code: 
  
  
  """simple docstring"""
  
  
  
  
  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 						__a      (  a,		a = 5_1_2					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				x.shape[1] if len(x.shape					) > 1 else 1
         _a          =				1 + np.arange(0,		feature_num * offset,		a,		dtype=np.intaa					)
         _a          =				x + feature_offset
         return x
  
  
  
  def 						__a      (  a,		a=True					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         requires_backends(a,		["cython"]					)
  
         if keep_features and "edge_attr" in item.keys():  # edge_attr
                _a          =				np.asarray(item["edge_attr"],		dtype=np.intaa					)
         else:
                _a          =				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
                _a          =				np.asarray(item["node_feat"],		dtype=np.intaa					)
         else:
                _a          =				np.ones((item["num_nodes"], 1),		dtype=np.intaa					)  # same embedding for all
  
         _a          =				np.asarray(item["edge_index"],		dtype=np.intaa					)
  
         _a          =				convert_to_single_emb(a					) + 1
         _a          =				item["num_nodes"]
  
         if len(edge_attr.shape					) == 1:
                _a          =				edge_attr[:, None]
         _a          =				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]],		dtype=np.intaa					)
         _a          =				convert_to_single_emb(a					) + 1
  
         # node adj matrix [num_nodes, num_nodes] bool
         _a          =				np.zeros([num_nodes, num_nodes],		dtype=a					)
         _a          =				True
  
         _a   , _a          =				algos_graphormer.floyd_warshall(a					)
         _a          =				np.amax(a					)
  
         _a          =				algos_graphormer.gen_edge_input(a,		a,		a					)
         _a          =				np.zeros([num_nodes + 1, num_nodes + 1],		dtype=np.single					)  # with graph token
  
         # combine
         _a          =				input_nodes + 1  # we shift all indices by one for padding
         _a          =				attn_bias
         _a          =				attn_edge_type
         _a          =				shortest_path_result.astype(np.intaa					) + 1  # we shift all indices by one for padding
         _a          =				np.sum(a,		axis=1					).reshape(-1					) + 1  # we shift all indices by one for padding
         _a          =				item["in_degree"]  # for undirected graph
         _a          =				input_edges + 1  # we shift all indices by one for padding
         if "labels" not in item:
                _a          =				item["y"]
  
         return item
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Tuple				,  UpperCamelCase__							:Dict=20				,  UpperCamelCase__							:Optional[int]=False       ):
            if not is_cython_available():
                   raise ImportError("Graphormer preprocessing needs Cython (pyximport)"       )
  
            _a          =				spatial_pos_max
            _a          =				on_the_fly_processing
  
  
  
  
     def __call__(		self							:List[Any]				,  UpperCamelCase__							:List[dict]       ):
            if self.on_the_fly_processing:
                   _a          =				[preprocess_item(UpperCamelCase__       ) for i in features]
  
            if not isinstance(features[0]				,  UpperCamelCase__       ):
                   _a          =				[vars(UpperCamelCase__       ) for f in features]
            _a          =				{}
  
            _a          =				max(len(i["input_nodes"]       ) for i in features       )
            _a          =				len(features[0]["input_nodes"][0]       )
            _a          =				len(features[0]["attn_edge_type"][0][0]       )
            _a          =				max(len(i["input_edges"][0][0]       ) for i in features       )
            _a          =				len(features[0]["input_edges"][0][0][0]       )
            _a          =				len(UpperCamelCase__       )
  
            _a          =				torch.zeros(UpperCamelCase__				,  max_node_num + 1				,  max_node_num + 1				,  dtype=torch.float       )
            _a          =				torch.zeros(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  dtype=torch.long       )
            _a          =				torch.zeros(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  dtype=torch.long       )
            _a          =				torch.zeros(UpperCamelCase__				,  UpperCamelCase__				,  dtype=torch.long       )
            _a          =				torch.zeros(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  dtype=torch.long       )
            _a          =				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"]:
                          _a          =				torch.tensor(f[k]       )
  
                   if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]       ) > 0:
                          _a          =				float("-inf"       )
  
                   _a          =				f["attn_bias"]
                   _a          =				f[
                       "attn_edge_type"
                   ]
                   _a          =				f["spatial_pos"]
                   _a          =				f["in_degree"]
                   _a          =				f["input_nodes"]
                   _a          =				f["input_edges"]
  
