
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


SCREAMING_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"""},
}

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

				__lowercase								= parser.parse_args()

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

				return args




def 							__SCREAMING_SNAKE_CASE       (		)					->						List[str]:
				transformers.utils.logging.set_verbosity_error()
				__lowercase								= parse_args()

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

				__lowercase								= start_measure()
				__lowercase								= model_cls.from_pretrained(args.model_name   ,							device_map='auto'   ,							**SCREAMING_SNAKE_CASE      )
				__lowercase								= end_measure(SCREAMING_SNAKE_CASE      )
				log_measures(SCREAMING_SNAKE_CASE   ,							'Model loading'      )

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

				__lowercase								= AutoTokenizer.from_pretrained(args.tokenizer_name      )

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

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

				__lowercase								= end_measure(SCREAMING_SNAKE_CASE      )
				log_measures(SCREAMING_SNAKE_CASE   ,							'Model generation'      )

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


if __name__ == "__main__":
							main()








from math import pi




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float      )					->						float:
				return degree / (180 / pi)


if __name__ == "__main__":
							from doctest import testmod

							testmod()





