'''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    :			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'''},
}

UpperCamelCase    :			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 SCREAMING_SNAKE_CASE__   (							)					->							Dict:







     """simple docstring"""
     a :				Optional[int]				=      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'   )

     a :				List[str]				=      parser.parse_args()

     # Sanitize args
     if args.model_name in DEFAULT_MODELS:
          a :				int				=      DEFAULT_MODELS[args.model_name]
          a :				Tuple				=      defaults['model']
          if args.tokenizer_name is None:
               a :				List[Any]				=      defaults.get('tokenizer'							,     args.model_name   )
          if args.is_causal is None:
               a :				Optional[int]				=      defaults['is_causal']
          if args.model_revision is None:
               a :				Optional[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:
          a :				Optional[Any]				=      args.model_name
     if args.model_revision is None:
          a :				List[Any]				=      'main'

     return args






def SCREAMING_SNAKE_CASE__   (							)					->							List[Any]:







     """simple docstring"""
     transformers.utils.logging.set_verbosity_error()
     a :				Tuple				=      parse_args()

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

     a :				str				=      start_measure()
     a :				Optional[Any]				=      model_cls.from_pretrained(args.model_name							,     device_map='auto'							,     **UpperCAmelCase__   )
     a :				Any				=      end_measure(UpperCAmelCase__   )
     log_measures(UpperCAmelCase__							,     'Model loading'   )

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

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

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

          a :				str				=      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"""   )

     a :				List[str]				=      end_measure(UpperCAmelCase__   )
     log_measures(UpperCAmelCase__							,     'Model generation'   )

     a :				int				=      [gen / tok for gen, tok in zip(UpperCAmelCase__							,     UpperCAmelCase__   )]
     a :				Optional[int]				=      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"""   )
     a :				str				=      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




