       parser.add_argument('''--torch_dtype'''     ,							type=lowerCAmelCase     ,							default=lowerCAmelCase     ,							help='''The dtype for the model.'''       )
       parser.add_argument('''--disk_offload'''     ,							action='''store_true'''       )

       _UpperCamelCase														=    parser.parse_args()

       # Sanitize args
       if args.model_name in DEFAULT_MODELS:
              _UpperCamelCase														=    DEFAULT_MODELS[args.model_name]
              _UpperCamelCase														=    defaults['''model''']
              if args.tokenizer_name is None:
                     _UpperCamelCase														=    defaults.get('''tokenizer'''     ,							args.model_name       )
              if args.is_causal is None:
                     _UpperCamelCase														=    defaults['''is_causal''']
              if args.model_revision is None:
                     _UpperCamelCase														=    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:
              _UpperCamelCase														=    args.model_name
       if args.model_revision is None:
              _UpperCamelCase														=    '''main'''

       return args


def      SCREAMING_SNAKE_CASE      (   ):
       transformers.utils.logging.set_verbosity_error()
       _UpperCamelCase														=    parse_args()

       if args.torch_dtype is None:
              _UpperCamelCase														=    AutoConfig.from_pretrained(args.model_name       )
              _UpperCamelCase														=    getattr(lowerCAmelCase     ,							'''torch_dtype'''     ,							torch.floataa       )
       else:
              _UpperCamelCase														=    getattr(lowerCAmelCase     ,							args.torch_dtype       )
       _UpperCamelCase														=    AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
       _UpperCamelCase														=    {
           '''torch_dtype''': torch_dtype,
           '''revision''': args.model_revision,
       }
       if args.disk_offload:
              _UpperCamelCase														=    '''tmp_offload'''
              _UpperCamelCase														=    True

       _UpperCamelCase														=    start_measure()
       _UpperCamelCase														=    model_cls.from_pretrained(args.model_name     ,							device_map='''auto'''     ,							**lowerCAmelCase       )
       _UpperCamelCase														=    end_measure(lowerCAmelCase       )
       log_measures(lowerCAmelCase     ,							'''Model loading'''       )

       _UpperCamelCase														=    compute_module_sizes(lowerCAmelCase       )
       _UpperCamelCase														=    {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]
       _UpperCamelCase														=    '''\n'''.join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]       )
       print(f'''\nTheoretical use:\n{message}'''       )

       _UpperCamelCase														=    AutoTokenizer.from_pretrained(args.tokenizer_name       )

       _UpperCamelCase														=    start_measure()
       _UpperCamelCase														=    []
       _UpperCamelCase														=    []
       _UpperCamelCase														=    []
       for prompt in PROMPTS:
              _UpperCamelCase														=    tokenizer(lowerCAmelCase     ,							return_tensors='''pt'''       ).to(0       )
              _UpperCamelCase														=    inputs['''input_ids'''][0].tolist()
              _UpperCamelCase														=    time.time()
              _UpperCamelCase														=    model.generate(inputs['''input_ids''']       )
              _UpperCamelCase														=    time.time()
              _UpperCamelCase														=    outputs[0].tolist()
              _UpperCamelCase														=    len(lowerCAmelCase       ) if outputs[: len(lowerCAmelCase       )] != tokens else len(lowerCAmelCase       ) - len(lowerCAmelCase       )
              _UpperCamelCase														=    after_generate - before_generate

              _UpperCamelCase														=    tokenizer.decode(lowerCAmelCase     ,							skip_special_tokens=lowerCAmelCase       )
              texts_outs.append(lowerCAmelCase       )
              generation_times.append(lowerCAmelCase       )
              gen_tokens.append(lowerCAmelCase       )
              print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''       )

       _UpperCamelCase														=    end_measure(lowerCAmelCase       )
       log_measures(lowerCAmelCase     ,							'''Model generation'''       )

       _UpperCamelCase														=    [gen / tok for gen, tok in zip(lowerCAmelCase     ,							lowerCAmelCase       )]
       _UpperCamelCase														=    sum(lowerCAmelCase       ) / len(lowerCAmelCase       )
       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'''       )
       _UpperCamelCase														=    sum(generation_times_per_token[1:]       ) / (len(lowerCAmelCase       ) - 1)
       print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''       )


if __name__ == "__main__":
       main()













def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    index
       _UpperCamelCase														=    2 * index + 1
       _UpperCamelCase														=    2 * index + 2
       if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
              _UpperCamelCase														=    left_index

       if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
              _UpperCamelCase														=    right_index

       if largest != index:
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[index], unsorted[largest]
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       _UpperCamelCase														=    len(lowerCAmelCase       )
       for i in range(n // 2 - 1     ,							-1     ,							-1       ):
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
       for i in range(n - 1     ,							0     ,							-1       ):
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[i], unsorted[0]
              heapify(lowerCAmelCase     ,							0     ,							lowerCAmelCase       )
       return unsorted


if __name__ == "__main__":
       lowercase     :			Union[str, Any]     						=	input("""Enter numbers separated by a comma:\n""").strip()
       lowercase     :			Dict     						=	[int(item) for item in user_input.split(""",""")]
       print(heap_sort(unsorted))