            _a          =				batch["in_degree"]
  
            _a          =				features[0]["labels"]
            if len(UpperCamelCase__       ) == 1:  # one task
                   if isinstance(sample[0]				,  UpperCamelCase__       ):  # regression
                          _a          =				torch.from_numpy(np.concatenate([i["labels"] for i in features]       )       )
                   else:  # binary classification
                          _a          =				torch.from_numpy(np.concatenate([i["labels"] for i in features]       )       )
            else:  # multi task classification, left to float to keep the NaNs
                   _a          =				torch.from_numpy(np.stack([i["labels"] for i in features]				,  axis=0       )       )
  
            return batch
  
style_context: 
  
  
  """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
  
  
  __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 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."					)
         parser.add_argument("model_name",		type=a,		default=a,		help="The name of the model to try."					)
         parser.add_argument(
             "--tokenizer_name",		type=a,		default=a,		help="The name of the tokenizer (if different from the model."					)
         parser.add_argument("--is_causal",		type=a,		default=a,		help="Whether or not the model is causal."					)
         parser.add_argument(
             "--model_revision",		type=a,		default=a,		help="The revision to use for the model checkpoint."					)
         parser.add_argument("--torch_dtype",		type=a,		default=a,		help="The dtype for the model."					)
         parser.add_argument("--disk_offload",		action="store_true"					)
  
         _a          =				parser.parse_args()
  
         # Sanitize args
         if args.model_name in DEFAULT_MODELS:
                _a          =				DEFAULT_MODELS[args.model_name]
                _a          =				defaults["model"]
                if args.tokenizer_name is None:
                       _a          =				defaults.get("tokenizer",		args.model_name					)
                if args.is_causal is None:
                       _a          =				defaults["is_causal"]
                if args.model_revision is None:
                       _a          =				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          =				args.model_name
         if args.model_revision is None:
                _a          =				"main"
  
         return args
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         transformers.utils.logging.set_verbosity_error()
         _a          =				parse_args()
  
         if args.torch_dtype is None:
                _a          =				AutoConfig.from_pretrained(args.model_name					)
                _a          =				getattr(a,		"torch_dtype",		torch.floataa					)
         else:
                _a          =				getattr(a,		args.torch_dtype					)
         _a          =				AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
         _a          =				{
             "torch_dtype": torch_dtype,
             "revision": args.model_revision,
         }
         if args.disk_offload:
                _a          =				"tmp_offload"
                _a          =				True
  
         _a          =				start_measure()
         _a          =				model_cls.from_pretrained(args.model_name,		device_map="auto",		**a					)
         _a          =				end_measure(a					)
         log_measures(a,		"Model loading"					)
  
         _a          =				compute_module_sizes(a					)
         _a          =				{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          =				"\n".join([F'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]					)
         print(F'\nTheoretical use:\n{message}'					)
  
         _a          =				AutoTokenizer.from_pretrained(args.tokenizer_name					)
  
         _a          =				start_measure()
         _a          =				[]
         _a          =				[]
         _a          =				[]
         for prompt in PROMPTS:
                _a          =				tokenizer(a,		return_tensors="pt"					).to(0					)
                _a          =				inputs["input_ids"][0].tolist()
                _a          =				time.time()
                _a          =				model.generate(inputs["input_ids"]					)
                _a          =				time.time()
                _a          =				outputs[0].tolist()
                _a          =				len(a					) if outputs[: len(a					)] != tokens else len(a					) - len(a					)
                _a          =				after_generate - before_generate
  
                _a          =				tokenizer.decode(a,		skip_special_tokens=a					)
                texts_outs.append(a					)
                generation_times.append(a					)
                gen_tokens.append(a					)
                print(F'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'					)
  
         _a          =				end_measure(a					)
         log_measures(a,		"Model generation"					)
  
         _a          =				[gen / tok for gen, tok in zip(a,		a					)]
         _a          =				sum(a					) / len(a					)
         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          =				sum(generation_times_per_token[1:]					) / (len(a					) - 1)
         print(F'Average time of generation per token (excluding the first): {avg_gen:.2f}s'					)
  
  
  if __name__ == "__main__":
     main()
  