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


_lowerCamelCase    :      Optional[Any]	 =						{
    '''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'''},
}

_lowerCamelCase    :      Any	 =						[
    '''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							():
					SCREAMING_SNAKE_CASE				=				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"     )

					SCREAMING_SNAKE_CASE				=				parser.parse_args()

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

					return args





def 		__lowerCamelCase							():
					transformers.utils.logging.set_verbosity_error()
					SCREAMING_SNAKE_CASE				=				parse_args()

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

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

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

					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained(args.tokenizer_name     )

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

										SCREAMING_SNAKE_CASE				=				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"     )

					SCREAMING_SNAKE_CASE				=				end_measure(UpperCAmelCase__     )
					log_measures(UpperCAmelCase__					,       "Model generation"     )

					SCREAMING_SNAKE_CASE				=				[gen / tok for gen, tok in zip(UpperCAmelCase__					,       UpperCAmelCase__     )]
					SCREAMING_SNAKE_CASE				=				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"     )
					SCREAMING_SNAKE_CASE				=				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()





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
							import pyximport

							pyximport.install(setup_args={'''include_dirs''': np.get_include()})
							from . import algos_graphormer  # noqa E402





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_attr"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((len(item["edge_index"][0]     ), 1)					,       dtype=np.intaa     )  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										SCREAMING_SNAKE_CASE				=				np.asarray(item["node_feat"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((item["num_nodes"], 1)					,       dtype=np.intaa     )  # same embedding for all

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

					# combine
					SCREAMING_SNAKE_CASE				=				input_nodes + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				attn_bias
					SCREAMING_SNAKE_CASE				=				attn_edge_type
					SCREAMING_SNAKE_CASE				=				shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				np.sum(UpperCAmelCase__					,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				item["in_degree"]  # for undirected graph
					SCREAMING_SNAKE_CASE				=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										SCREAMING_SNAKE_CASE				=				item["y"]

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(
												    _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)

												for ix, f in enumerate(_UpperCamelCase					):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																						SCREAMING_SNAKE_CASE				=				torch.tensor(f[k]					)

																	if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
																						SCREAMING_SNAKE_CASE				=				float("-inf"					)

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
												else:  # multi task classification, left to float to keep the NaNs
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




