





'''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


A_				     =							{
    "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"},
}

A_				     =							[
    "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							(      )  ->				str:


  '''simple docstring'''




  __lowerCAmelCase		:    List[str]			     =  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'					)

  __lowerCAmelCase		:    Any			     =  parser.parse_args()

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

  return args




def       A							(      )  ->				List[Any]:


  '''simple docstring'''




  transformers.utils.logging.set_verbosity_error()
  __lowerCAmelCase		:    Tuple			     =  parse_args()

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

  __lowerCAmelCase		:    int			     =  start_measure()
  __lowerCAmelCase		:    List[str]			     =  model_cls.from_pretrained(args.model_name						,device_map='auto'						,**_UpperCAmelCase					)
  __lowerCAmelCase		:    List[str]			     =  end_measure(_UpperCAmelCase					)
  log_measures(_UpperCAmelCase						,'Model loading'					)

  __lowerCAmelCase		:    List[Any]			     =  compute_module_sizes(_UpperCAmelCase					)
  __lowerCAmelCase		:    List[str]			     =  {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]
  __lowerCAmelCase		:    Dict			     =  '\n'.join([F"""- {device}: {size // 2**2_0}MiB""" for device, size in device_size.items()]					)
  print(F"""\nTheoretical use:\n{message}"""					)

  __lowerCAmelCase		:    int			     =  AutoTokenizer.from_pretrained(args.tokenizer_name					)

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

    __lowerCAmelCase		:    Optional[Any]			     =  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"""					)

  __lowerCAmelCase		:    List[str]			     =  end_measure(_UpperCAmelCase					)
  log_measures(_UpperCAmelCase						,'Model generation'					)

  __lowerCAmelCase		:    Optional[int]			     =  [gen / tok for gen, tok in zip(_UpperCAmelCase						,_UpperCAmelCase					)]
  __lowerCAmelCase		:    Dict			     =  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"""					)
  __lowerCAmelCase		:    Union[str, Any]			     =  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'''


from typing import Any

import numpy as np


class 		UpperCamelCase__     :
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  None:
        # Check if RHS is negative
        if np.any(tableau[:, -1]     , where=tableau[:, -1] < 0 ):
          raise ValueError('RHS must be > 0' )

        __lowerCAmelCase		:    Any			     =  tableau
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Optional[int]			     =  tableau.shape

        # Number of decision variables x1, x2, x3...
        __lowerCAmelCase		:    Any			     =  n_vars

        # Number of artificial variables to be minimised
        __lowerCAmelCase		:    str			     =  len(np.where(tableau[self.n_vars : -1] == -1 )[0] )

        # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
        __lowerCAmelCase		:    Union[str, Any]			     =  (self.n_art_vars > 0) + 1

        # Number of slack variables added to make inequalities into equalities
        __lowerCAmelCase		:    Any			     =  self.n_rows - self.n_stages

        # Objectives for each stage
        __lowerCAmelCase		:    List[Any]			     =  ['max']

        # In two stage simplex, first minimise then maximise
        if self.n_art_vars:
          self.objectives.append('min' )

        __lowerCAmelCase		:    Optional[Any]			     =  ['']

        # Index of current pivot row and column
        __lowerCAmelCase		:    List[Any]			     =  None
        __lowerCAmelCase		:    Dict			     =  None

        # Does objective row only contain (non)-negative values?
        __lowerCAmelCase		:    Any			     =  False




      @staticmethod
      def 	snake_case  (     *SCREAMING_SNAKE_CASE )				->  list[str]:
        if len(SCREAMING_SNAKE_CASE ) != 3:
          raise ValueError('Must provide n_vars, n_slack, and n_art_vars' )

        if not all(x >= 0 and isinstance(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) for x in args ):
          raise ValueError('All arguments must be non-negative integers' )

        # decision | slack | artificial
        __lowerCAmelCase		:    int			     =  ['x', 's', 'a']
        __lowerCAmelCase		:    Tuple			     =  []
        for i in range(3 ):
          for j in range(args[i] ):
            titles.append(string_starts[i] + str(j + 1 ) )
        titles.append('RHS' )
        return titles




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  tuple[Any, Any]:
        __lowerCAmelCase		:    List[Any]			     =  self.objectives[-1]

        # Find entries of highest magnitude in objective rows
        __lowerCAmelCase		:    List[Any]			     =  (objective == 'min') - (objective == 'max')
        __lowerCAmelCase		:    Dict			     =  np.argmax(sign * tableau[0, : self.n_vars] )

        # Choice is only valid if below 0 for maximise, and above for minimise
        if sign * self.tableau[0, col_idx] <= 0:
          __lowerCAmelCase		:    Dict			     =  True
          return 0, 0

