






"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterable




class  lowercase_     :






      def __init__(     self  ,  a_  ,  a_						)						->Optional[Any]:







        '''simple docstring'''


        _a      ,						_a					      =						float(a_						), float(a_						)






      def __eq__(     self  ,  a_						)						->Tuple:







        '''simple docstring'''


        return self.x == other.x and self.y == other.y






      def __ne__(     self  ,  a_						)						->str:







        '''simple docstring'''


        return not self == other






      def __gt__(     self  ,  a_						)						->Optional[Any]:







        '''simple docstring'''


        if self.x > other.x:
          return True
        elif self.x == other.x:
          return self.y > other.y
        return False






      def __lt__(     self  ,  a_						)						->List[str]:







        '''simple docstring'''


        return not self > other






      def __ge__(     self  ,  a_						)						->List[Any]:







        '''simple docstring'''


        if self.x > other.x:
          return True
        elif self.x == other.x:
          return self.y >= other.y
        return False






      def __le__(     self  ,  a_						)						->Any:







        '''simple docstring'''


        if self.x < other.x:
          return True
        elif self.x == other.x:
          return self.y <= other.y
        return False






      def __repr__(     self						)						->List[Any]:







        '''simple docstring'''


        return f'''({self.x}, {self.y})'''






      def __hash__(     self						)						->List[str]:







        '''simple docstring'''


        return hash(self.x						)







def 							lowerCAmelCase			(   UpperCamelCase_:				list[Point] | list[list[float]] | Iterable[list[float]]  ,			)							->				list[Point]:







  '''simple docstring'''







  _a					      =						[]
  if list_of_tuples:
    for p in list_of_tuples:
      if isinstance(UpperCamelCase_  ,			UpperCamelCase_       ):
        points.append(UpperCamelCase_       )
      else:
        try:
          points.append(Point(p[0]  ,			p[1]       )       )
        except (IndexError, TypeError):
          print(
              f'''Ignoring deformed point {p}. All points'''
              " must have at least 2 coordinates."       )
  return points






def 							lowerCAmelCase			(   UpperCamelCase_:				list[Point] | list[list[float]]       )							->				list[Point]:







  '''simple docstring'''







  if not hasattr(UpperCamelCase_  ,			"__iter__"       ):
    _a					      =						f'''Expecting an iterable object but got an non-iterable type {points}'''
    raise ValueError(UpperCamelCase_       )

  if not points:
    _a					      =						f'''Expecting a list of points but got {points}'''
    raise ValueError(UpperCamelCase_       )

  return _construct_points(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				Point  ,			UpperCamelCase_:				Point  ,			UpperCamelCase_:				Point       )							->				float:







  '''simple docstring'''







  _a					      =						(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
  return det






def 							lowerCAmelCase			(   UpperCamelCase_:				list[Point]       )							->				list[Point]:







  '''simple docstring'''







  _a					      =						sorted(_validate_input(UpperCamelCase_       )       )
  _a					      =						len(UpperCamelCase_       )
  _a					      =						set()

  for i in range(n - 1       ):
    for j in range(i + 1  ,			UpperCamelCase_       ):
      _a					      =						_a					      =						False
      _a					      =						True
      for k in range(UpperCamelCase_       ):
        if k != i and k != j:
          _a					      =						_det(points[i]  ,			points[j]  ,			points[k]       )

          if det_k > 0:
            _a					      =						True
          elif det_k < 0:
            _a					      =						True
          else:
            # point[i], point[j], point[k] all lie on a straight line
            # if point[k] is to the left of point[i] or it's to the
            # right of point[j], then point[i], point[j] cannot be
            # part of the convex hull of A
            if points[k] < points[i] or points[k] > points[j]:
              _a					      =						False
              break

        if points_left_of_ij and points_right_of_ij:
          _a					      =						False
          break

      if ij_part_of_convex_hull:
        convex_set.update([points[i], points[j]]       )

  return sorted(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				list[Point]       )							->				list[Point]:







  '''simple docstring'''







  _a					      =						sorted(_validate_input(UpperCamelCase_       )       )
  _a					      =						len(UpperCamelCase_       )

  # divide all the points into an upper hull and a lower hull
  # the left most point and the right most point are definitely
  # members of the convex hull by definition.
  # use these two anchors to divide all the points into two hulls,
  # an upper hull and a lower hull.

