






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


__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"""},
}

__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       (    ):
      UpperCamelCase	:Any     							=       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'''	)

      UpperCamelCase	:List[str]     							=       parser.parse_args()

      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
            UpperCamelCase	:str     							=       DEFAULT_MODELS[args.model_name]
            UpperCamelCase	:List[Any]     							=       defaults['''model''']
            if args.tokenizer_name is None:
                  UpperCamelCase	:Optional[int]     							=       defaults.get('''tokenizer'''					,  args.model_name	)
            if args.is_causal is None:
                  UpperCamelCase	:Union[str, Any]     							=       defaults['''is_causal''']
            if args.model_revision is None:
                  UpperCamelCase	:Any     							=       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	:Any     							=       args.model_name
      if args.model_revision is None:
            UpperCamelCase	:int     							=       '''main'''

      return args


def   _A       (    ):
      transformers.utils.logging.set_verbosity_error()
      UpperCamelCase	:int     							=       parse_args()

      if args.torch_dtype is None:
            UpperCamelCase	:List[Any]     							=       AutoConfig.from_pretrained(args.model_name	)
            UpperCamelCase	:int     							=       getattr(_A					,  '''torch_dtype'''					,  torch.floataa	)
      else:
            UpperCamelCase	:str     							=       getattr(_A					,  args.torch_dtype	)
      UpperCamelCase	:Tuple     							=       AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      UpperCamelCase	:Dict     							=       {
          '''torch_dtype''': torch_dtype,
          '''revision''': args.model_revision,
      }
      if args.disk_offload:
            UpperCamelCase	:List[Any]     							=       '''tmp_offload'''
            UpperCamelCase	:Union[str, Any]     							=       True

      UpperCamelCase	:List[Any]     							=       start_measure()
      UpperCamelCase	:Optional[Any]     							=       model_cls.from_pretrained(args.model_name					,  device_map='''auto'''					,  **_A	)
      UpperCamelCase	:Dict     							=       end_measure(_A	)
      log_measures(_A					,  '''Model loading'''	)

      UpperCamelCase	:Any     							=       compute_module_sizes(_A	)
      UpperCamelCase	:Dict     							=       {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	:Tuple     							=       '''\n'''.join([F'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]	)
      print(F'''\nTheoretical use:\n{message}'''	)

      UpperCamelCase	:Optional[int]     							=       AutoTokenizer.from_pretrained(args.tokenizer_name	)

      UpperCamelCase	:List[str]     							=       start_measure()
      UpperCamelCase	:int     							=       []
      UpperCamelCase	:List[str]     							=       []
      UpperCamelCase	:Any     							=       []
      for prompt in PROMPTS:
            UpperCamelCase	:Union[str, Any]     							=       tokenizer(_A					,  return_tensors='''pt'''	).to(0	)
            UpperCamelCase	:Any     							=       inputs['''input_ids'''][0].tolist()
            UpperCamelCase	:int     							=       time.time()
            UpperCamelCase	:Union[str, Any]     							=       model.generate(inputs['''input_ids''']	)
            UpperCamelCase	:Tuple     							=       time.time()
            UpperCamelCase	:List[Any]     							=       outputs[0].tolist()
            UpperCamelCase	:Dict     							=       len(_A	) if outputs[: len(_A	)] != tokens else len(_A	) - len(_A	)
            UpperCamelCase	:List[str]     							=       after_generate - before_generate

            UpperCamelCase	:List[Any]     							=       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'''	)

      UpperCamelCase	:Union[str, Any]     							=       end_measure(_A	)
      log_measures(_A					,  '''Model generation'''	)

      UpperCamelCase	:Tuple     							=       [gen / tok for gen, tok in zip(_A					,  _A	)]
      UpperCamelCase	:Union[str, Any]     							=       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'''	)
      UpperCamelCase	:Optional[Any]     							=       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()













import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





