

import torch

from transformers import AutoTokenizer

class 			lowerCamelCase  (					lowerCAmelCase__      ):


  '''simple docstring'''







  def __init__(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						AutoTokenizer.from_pretrained(UpperCAmelCase_  )



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        if isinstance(UpperCAmelCase_       ,					UpperCAmelCase_  ) and all(isinstance(_x       ,					UpperCAmelCase_  ) for _x in x  ):
              UpperCAmelCase__ :       Union[str, Any]						=						None
              for l in x:
                    UpperCAmelCase__ :       Any						=						self.tokenizer(
                        UpperCAmelCase_       ,					padding="""max_length"""       ,					max_length=384       ,					truncation=UpperCAmelCase_       ,					return_tensors="""pt"""       ,					)
                    UpperCAmelCase__ :       List[Any]						=						torch.tensor([len(UpperCAmelCase_  )]  )
                    if d is not None:
                          for k in d.keys():
                                UpperCAmelCase__ :       Dict						=						torch.cat((d[k], t[k])       ,					0  )
                    else:
                          UpperCAmelCase__ :       Optional[int]						=						t

              UpperCAmelCase__ :       str						=						torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""  )  )
              UpperCAmelCase__ :       str						=						torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""  )  )

        elif isinstance(UpperCAmelCase_       ,					UpperCAmelCase_  ) and all(isinstance(_x       ,					UpperCAmelCase_  ) for _x in x  ):
              UpperCAmelCase__ :       int						=						self.tokenizer(
                  UpperCAmelCase_       ,					padding="""max_length"""       ,					max_length=384       ,					truncation=UpperCAmelCase_       ,					return_tensors="""pt"""       ,					)

        else:
              raise Exception(
                  """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
                  """ strings` for supports are supported."""  )

        return d




  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=0.70  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						[]
        for idx in range(len(W_query["""input_ids"""]  )  ):
              UpperCAmelCase__ :       Union[str, Any]						=						range(p_start.shape[1]  )

              UpperCAmelCase__ :       str						=						[]
              for start_id in start_indexes:
                    for end_id in end_indexes:
                          if start_id < end_id:
                                output.append(
                                    (
                                        start_id,
                                        end_id,
                                        p_start[idx][start_id].item(),
                                        p_end[idx][end_id].item(),
                                    )  )

              output.sort(key=lambda _lowerCamelCase  : (tup[2] * tup[3])       ,					reverse=UpperCAmelCase_  )
              UpperCAmelCase__ :       Optional[Any]						=						[]
              for k in range(len(UpperCAmelCase_  )  ):
                    if output[k][2] * output[k][3] >= thresh:
                          UpperCAmelCase__ :       int						=						output[k][0], output[k][1]
                          UpperCAmelCase__ :       List[Any]						=						self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]  )
                          temp.append((decoded, output[k][2] * output[k][3])  )

              final_outputs.append(UpperCAmelCase_  )

        return final_outputs




"""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


_A						=     {
    """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"""},
}

_A						=     [
    """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__	(	)  ->   Optional[Any]:
      UpperCAmelCase__ :       int						=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
      parser.add_argument("""model_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the model to try."""						)
      parser.add_argument(
          """--tokenizer_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the tokenizer (if different from the model."""						)
      parser.add_argument("""--is_causal""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""Whether or not the model is causal."""						)
      parser.add_argument(
          """--model_revision""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The revision to use for the model checkpoint."""						)
      parser.add_argument("""--torch_dtype""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The dtype for the model."""						)
      parser.add_argument("""--disk_offload""" ,  action="""store_true"""						)

      UpperCAmelCase__ :       Union[str, Any]						=						parser.parse_args()

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

      return args




def 				a__	(	)  ->   List[Any]:
      transformers.utils.logging.set_verbosity_error()
      UpperCAmelCase__ :       Optional[int]						=						parse_args()

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

      UpperCAmelCase__ :       Any						=						start_measure()
      UpperCAmelCase__ :       Optional[int]						=						model_cls.from_pretrained(args.model_name ,  device_map="""auto""" ,  **lowerCAmelCase						)
      UpperCAmelCase__ :       Any						=						end_measure(lowerCAmelCase						)
      log_measures(lowerCAmelCase ,  """Model loading"""						)

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

      UpperCAmelCase__ :       Tuple						=						AutoTokenizer.from_pretrained(args.tokenizer_name						)

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

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

      UpperCAmelCase__ :       int						=						end_measure(lowerCAmelCase						)
      log_measures(lowerCAmelCase ,  """Model generation"""						)

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


if __name__ == "__main__":
    main()