        # Pivot row is chosen as having the lowest quotient when elements of
        # the pivot column divide the right-hand side

        # Slice excluding the objective rows
        __lowerCAmelCase		:    str			     =  slice(self.n_stages     , self.n_rows )

        # RHS
        __lowerCAmelCase		:    Union[str, Any]			     =  tableau[s, -1]

        # Elements of pivot column within slice
        __lowerCAmelCase		:    Optional[Any]			     =  tableau[s, col_idx]

        # Array filled with nans
        __lowerCAmelCase		:    Optional[Any]			     =  np.full(self.n_rows - self.n_stages     , np.nan )

        # If element in pivot column is greater than zeron_stages, return
        # quotient or nan otherwise
        __lowerCAmelCase		:    Tuple			     =  np.divide(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , out=SCREAMING_SNAKE_CASE     , where=divisor > 0 )

        # Arg of minimum quotient excluding the nan values. n_stages is added
        # to compensate for earlier exclusion of objective columns
        __lowerCAmelCase		:    Any			     =  np.nanargmin(SCREAMING_SNAKE_CASE ) + self.n_stages
        return row_idx, col_idx




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  np.ndarray:
        __lowerCAmelCase		:    Tuple			     =  tableau[row_idx].copy()

        __lowerCAmelCase		:    Optional[int]			     =  piv_row[col_idx]

        # Entry becomes 1
        piv_row *= 1 / piv_val

        # Variable in pivot column becomes basic, ie the only non-zero entry
        for idx, coeff in enumerate(tableau[:, col_idx] ):
          tableau[idx] += -coeff * piv_row
        __lowerCAmelCase		:    str			     =  piv_row
        return tableau




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  np.ndarray:
        self.objectives.pop()

        if not self.objectives:
          return tableau

        # Slice containing ids for artificial columns
        __lowerCAmelCase		:    Optional[Any]			     =  slice(-self.n_art_vars - 1     , -1 )

        # Delete the artificial variable columns
        __lowerCAmelCase		:    str			     =  np.delete(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , axis=1 )

        # Delete the objective row of the first stage
        __lowerCAmelCase		:    Optional[int]			     =  np.delete(SCREAMING_SNAKE_CASE     , 0     , axis=0 )

        __lowerCAmelCase		:    Tuple			     =  1
        self.n_rows -= 1
        __lowerCAmelCase		:    str			     =  0
        __lowerCAmelCase		:    Any			     =  False
        return tableau




      def 	snake_case  (     self )				->  dict[Any, Any]:
        # Stop simplex algorithm from cycling.
        for _ in range(1_00 ):
          # Completion of each stage removes an objective. If both stages
          # are complete, then no objectives are left
          if not self.objectives:
            __lowerCAmelCase		:    Any			     =  self.generate_col_titles(
                self.n_vars     , self.n_slack     , self.n_art_vars )

            # Find the values of each variable at optimal solution
            return self.interpret_tableau(self.tableau     , self.col_titles )

          __lowerCAmelCase     ,					__lowerCAmelCase		:    Tuple			     =  self.find_pivot(self.tableau )

          # If there are no more negative values in objective row
          if self.stop_iter:
            # Delete artificial variable columns and rows. Update attributes
            __lowerCAmelCase		:    Dict			     =  self.change_stage(self.tableau )
          else:
            __lowerCAmelCase		:    Optional[Any]			     =  self.pivot(self.tableau     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return {}







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  dict[str, float]:
        __lowerCAmelCase		:    List[Any]			     =  {'P': abs(tableau[0, -1] )}

        for i in range(self.n_vars ):
          # Gives ids of nonzero entries in the ith column
          __lowerCAmelCase		:    str			     =  np.nonzero(tableau[:, i] )
          __lowerCAmelCase		:    Union[str, Any]			     =  len(nonzero[0] )

          # First entry in the nonzero ids
          __lowerCAmelCase		:    Optional[Any]			     =  nonzero[0][0]
          __lowerCAmelCase		:    str			     =  tableau[nonzero_rowidx, i]

          # If there is only one nonzero value in column, which is one
          if n_nonzero == nonzero_val == 1:
            __lowerCAmelCase		:    Dict			     =  tableau[nonzero_rowidx, -1]
            __lowerCAmelCase		:    List[Any]			     =  rhs_val

        # Check for basic variables
        for title in col_titles:
          # Don't add RHS or slack variables to output dict
          if title[0] not in "R-s-a":
            output_dict.setdefault(SCREAMING_SNAKE_CASE     , 0 )
        return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()





