"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""rembert"""



      def __init__(		self       ,     lowercase=25_03_00       ,     lowercase=11_52       ,     lowercase=32       ,     lowercase=18       ,     lowercase=2_56       ,     lowercase=16_64       ,     lowercase=46_08       ,     lowercase="gelu"       ,     lowercase=0.0       ,     lowercase=0.0       ,     lowercase=0.1       ,     lowercase=5_12       ,     lowercase=2       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=True       ,     lowercase=0       ,     lowercase=3_12       ,     lowercase=3_13       ,     **lowercase       ,     )      ->     Optional[int]:





        '''simple docstring'''





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

        a__:	str     				=      vocab_size
        a__:	List[str]     				=      input_embedding_size
        a__:	Optional[int]     				=      output_embedding_size
        a__:	List[str]     				=      max_position_embeddings
        a__:	int     				=      hidden_size
        a__:	Union[str, Any]     				=      num_hidden_layers
        a__:	List[str]     				=      num_attention_heads
        a__:	Optional[int]     				=      intermediate_size
        a__:	Optional[Any]     				=      hidden_act
        a__:	List[str]     				=      hidden_dropout_prob
        a__:	int     				=      attention_probs_dropout_prob
        a__:	Dict     				=      classifier_dropout_prob
        a__:	List[str]     				=      initializer_range
        a__:	int     				=      type_vocab_size
        a__:	Tuple     				=      layer_norm_eps
        a__:	Any     				=      use_cache
        a__:	List[str]     				=      False







class   __snake_case			(	__lowerCAmelCase							):



      @property
      def     lowerCamelCase_  (		self)      ->     Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        if self.task == "multiple-choice":
          a__:	Dict     				=      {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          a__:	Any     				=      {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ])



      @property
      def     lowerCamelCase_  (		self)      ->     float:





        '''simple docstring'''





        return 1e-4






"""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


snake_case__		:  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'''},
}

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

      lowerCAmelCase       :						Dict							= parser.parse_args()

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

      return args


def   _snake_case    (    ):
      transformers.utils.logging.set_verbosity_error()
      lowerCAmelCase       :						Union[str, Any]							= parse_args()

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

      lowerCAmelCase       :						str							= start_measure()
      lowerCAmelCase       :						List[str]							= model_cls.from_pretrained(args.model_name			, device_map='''auto'''			, **_snake_case      )
      lowerCAmelCase       :						Tuple							= end_measure(_snake_case      )
      log_measures(_snake_case			, '''Model loading'''      )

      lowerCAmelCase       :						str							= compute_module_sizes(_snake_case      )
      lowerCAmelCase       :						Tuple							= {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]
      lowerCAmelCase       :						int							= '''\n'''.join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]      )
      print(f'''\nTheoretical use:\n{message}'''      )

      lowerCAmelCase       :						int							= AutoTokenizer.from_pretrained(args.tokenizer_name      )

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

            lowerCAmelCase       :						Tuple							= tokenizer.decode(_snake_case			, skip_special_tokens=_snake_case      )
            texts_outs.append(_snake_case      )
            generation_times.append(_snake_case      )
            gen_tokens.append(_snake_case      )
            print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''      )

      lowerCAmelCase       :						List[Any]							= end_measure(_snake_case      )
      log_measures(_snake_case			, '''Model generation'''      )

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


if __name__ == "__main__":
 main()