  # all points to the left (above) the line joining the extreme points belong to the
  # upper hull
  # all points to the right (below) the line joining the extreme points below to the
  # lower hull
  # ignore all points on the line joining the extreme points since they cannot be
  # part of the convex hull

  _a					      =						points[0]
  _a					      =						points[n - 1]

  _a					      =						{left_most_point, right_most_point}
  _a					      =						[]
  _a					      =						[]

  for i in range(1  ,			n - 1       ):
    _a					      =						_det(UpperCamelCase_  ,			UpperCamelCase_  ,			points[i]       )

    if det > 0:
      upper_hull.append(points[i]       )
    elif det < 0:
      lower_hull.append(points[i]       )

  _construct_hull(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )
  _construct_hull(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )

  return sorted(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				list[Point]  ,			UpperCamelCase_:				Point  ,			UpperCamelCase_:				Point  ,			UpperCamelCase_:				set[Point]       )							->				None:







  '''simple docstring'''







  if points:
    _a					      =						None
    _a					      =						float("-inf"       )
    _a					      =						[]

    for p in points:
      _a					      =						_det(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )

      if det > 0:
        candidate_points.append(UpperCamelCase_       )

        if det > extreme_point_distance:
          _a					      =						det
          _a					      =						p

    if extreme_point:
      _construct_hull(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )
      convex_set.add(UpperCamelCase_       )
      _construct_hull(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				list[Point]       )							->				list[Point]:







  '''simple docstring'''







  _a					      =						sorted(_validate_input(UpperCamelCase_       )       )
  _a					      =						len(UpperCamelCase_       )

  _a					      =						points[:2]
  for i in range(2  ,			UpperCamelCase_       ):
    _a					      =						_det(convex_hull[1]  ,			convex_hull[0]  ,			points[i]       )
    if det > 0:
      convex_hull.insert(0  ,			points[i]       )
      break
    elif det < 0:
      convex_hull.append(points[i]       )
      break
    else:
      _a					      =						points[i]
  i += 1

  for j in range(UpperCamelCase_  ,			UpperCamelCase_       ):
    if (
        _det(convex_hull[0]  ,			convex_hull[-1]  ,			points[j]       ) > 0
        and _det(convex_hull[-1]  ,			convex_hull[0]  ,			points[1]       ) < 0
    ):
      # The point lies within the convex hull
      continue

    convex_hull.insert(0  ,			points[j]       )
    convex_hull.append(points[j]       )
    while _det(convex_hull[0]  ,			convex_hull[1]  ,			convex_hull[2]       ) >= 0:
      del convex_hull[1]
    while _det(convex_hull[-1]  ,			convex_hull[-2]  ,			convex_hull[-3]       ) <= 0:
      del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
  return sorted(convex_hull[1:] if len(UpperCamelCase_       ) > 3 else convex_hull       )






def 							lowerCAmelCase			(   )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						[
      (0, 3),
      (2, 2),
      (1, 1),
      (2, 1),
      (3, 0),
      (0, 0),
      (3, 3),
      (2, -1),
      (2, -4),
      (1, -3),
  ]
  # the convex set of points is
  # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
  _a					      =						convex_hull_bf(UpperCamelCase_       )

  _a					      =						convex_hull_recursive(UpperCamelCase_       )
  assert results_bf == results_recursive

  _a					      =						convex_hull_melkman(UpperCamelCase_       )
  assert results_bf == results_melkman

  print(UpperCamelCase_       )


if __name__ == "__main__":
     main()










"""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


UpperCamelCase											=     {
    """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"""},
}

UpperCamelCase											=     [
    """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			(   )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						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"       )

  _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 							lowerCAmelCase			(   )							->				str:







  '''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(UpperCamelCase_  ,			"torch_dtype"  ,			torch.floataa       )
  else:
    _a					      =						getattr(UpperCamelCase_  ,			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"  ,			**UpperCamelCase_       )
  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model loading"       )

  _a					      =						compute_module_sizes(UpperCamelCase_       )
  _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**20}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(UpperCamelCase_  ,			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(UpperCamelCase_       ) if outputs[: len(UpperCamelCase_       )] != tokens else len(UpperCamelCase_       ) - len(UpperCamelCase_       )
    _a					      =						after_generate - before_generate

    _a					      =						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'''       )

  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model generation"       )

  _a					      =						[gen / tok for gen, tok in zip(UpperCamelCase_  ,			UpperCamelCase_       )]
  _a					      =						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'''       )
  _a					      =						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()


