






"""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


lowercase           =							{
    '''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'''},
}

lowercase           =							[
    '''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						(	):

						'''simple docstring'''






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

						__magic_name__      =  parser.parse_args()

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

						return args






def _lowerCAmelCase						(	):

						'''simple docstring'''






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

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

						__magic_name__      =  start_measure()
						__magic_name__      =  model_cls.from_pretrained(args.model_name			,    device_map="auto"			,    **__lowerCamelCase )
						__magic_name__      =  end_measure(__lowerCamelCase )
						log_measures(__lowerCamelCase			,    "Model loading" )

						__magic_name__      =  compute_module_sizes(__lowerCamelCase )
						__magic_name__      =  {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]
						__magic_name__      =  "\n".join([f'''- {device}: {size // 2**2_0}MiB''' for device, size in device_size.items()] )
						print(f'''\nTheoretical use:\n{message}''' )

						__magic_name__      =  AutoTokenizer.from_pretrained(args.tokenizer_name )

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

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

						__magic_name__      =  end_measure(__lowerCamelCase )
						log_measures(__lowerCamelCase			,    "Model generation" )

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


if __name__ == "__main__":
			main()











"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



