


'''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_		:       str							       =		{
    '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_		:       Dict							       =		[
    '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       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Dict							=				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"""					)

				_SCREAMING_SNAKE_CASE			:	int							=				parser.parse_args()

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

				return args







def       snake_case_       (  ):







				"""simple docstring"""





				transformers.utils.logging.set_verbosity_error()
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				parse_args()

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

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

				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				compute_module_sizes(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[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]
				_SCREAMING_SNAKE_CASE			:	Tuple							=				"""\n""".join([f"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]					)
				print(f"""\nTheoretical use:\n{message}"""					)

				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				AutoTokenizer.from_pretrained(args.tokenizer_name					)

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

								_SCREAMING_SNAKE_CASE			:	Dict							=				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"""					)

				_SCREAMING_SNAKE_CASE			:	str							=				end_measure(SCREAMING_SNAKE_CASE__					)
				log_measures(SCREAMING_SNAKE_CASE__							,				"""Model generation"""					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				[gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)]
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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"""					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				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()








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       List[Any]							       =		{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






							def __init__(   self   , __snake_case = 1   , __snake_case = 512 * 9   , __snake_case = 512 * 3   , __snake_case = 512   , __snake_case = 512   , __snake_case = 512   , __snake_case = 128   , __snake_case = 5   , __snake_case = 1024   , __snake_case = "multi_hop"   , __snake_case = 512   , __snake_case = False   , __snake_case = 12   , __snake_case = 768   , __snake_case = 768   , __snake_case = 32   , __snake_case = 0.1   , __snake_case = 0.1   , __snake_case = 0.0   , __snake_case = False   , __snake_case = False   , __snake_case = False   , __snake_case = "gelu"   , __snake_case = None   , __snake_case = False   , __snake_case = 0   , __snake_case = False   , __snake_case = 0.0   , __snake_case = 8   , __snake_case = None   , __snake_case = None   , __snake_case = True   , __snake_case = True   , __snake_case=0   , __snake_case=1   , __snake_case=2   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_classes
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_atoms
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_in_degree
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				num_out_degree
											_SCREAMING_SNAKE_CASE			:	Any							=				num_edges
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_spatial
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_edge_dis
											_SCREAMING_SNAKE_CASE			:	int							=				edge_type
											_SCREAMING_SNAKE_CASE			:	Tuple							=				multi_hop_max_dist
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				spatial_pos_max
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_nodes
											_SCREAMING_SNAKE_CASE			:	str							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	int							=				ffn_embedding_dim
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dropout
											_SCREAMING_SNAKE_CASE			:	List[str]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	int							=				layerdrop
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				encoder_normalize_before
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				pre_layernorm
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				apply_graphormer_init
											_SCREAMING_SNAKE_CASE			:	List[str]							=				activation_fn
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				embed_scale
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				freeze_embeddings
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_trans_layers_to_freeze
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				share_input_output_embed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				traceable
											_SCREAMING_SNAKE_CASE			:	List[str]							=				q_noise
											_SCREAMING_SNAKE_CASE			:	str							=				qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE			:	Any							=				kdim
											_SCREAMING_SNAKE_CASE			:	int							=				vdim
											_SCREAMING_SNAKE_CASE			:	Any							=				self_attention
											_SCREAMING_SNAKE_CASE			:	Tuple							=				bias

											super().__init__(
											    pad_token_id=__snake_case   , bos_token_id=__snake_case   , eos_token_id=__snake_case   , **__snake_case   , )




