






"""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 							A(    ):
       """simple docstring"""





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

       lowercase__:							Optional[Any]         =     parser.parse_args()

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

       return args






def 							A(    ):
       """simple docstring"""





       transformers.utils.logging.set_verbosity_error()
       lowercase__:							Optional[Any]         =     parse_args()

       if args.torch_dtype is None:
              lowercase__:							Optional[Any]         =     AutoConfig.from_pretrained(args.model_name						)
              lowercase__:							Optional[int]         =     getattr(snake_case_		,  "torch_dtype"		,  torch.floataa						)
       else:
              lowercase__:							Union[str, Any]         =     getattr(snake_case_		,  args.torch_dtype						)
       lowercase__:							Union[str, Any]         =     AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
       lowercase__:							Optional[int]         =     {
           "torch_dtype": torch_dtype,
           "revision": args.model_revision,
       }
       if args.disk_offload:
              lowercase__:							Optional[int]         =     "tmp_offload"
              lowercase__:							Dict         =     True

       lowercase__:							List[Any]         =     start_measure()
       lowercase__:							Optional[int]         =     model_cls.from_pretrained(args.model_name		,  device_map="auto"		,  **snake_case_						)
       lowercase__:							List[Any]         =     end_measure(snake_case_						)
       log_measures(snake_case_		,  "Model loading"						)

       lowercase__:							Tuple         =     compute_module_sizes(snake_case_						)
       lowercase__:							Optional[Any]         =     {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]
       lowercase__:							List[Any]         =     "\n".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
       print(F"""\nTheoretical use:\n{message}"""						)

       lowercase__:							str         =     AutoTokenizer.from_pretrained(args.tokenizer_name						)

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

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

       lowercase__:							Dict         =     end_measure(snake_case_						)
       log_measures(snake_case_		,  "Model generation"						)

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


if __name__ == "__main__":
  main()










from __future__ import annotations

__SCREAMING_SNAKE_CASE	:							Union[str, Any]           =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]


class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    Dict	,							lowerCamelCase_     :    int					):
								_lowerCAmelCase  						=-1
								_lowerCAmelCase  						=-1
								_lowerCAmelCase  						=[-1] * length
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None






				def __repr__(				self     :    Tuple					):
								return F"Node(min_value={self.minn} max_value={self.maxx})"

def 		snake_case_   (				lowercase__     :    list[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=Node(len(lowercase__     )     )
				_lowerCAmelCase						,							_lowerCAmelCase  						=min(lowercase__     ), max(lowercase__     )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_lowerCAmelCase  						=(root.minn + root.maxx) // 2

				_lowerCAmelCase  						=[]
				_lowerCAmelCase  						=[]

				for index, num in enumerate(lowercase__     ):
								if num <= pivot:
												left_arr.append(lowercase__     )
								else:
												right_arr.append(lowercase__     )
								_lowerCAmelCase  						=len(lowercase__     )
				_lowerCAmelCase  						=build_tree(lowercase__     )
				_lowerCAmelCase  						=build_tree(lowercase__     )
				return root

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_lowerCAmelCase  						=(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left       ,     lowercase__       ,     node.map_left[index] - 1     )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right       ,     lowercase__       ,     index - node.map_left[index]     )

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if start > end:
								return 0
				_lowerCAmelCase  						=rank_till_index(lowercase__       ,     lowercase__       ,     lowercase__     )
				_lowerCAmelCase  						=rank_till_index(lowercase__       ,     lowercase__       ,     start - 1     )
				return rank_till_end - rank_before_start

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_lowerCAmelCase  						=node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left       ,     lowercase__       ,     (node.map_left[start - 1] if start else 0)       ,     node.map_left[end] - 1       ,     )
				else:
								return quantile(
								    node.right       ,     index - num_elements_in_left_tree       ,     start - (node.map_left[start - 1] if start else 0)       ,     end - node.map_left[end]       ,     )

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_lowerCAmelCase  						=range_counting(
				    node.left       ,     (node.map_left[start - 1] if start else 0)       ,     node.map_left[end] - 1       ,     lowercase__       ,     lowercase__       ,     )
				_lowerCAmelCase  						=range_counting(
				    node.right       ,     start - (node.map_left[start - 1] if start else 0)       ,     end - node.map_left[end]       ,     lowercase__       ,     lowercase__       ,     )
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()

