

'''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


lowerCamelCase				:   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"},
}

lowerCamelCase				:   Tuple      				=  [
    "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  _lowerCAmelCase      (		)   ->    List[str]:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE												=parser.parse_args()

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

     return args


def  _lowerCAmelCase      (		)   ->    str:
     """simple docstring"""




     transformers.utils.logging.set_verbosity_error()
     _SCREAMING_SNAKE_CASE												=parse_args()

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

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

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

     _SCREAMING_SNAKE_CASE												=AutoTokenizer.from_pretrained(args.tokenizer_name       )

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

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

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

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


if __name__ == "__main__":
     main()



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  Optional[Any]       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=len(_UpperCamelCase       )
     # Create a stack and push index of fist element to it
     _SCREAMING_SNAKE_CASE												=[]
     st.append(0       )

     # Span value of first element is always 1
     _SCREAMING_SNAKE_CASE												=1

     # Calculate span values for rest of the elements
     for i in range(1							,				_UpperCamelCase       ):
          # Pop elements from stack while stack is not
          # empty and top of stack is smaller than price[i]
          while len(_UpperCamelCase       ) > 0 and price[st[0]] <= price[i]:
               st.pop()

          # If stack becomes empty, then price[i] is greater
          # than all elements on left of it, i.e. price[0],
          # price[1], ..price[i-1]. Else the price[i]  is
          # greater than elements after top of stack
          _SCREAMING_SNAKE_CASE												=i + 1 if len(_UpperCamelCase       ) <= 0 else (i - st[0])

          # Push this element to stack
          st.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  str       )   ->    Any:
     """simple docstring"""




     for i in range(0							,				_UpperCamelCase       ):
          print(arr[i]							,				end=' '       )


# Driver program to test above function
lowerCamelCase				:   str      				=  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
lowerCamelCase				:   str      				=  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)







print_array(S, len(price))
